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Singlan N, Abou Choucha F, Pasquier C. A new Similarity Based Adapted Louvain Algorithm (SIMBA) for active module identification in p-value attributed biological networks. Sci Rep 2025; 15:11360. [PMID: 40175439 PMCID: PMC11965526 DOI: 10.1038/s41598-025-95749-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2025] [Accepted: 03/24/2025] [Indexed: 04/04/2025] Open
Abstract
Real-world networks, such as biological networks, often exhibit complex structures and have attributes associated with nodes, which leads to significant challenges for analysis and modeling. Community detection algorithms can help identify groups of nodes of particular importance. However, traditional methods focus primarily on topological information, overlooking the importance of attribute-based similarities. This limitation hinders their ability to identify functionally coherent subnetworks. To address this, we propose a new scoring method for graph partitioning on the basis of a novel similarity function between node attributes. We then adapt the Louvain algorithm to optimize this scoring function, enabling the identification of communities that are both densely connected and functionally coherent. Extensive experiments on diverse biological networks, including artificial and real-world datasets, demonstrate the superiority of our approach over state-of-the-art methods. By leveraging both topological and attribute-based information, our approach provides a powerful tool for uncovering biologically meaningful modules and gaining deeper insights into complex biological processes.
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Affiliation(s)
- Nina Singlan
- Université Côte d'Azur, CNRS, i3S, 06560, Valbonne, France.
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2
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Blumenthal DB, Lucchetta M, Kleist L, Fekete SP, List M, Schaefer MH. Emergence of power law distributions in protein-protein interaction networks through study bias. eLife 2024; 13:e99951. [PMID: 39660719 PMCID: PMC11718653 DOI: 10.7554/elife.99951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 12/10/2024] [Indexed: 12/12/2024] Open
Abstract
Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study biases affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations, and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.
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Affiliation(s)
- David B Blumenthal
- Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany
| | - Marta Lucchetta
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCSMilanItaly
| | - Linda Kleist
- Department of Computer Science, TU BraunschweigBraunschweigGermany
| | - Sándor P Fekete
- Department of Computer Science, TU BraunschweigBraunschweigGermany
- Braunschweig Integrated Centre of Systems Biology (BRICS)BraunschweigGermany
| | - Markus List
- Data Science in Systems Biology, TUM School of Life Sciences, Technical University of MunichFreisingGermany
- Munich Data Science Institute (MDSI), Technical University of MunichGarchingGermany
| | - Martin H Schaefer
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCSMilanItaly
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3
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Picard M, Scott-Boyer MP, Bodein A, Leclercq M, Prunier J, Périn O, Droit A. Target repositioning using multi-layer networks and machine learning: The case of prostate cancer. Comput Struct Biotechnol J 2024; 24:464-475. [PMID: 38983753 PMCID: PMC11231507 DOI: 10.1016/j.csbj.2024.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 07/11/2024] Open
Abstract
The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.
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Affiliation(s)
- Milan Picard
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Julien Prunier
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Transformation and Innovation Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
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4
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Yang L, Chen R, Goodison S, Sun Y. A comprehensive benchmark study of methods for identifying significantly perturbed subnetworks in cancer. Brief Bioinform 2024; 26:bbae692. [PMID: 39737568 DOI: 10.1093/bib/bbae692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 12/02/2024] [Accepted: 12/16/2024] [Indexed: 01/01/2025] Open
Abstract
Network-based methods utilize protein-protein interaction information to identify significantly perturbed subnetworks in cancer and to propose key molecular pathways. Numerous methods have been developed, but to date, a rigorous benchmark analysis to compare the performance of existing approaches is lacking. In this paper, we proposed a novel benchmarking framework using synthetic data and conducted a comprehensive analysis to investigate the ability of existing methods to detect target genes and subnetworks and to control false positives, and how they perform in the presence of topological biases at both gene and subnetwork levels. Our analysis revealed insights into algorithmic performance that were previously unattainable. Based on the results of the benchmark study, we presented a practical guide for users on how to select appropriate detection methods and protein-protein interaction networks for cancer pathway identification, and provided suggestions for future algorithm development.
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Affiliation(s)
- Le Yang
- Department of Microbiology and Immunology, University at Buffalo, The State University of New York, 955 Main Street, Buffalo, New York, NY 14203, United States
| | - Runpu Chen
- Department of Microbiology and Immunology, University at Buffalo, The State University of New York, 955 Main Street, Buffalo, New York, NY 14203, United States
| | - Steve Goodison
- Department of Quantitative Health Sciences, Mayo Clinic, 4500 San Pablo Rd S, Jacksonville, FL 32224, United States
| | - Yijun Sun
- Department of Microbiology and Immunology, University at Buffalo, The State University of New York, 955 Main Street, Buffalo, New York, NY 14203, United States
- Department of Computer Science and Engineering, University at Buffalo, The State University of New York, 12 Capen Hall, Buffalo, New York, NY 14260, United States
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5
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Hoffmann M, Poschenrieder J, Incudini M, Baier S, Fritz A, Maier A, Hartung M, Hoffmann C, Trummer N, Adamowicz K, Picciani M, Scheibling E, Harl M, Lesch I, Frey H, Kayser S, Wissenberg P, Schwartz L, Hafner L, Acharya A, Hackl L, Grabert G, Lee SG, Cho G, Cloward M, Jankowski J, Lee H, Tsoy O, Wenke N, Pedersen A, Bønnelykke K, Mandarino A, Melograna F, Schulz L, Climente-González H, Wilhelm M, Iapichino L, Wienbrandt L, Ellinghaus D, Van Steen K, Grossi M, Furth P, Hennighausen L, Di Pierro A, Baumbach J, Kacprowski T, List M, Blumenthal D. Network medicine-based epistasis detection in complex diseases: ready for quantum computing. Nucleic Acids Res 2024; 52:10144-10160. [PMID: 39175109 PMCID: PMC11417373 DOI: 10.1093/nar/gkae697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 07/12/2024] [Accepted: 08/01/2024] [Indexed: 08/24/2024] Open
Abstract
Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs) (1-3). Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.
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Affiliation(s)
- Markus Hoffmann
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Advanced Study (Lichtenbergstrasse 2 a) Technical University of Munich, D-85748 Garching, Germany
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Julian M Poschenrieder
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Massimiliano Incudini
- Dipartimento di Informatica, Universit‘a di Verona, Strada le Grazie 15 - 34137 Verona, Italy
| | - Sylvie Baier
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Amelie Fritz
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs. Lyngby, Denmark
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Andreas Maier
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Michael Hartung
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Christian Hoffmann
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Nico Trummer
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Klaudia Adamowicz
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Evelyn Scheibling
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Maximilian V Harl
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
- Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland
| | - Ingmar Lesch
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Hunor Frey
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Simon Kayser
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Paul Wissenberg
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Leon Schwartz
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Leon Hafner
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Advanced Study (Lichtenbergstrasse 2 a) Technical University of Munich, D-85748 Garching, Germany
| | - Aakriti Acharya
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Germany
| | - Lena Hackl
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Gordon Grabert
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Germany
| | - Sung-Gwon Lee
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
- School of Biological Sciences and Technology, Chonnam National University, Gwangju, Korea
| | - Gyuhyeok Cho
- Department of Chemistry, Gwangju Institute of Science and Technology, Gwangju, Korea
| | | | - Jakub Jankowski
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Hye Kyung Lee
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Olga Tsoy
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Nina Wenke
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Anders Gorm Pedersen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs. Lyngby, Denmark
| | - Klaus Bønnelykke
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Antonio Mandarino
- International Centre for Theory of Quantum Technologies, University of Gdańsk, 80-309 Gdańsk, Poland
| | - Federico Melograna
- BIO3 - Systems Genetics; GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- BIO3 - Systems Medicine; Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Laura Schulz
- Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ), Garching b. München, Germany
| | | | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
- Munich Data Science Institute (MDSI), Technical University of Munich, Garching, Germany
| | - Luigi Iapichino
- Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ), Garching b. München, Germany
| | - Lars Wienbrandt
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - David Ellinghaus
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - Kristel Van Steen
- BIO3 - Systems Genetics; GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- BIO3 - Systems Medicine; Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Michele Grossi
- European Organization for Nuclear Research (CERN), Geneva1211, Switzerland
| | - Priscilla A Furth
- Departments of Oncology & Medicine, Georgetown University, Washington, DC, USA
| | - Lothar Hennighausen
- Institute for Advanced Study (Lichtenbergstrasse 2 a) Technical University of Munich, D-85748 Garching, Germany
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Alessandra Di Pierro
- Dipartimento di Informatica, Universit‘a di Verona, Strada le Grazie 15 - 34137 Verona, Italy
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Germany
- Computational BioMedicine Lab, University of Southern Denmark, Denmark
| | - Tim Kacprowski
- Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
| | - Markus List
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
- Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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6
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Zitnik M, Li MM, Wells A, Glass K, Morselli Gysi D, Krishnan A, Murali TM, Radivojac P, Roy S, Baudot A, Bozdag S, Chen DZ, Cowen L, Devkota K, Gitter A, Gosline SJC, Gu P, Guzzi PH, Huang H, Jiang M, Kesimoglu ZN, Koyuturk M, Ma J, Pico AR, Pržulj N, Przytycka TM, Raphael BJ, Ritz A, Sharan R, Shen Y, Singh M, Slonim DK, Tong H, Yang XH, Yoon BJ, Yu H, Milenković T. Current and future directions in network biology. BIOINFORMATICS ADVANCES 2024; 4:vbae099. [PMID: 39143982 PMCID: PMC11321866 DOI: 10.1093/bioadv/vbae099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 05/31/2024] [Accepted: 07/08/2024] [Indexed: 08/16/2024]
Abstract
Summary Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation Not applicable.
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Affiliation(s)
- Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Michelle M Li
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Aydin Wells
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
- Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN 46556, United States
- Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Deisy Morselli Gysi
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Statistics, Federal University of Paraná, Curitiba, Paraná 81530-015, Brazil
- Department of Physics, Northeastern University, Boston, MA 02115, United States
| | - Arjun Krishnan
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, United States
| | - Sushmita Roy
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States
- Wisconsin Institute for Discovery, Madison, WI 53715, United States
| | - Anaïs Baudot
- Aix Marseille Université, INSERM, MMG, Marseille, France
| | - Serdar Bozdag
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
- Department of Mathematics, University of North Texas, Denton, TX 76203, United States
| | - Danny Z Chen
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Lenore Cowen
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Kapil Devkota
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States
- Morgridge Institute for Research, Madison, WI 53715, United States
| | - Sara J C Gosline
- Biological Sciences Division, Pacific Northwest National Laboratory, Seattle, WA 98109, United States
| | - Pengfei Gu
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Pietro H Guzzi
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, 88100, Italy
| | - Heng Huang
- Department of Computer Science, University of Maryland College Park, College Park, MD 20742, United States
| | - Meng Jiang
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Ziynet Nesibe Kesimoglu
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20814, United States
| | - Mehmet Koyuturk
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, United States
| | - Nataša Pržulj
- Department of Computer Science, University College London, London, WC1E 6BT, England
- ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, 08010, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
| | - Teresa M Przytycka
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20814, United States
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08544, United States
| | - Anna Ritz
- Department of Biology, Reed College, Portland, OR 97202, United States
| | - Roded Sharan
- School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, NJ 08544, United States
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, United States
| | - Donna K Slonim
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Hanghang Tong
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Xinan Holly Yang
- Department of Pediatrics, University of Chicago, Chicago, IL 60637, United States
| | - Byung-Jun Yoon
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, United States
| | - Haiyuan Yu
- Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, United States
| | - Tijana Milenković
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
- Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN 46556, United States
- Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
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7
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Saarinen H, Goldsmith M, Wang RS, Loscalzo J, Maniscalco S. Disease gene prioritization with quantum walks. Bioinformatics 2024; 40:btae513. [PMID: 39171848 PMCID: PMC11361815 DOI: 10.1093/bioinformatics/btae513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 06/23/2024] [Accepted: 08/16/2024] [Indexed: 08/23/2024] Open
Abstract
MOTIVATION Disease gene prioritization methods assign scores to genes or proteins according to their likely relevance for a given disease based on a provided set of seed genes. This scoring can be used to find new biologically relevant genes or proteins for many diseases. Although methods based on classical random walks have proven to yield competitive results, quantum walk methods have not been explored to this end. RESULTS We propose a new algorithm for disease gene prioritization based on continuous-time quantum walks using the adjacency matrix of a protein-protein interaction (PPI) network. We demonstrate the success of our proposed quantum walk method by comparing it to several well-known gene prioritization methods on three disease sets, across seven different PPI networks. In order to compare these methods, we use cross-validation and examine the mean reciprocal ranks of recall and average precision values. We further validate our method by performing an enrichment analysis of the predicted genes for coronary artery disease. AVAILABILITY AND IMPLEMENTATION The data and code for the methods can be accessed at https://github.com/markgolds/qdgp.
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Affiliation(s)
- Harto Saarinen
- Algorithmiq Ltd, FI-00160 Helsinki, Finland
- Department of Mathematics and Statistics, Complex Systems Research Group, University of Turku, FI-20014, Turku, Finland
| | - Mark Goldsmith
- Algorithmiq Ltd, FI-00160 Helsinki, Finland
- Department of Mathematics and Statistics, Complex Systems Research Group, University of Turku, FI-20014, Turku, Finland
| | - Rui-Sheng Wang
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, United States
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, United States
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8
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Ceylan B, Düz E, Çakir T. Personalized Protein-Protein Interaction Networks Towards Unraveling the Molecular Mechanisms of Alzheimer's Disease. Mol Neurobiol 2024; 61:2120-2135. [PMID: 37855983 DOI: 10.1007/s12035-023-03690-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/02/2023] [Indexed: 10/20/2023]
Abstract
Alzheimer's disease (AD) is a highly heterogenous neurodegenerative disease, and several omic-based datasets were generated in the last decade from the patients with the disease. However, the vast majority of studies evaluate these datasets in bulk by considering all the patients as a single group, which obscures the molecular differences resulting from the heterogeneous nature of the disease. In this study, we adopted a personalized approach and analyzed the transcriptome data from 403 patients individually by mapping the data on a human protein-protein interaction network. Patient-specific subnetworks were discovered and analyzed in terms of the genes in the subnetworks, enriched functional terms, and known AD genes. We identified several affected pathways that could not be captured by the bulk comparison. We also showed that our personalized findings point to patterns of alterations consistent with the recently suggested AD subtypes.
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Affiliation(s)
- Betül Ceylan
- Department of Bioengineering, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey
| | - Elif Düz
- Department of Bioengineering, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey
| | - Tunahan Çakir
- Department of Bioengineering, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey.
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9
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Hoffmann M, Poschenrieder JM, Incudini M, Baier S, Fitz A, Maier A, Hartung M, Hoffmann C, Trummer N, Adamowicz K, Picciani M, Scheibling E, Harl MV, Lesch I, Frey H, Kayser S, Wissenberg P, Schwartz L, Hafner L, Acharya A, Hackl L, Grabert G, Lee SG, Cho G, Cloward M, Jankowski J, Lee HK, Tsoy O, Wenke N, Pedersen AG, Bønnelykke K, Mandarino A, Melograna F, Schulz L, Climente-González H, Wilhelm M, Iapichino L, Wienbrandt L, Ellinghaus D, Van Steen K, Grossi M, Furth PA, Hennighausen L, Di Pierro A, Baumbach J, Kacprowski T, List M, Blumenthal DB. Network medicine-based epistasis detection in complex diseases: ready for quantum computing. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.07.23298205. [PMID: 38076997 PMCID: PMC10705612 DOI: 10.1101/2023.11.07.23298205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs)1-3. Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL is the first application that demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.
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Affiliation(s)
- Markus Hoffmann
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
- Institute for Advanced Study (Lichtenbergstrasse 2 a, D-85748 Garching, Germany), Technical University of Munich, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Julian M. Poschenrieder
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Massimiliano Incudini
- Dipartimento di Informatica, Universit’a di Verona, Strada le Grazie 15 - 34137, Verona, Italy
| | - Sylvie Baier
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Amelie Fitz
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs. Lyngby, Denmark
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Andreas Maier
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Michael Hartung
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Christian Hoffmann
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Nico Trummer
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Klaudia Adamowicz
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Evelyn Scheibling
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Maximilian V. Harl
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Ingmar Lesch
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Hunor Frey
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Simon Kayser
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Paul Wissenberg
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Leon Schwartz
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Leon Hafner
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
- Institute for Advanced Study (Lichtenbergstrasse 2 a, D-85748 Garching, Germany), Technical University of Munich, Germany
| | - Aakriti Acharya
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Braunschweig, Germany
| | - Lena Hackl
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Gordon Grabert
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Braunschweig, Germany
| | - Sung-Gwon Lee
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
- School of Biological Sciences and Technology, Chonnam National University, Gwangju, Korea
| | - Gyuhyeok Cho
- Department of Chemistry, Gwangju Institute of Science and Technology, Gwangju, Korea
| | - Matthew Cloward
- Department of Biology, Brigham Young University, Provo, UT, USA
| | - Jakub Jankowski
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Hye Kyung Lee
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Olga Tsoy
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Nina Wenke
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Anders Gorm Pedersen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs. Lyngby, Denmark
| | - Klaus Bønnelykke
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Antonio Mandarino
- International Centre for Theory of Quantum Technologies, University of Gdańsk, 80-309 Gdańsk, Poland
| | - Federico Melograna
- BIO3 - Systems Genetics; GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- BIO3 - Systems Medicine; Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Laura Schulz
- Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ), Garching b. München, Germany
| | | | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Luigi Iapichino
- Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ), Garching b. München, Germany
| | - Lars Wienbrandt
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - David Ellinghaus
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - Kristel Van Steen
- BIO3 - Systems Genetics; GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- BIO3 - Systems Medicine; Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Michele Grossi
- European Organization for Nuclear Research (CERN), Geneva 1211, Switzerland
| | - Priscilla A. Furth
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
- Departments of Oncology & Medicine, Georgetown University, Washington, DC, USA
| | - Lothar Hennighausen
- Institute for Advanced Study (Lichtenbergstrasse 2 a, D-85748 Garching, Germany), Technical University of Munich, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Alessandra Di Pierro
- Dipartimento di Informatica, Universit’a di Verona, Strada le Grazie 15 - 34137, Verona, Italy
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Germany
- Computational BioMedicine Lab, University of Southern Denmark, Denmark
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - David B. Blumenthal
- Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
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10
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Pasquier C, Guerlais V, Pallez D, Rapetti-Mauss R, Soriani O. A network embedding approach to identify active modules in biological interaction networks. Life Sci Alliance 2023; 6:e202201550. [PMID: 37339804 PMCID: PMC10282331 DOI: 10.26508/lsa.202201550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/06/2023] [Accepted: 06/06/2023] [Indexed: 06/22/2023] Open
Abstract
The identification of condition-specific gene sets from transcriptomic experiments is important to reveal regulatory and signaling mechanisms associated with a given cellular response. Statistical methods of differential expression analysis, designed to assess individual gene variations, have trouble highlighting modules of small varying genes whose interaction is essential to characterize phenotypic changes. To identify these highly informative gene modules, several methods have been proposed in recent years, but they have many limitations that make them of little use to biologists. Here, we propose an efficient method for identifying these active modules that operates on a data embedding combining gene expressions and interaction data. Applications carried out on real datasets show that our method can identify new groups of genes of high interest corresponding to functions not revealed by traditional approaches. Software is available at https://github.com/claudepasquier/amine.
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Affiliation(s)
- Claude Pasquier
- Laboratoire d'Informatique, Signaux et Systèmes de Sophia-Antipolis, I3S - UMR7271 - UNS CNRS, Les Algorithmes - bât. Euclide B, Sophia Antipolis, France
| | - Vincent Guerlais
- Laboratoire d'Informatique, Signaux et Systèmes de Sophia-Antipolis, I3S - UMR7271 - UNS CNRS, Les Algorithmes - bât. Euclide B, Sophia Antipolis, France
| | - Denis Pallez
- Laboratoire d'Informatique, Signaux et Systèmes de Sophia-Antipolis, I3S - UMR7271 - UNS CNRS, Les Algorithmes - bât. Euclide B, Sophia Antipolis, France
| | - Raphaël Rapetti-Mauss
- iBV - Institut de Biologie Valrose, Université Nice Sophia Antipolis, Faculté des Sciences, Parc Valrose, Nice cedex 2, France
| | - Olivier Soriani
- iBV - Institut de Biologie Valrose, Université Nice Sophia Antipolis, Faculté des Sciences, Parc Valrose, Nice cedex 2, France
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11
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Mastropietro A, De Carlo G, Anagnostopoulos A. XGDAG: explainable gene-disease associations via graph neural networks. Bioinformatics 2023; 39:btad482. [PMID: 37531293 PMCID: PMC10421968 DOI: 10.1093/bioinformatics/btad482] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/27/2023] [Accepted: 08/01/2023] [Indexed: 08/04/2023] Open
Abstract
MOTIVATION Disease gene prioritization consists in identifying genes that are likely to be involved in the mechanisms of a given disease, providing a ranking of such genes. Recently, the research community has used computational methods to uncover unknown gene-disease associations; these methods range from combinatorial to machine learning-based approaches. In particular, during the last years, approaches based on deep learning have provided superior results compared to more traditional ones. Yet, the problem with these is their inherent black-box structure, which prevents interpretability. RESULTS We propose a new methodology for disease gene discovery, which leverages graph-structured data using graph neural networks (GNNs) along with an explainability phase for determining the ranking of candidate genes and understanding the model's output. Our approach is based on a positive-unlabeled learning strategy, which outperforms existing gene discovery methods by exploiting GNNs in a non-black-box fashion. Our methodology is effective even in scenarios where a large number of associated genes need to be retrieved, in which gene prioritization methods often tend to lose their reliability. AVAILABILITY AND IMPLEMENTATION The source code of XGDAG is available on GitHub at: https://github.com/GiDeCarlo/XGDAG. The data underlying this article are available at: https://www.disgenet.org/, https://thebiogrid.org/, https://doi.org/10.1371/journal.pcbi.1004120.s003, and https://doi.org/10.1371/journal.pcbi.1004120.s004.
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Affiliation(s)
- Andrea Mastropietro
- Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome 00185, Italy
| | - Gianluca De Carlo
- Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome 00185, Italy
| | - Aris Anagnostopoulos
- Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome 00185, Italy
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12
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Sarkar S, Lucchetta M, Maier A, Abdrabbou MM, Baumbach J, List M, Schaefer MH, Blumenthal DB. Online bias-aware disease module mining with ROBUST-Web. Bioinformatics 2023; 39:btad345. [PMID: 37233198 PMCID: PMC10246579 DOI: 10.1093/bioinformatics/btad345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 04/24/2023] [Accepted: 05/25/2023] [Indexed: 05/27/2023] Open
Abstract
SUMMARY We present ROBUST-Web which implements our recently presented ROBUST disease module mining algorithm in a user-friendly web application. ROBUST-Web features seamless downstream disease module exploration via integrated gene set enrichment analysis, tissue expression annotation, and visualization of drug-protein and disease-gene links. Moreover, ROBUST-Web includes bias-aware edge costs for the underlying Steiner tree model as a new algorithmic feature, which allow to correct for study bias in protein-protein interaction networks and further improves the robustness of the computed modules. AVAILABILITY AND IMPLEMENTATION Web application: https://robust-web.net. Source code of web application and Python package with new bias-aware edge costs: https://github.com/bionetslab/robust-web, https://github.com/bionetslab/robust_bias_aware.
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Affiliation(s)
- Suryadipto Sarkar
- Biomedical Network Science Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen 91301, Germany
| | - Marta Lucchetta
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan 20139, Italy
| | - Andreas Maier
- Institute for Computational Systems Biology, University of Hamburg, Hamburg 22607, Germany
| | - Mohamed M Abdrabbou
- Biomedical Network Science Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen 91301, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg 22607, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising 85354, Germany
| | - Martin H Schaefer
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan 20139, Italy
| | - David B Blumenthal
- Biomedical Network Science Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen 91301, Germany
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13
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Pandey AK, Loscalzo J. Network medicine: an approach to complex kidney disease phenotypes. Nat Rev Nephrol 2023:10.1038/s41581-023-00705-0. [PMID: 37041415 DOI: 10.1038/s41581-023-00705-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 04/13/2023]
Abstract
Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.
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Affiliation(s)
- Arvind K Pandey
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
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14
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Sadegh S, Skelton J, Anastasi E, Maier A, Adamowicz K, Möller A, Kriege NM, Kronberg J, Haller T, Kacprowski T, Wipat A, Baumbach J, Blumenthal DB. Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond. Nat Commun 2023; 14:1662. [PMID: 36966134 PMCID: PMC10039912 DOI: 10.1038/s41467-023-37349-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 03/13/2023] [Indexed: 03/27/2023] Open
Abstract
A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.
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Affiliation(s)
- Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - James Skelton
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Elisa Anastasi
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Andreas Maier
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Klaudia Adamowicz
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Anna Möller
- Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Nils M Kriege
- Faculty of Computer Science, University of Vienna, Vienna, Austria
- Research Network Data Science, University of Vienna, Vienna, Austria
| | - Jaanika Kronberg
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Toomas Haller
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Anil Wipat
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
- Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - David B Blumenthal
- Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
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15
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Lio CT, Grabert G, Louadi Z, Fenn A, Baumbach J, Kacprowski T, List M, Tsoy O. Systematic analysis of alternative splicing in time course data using Spycone. Bioinformatics 2022; 39:6965022. [PMID: 36579860 PMCID: PMC9831059 DOI: 10.1093/bioinformatics/btac846] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 11/16/2022] [Accepted: 12/28/2022] [Indexed: 12/30/2022] Open
Abstract
MOTIVATION During disease progression or organism development, alternative splicing may lead to isoform switches that demonstrate similar temporal patterns and reflect the alternative splicing co-regulation of such genes. Tools for dynamic process analysis usually neglect alternative splicing. RESULTS Here, we propose Spycone, a splicing-aware framework for time course data analysis. Spycone exploits a novel IS detection algorithm and offers downstream analysis such as network and gene set enrichment. We demonstrate the performance of Spycone using simulated and real-world data of SARS-CoV-2 infection. AVAILABILITY AND IMPLEMENTATION The Spycone package is available as a PyPI package. The source code of Spycone is available under the GPLv3 license at https://github.com/yollct/spycone and the documentation at https://spycone.readthedocs.io/en/latest/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chit Tong Lio
- Institute for Computational Systems Biology, University of Hamburg, Notkestrasse 9, Hamburg 22607, Germany,Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising 85354, Germany
| | - Gordon Grabert
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig 38106, Germany,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig 38106, Germany
| | - Zakaria Louadi
- Institute for Computational Systems Biology, University of Hamburg, Notkestrasse 9, Hamburg 22607, Germany,Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising 85354, Germany
| | - Amit Fenn
- Institute for Computational Systems Biology, University of Hamburg, Notkestrasse 9, Hamburg 22607, Germany,Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising 85354, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Notkestrasse 9, Hamburg 22607, Germany,Institute of Mathematics and Computer Science, University of Southern Denmark, Odense 5000, Denmark
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig 38106, Germany,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig 38106, Germany
| | | | - Olga Tsoy
- To whom correspondence should be addressed.
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16
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Galindez G, Sadegh S, Baumbach J, Kacprowski T, List M. Network-based approaches for modeling disease regulation and progression. Comput Struct Biotechnol J 2022; 21:780-795. [PMID: 36698974 PMCID: PMC9841310 DOI: 10.1016/j.csbj.2022.12.022] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.
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Affiliation(s)
- Gihanna Galindez
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
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17
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Li Q, Newaz K, Milenković T. Towards future directions in data-integrative supervised prediction of human aging-related genes. BIOINFORMATICS ADVANCES 2022; 2:vbac081. [PMID: 36699345 PMCID: PMC9710570 DOI: 10.1093/bioadv/vbac081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 09/23/2022] [Accepted: 10/31/2022] [Indexed: 11/13/2022]
Abstract
Motivation Identification of human genes involved in the aging process is critical due to the incidence of many diseases with age. A state-of-the-art approach for this purpose infers a weighted dynamic aging-specific subnetwork by mapping gene expression (GE) levels at different ages onto the protein-protein interaction network (PPIN). Then, it analyzes this subnetwork in a supervised manner by training a predictive model to learn how network topologies of known aging- versus non-aging-related genes change across ages. Finally, it uses the trained model to predict novel aging-related gene candidates. However, the best current subnetwork resulting from this approach still yields suboptimal prediction accuracy. This could be because it was inferred using outdated GE and PPIN data. Here, we evaluate whether analyzing a weighted dynamic aging-specific subnetwork inferred from newer GE and PPIN data improves prediction accuracy upon analyzing the best current subnetwork inferred from outdated data. Results Unexpectedly, we find that not to be the case. To understand this, we perform aging-related pathway and Gene Ontology term enrichment analyses. We find that the suboptimal prediction accuracy, regardless of which GE or PPIN data is used, may be caused by the current knowledge about which genes are aging-related being incomplete, or by the current methods for inferring or analyzing an aging-specific subnetwork being unable to capture all of the aging-related knowledge. These findings can potentially guide future directions towards improving supervised prediction of aging-related genes via -omics data integration. Availability and implementation All data and code are available at zenodo, DOI: 10.5281/zenodo.6995045. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Qi Li
- Department of Computer Science and Engineering, Lucy Family Institute for Data & Society, and Eck Institute for Global Health (EIGH), University of Notre Dame, Notre Dame, IN 46556, USA
| | - Khalique Newaz
- Department of Computer Science and Engineering, Lucy Family Institute for Data & Society, and Eck Institute for Global Health (EIGH), University of Notre Dame, Notre Dame, IN 46556, USA,Center for Data and Computing in Natural Sciences (CDCS), Institute for Computational Systems Biology, Universität Hamburg, Hamburg 20146, Germany
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18
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Gentili M, Martini L, Sponziello M, Becchetti L. Biological Random Walks: multi-omics integration for disease gene prioritization. Bioinformatics 2022; 38:4145-4152. [PMID: 35792834 DOI: 10.1093/bioinformatics/btac446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 06/22/2022] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Over the past decade, network-based approaches have proven useful in identifying disease modules within the human interactome, often providing insights into key mechanisms and guiding the quest for therapeutic targets. This is all the more important, since experimental investigation of potential gene candidates is an expensive task, thus not always a feasible option. On the other hand, many sources of biological information exist beyond the interactome and an important research direction is the design of effective techniques for their integration. RESULTS In this work, we introduce the Biological Random Walks (BRW) approach for disease gene prioritization in the human interactome. The proposed framework leverages multiple biological sources within an integrated framework. We perform an extensive, comparative study of BRW's performance against well-established baselines. AVAILABILITY AND IMPLEMENTATION All codes are publicly available and can be downloaded at https://github.com/LeoM93/BiologicalRandomWalks. We used publicly available datasets, details on their retrieval and preprocessing are provided in the Supplementary Material. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Michele Gentili
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Leonardo Martini
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Marialuisa Sponziello
- Translational and Precision Medicine Department, Sapienza University of Rome, Rome, Italy
| | - Luca Becchetti
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
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19
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Adamowicz K, Maier A, Baumbach J, Blumenthal DB. Online in silico validation of disease and gene sets, clusterings or subnetworks with DIGEST. Brief Bioinform 2022; 23:6618231. [PMID: 35753693 DOI: 10.1093/bib/bbac247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 11/12/2022] Open
Abstract
As the development of new drugs reaches its physical and financial limits, drug repurposing has become more important than ever. For mechanistically grounded drug repurposing, it is crucial to uncover the disease mechanisms and to detect clusters of mechanistically related diseases. Various methods for computing candidate disease mechanisms and disease clusters exist. However, in the absence of ground truth, in silico validation is challenging. This constitutes a major hurdle toward the adoption of in silico prediction tools by experimentalists who are often hesitant to carry out wet-lab validations for predicted candidate mechanisms without clearly quantified initial plausibility. To address this problem, we present DIGEST (in silico validation of disease and gene sets, clusterings or subnetworks), a Python-based validation tool available as a web interface (https://digest-validation.net), as a stand-alone package or over a REST API. DIGEST greatly facilitates in silico validation of gene and disease sets, clusterings or subnetworks via fully automated pipelines comprising disease and gene ID mapping, enrichment analysis, comparisons of shared genes and variants and background distribution estimation. Moreover, functionality is provided to automatically update the external databases used by the pipelines. DIGEST hence allows the user to assess the statistical significance of candidate mechanisms with regard to functional and genetic coherence and enables the computation of empirical $P$-values with just a few mouse clicks.
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Affiliation(s)
- Klaudia Adamowicz
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Andreas Maier
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - David B Blumenthal
- Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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20
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Sonawane AR, Aikawa E, Aikawa M. Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases. Front Cardiovasc Med 2022; 9:873582. [PMID: 35665246 PMCID: PMC9160390 DOI: 10.3389/fcvm.2022.873582] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 04/19/2022] [Indexed: 01/18/2023] Open
Abstract
Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.
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Affiliation(s)
- Abhijeet Rajendra Sonawane
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Masanori Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
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21
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You Y, Lai X, Pan Y, Zheng H, Vera J, Liu S, Deng S, Zhang L. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022; 7:156. [PMID: 35538061 PMCID: PMC9090746 DOI: 10.1038/s41392-022-00994-0] [Citation(s) in RCA: 143] [Impact Index Per Article: 47.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
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Affiliation(s)
- Yujie You
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Room D513, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, China
| | - Huiru Zheng
- School of Computing, Ulster University, Belfast, BT15 1ED, UK
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Suran Liu
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Senyi Deng
- Institute of Thoracic Oncology, Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610065, China.
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
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22
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Xiang J, Meng X, Zhao Y, Wu FX, Li M. HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure. Brief Bioinform 2022; 23:6547263. [PMID: 35275996 DOI: 10.1093/bib/bbac072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 01/18/2022] [Accepted: 02/13/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction. RESULTS We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation. CONCLUSIONS The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.
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Affiliation(s)
- Ju Xiang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China; Department of Basic Medical Sciences & Academician Workstation, Changsha Medical University, Changsha, Hunan 410219, China
| | - Xiangmao Meng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Yichao Zhao
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada
| | - Min Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
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23
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Bernett J, Krupke D, Sadegh S, Baumbach J, Fekete SP, Kacprowski T, List M, Blumenthal DB. Robust disease module mining via enumeration of diverse prize-collecting Steiner trees. Bioinformatics 2022; 38:1600-1606. [PMID: 34984440 DOI: 10.1093/bioinformatics/btab876] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/29/2021] [Accepted: 12/31/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Disease module mining methods (DMMMs) extract subgraphs that constitute candidate disease mechanisms from molecular interaction networks such as protein-protein interaction (PPI) networks. Irrespective of the employed models, DMMMs typically include non-robust steps in their workflows, i.e. the computed subnetworks vary when running the DMMMs multiple times on equivalent input. This lack of robustness has a negative effect on the trustworthiness of the obtained subnetworks and is hence detrimental for the widespread adoption of DMMMs in the biomedical sciences. RESULTS To overcome this problem, we present a new DMMM called ROBUST (robust disease module mining via enumeration of diverse prize-collecting Steiner trees). In a large-scale empirical evaluation, we show that ROBUST outperforms competing methods in terms of robustness, scalability and, in most settings, functional relevance of the produced modules, measured via KEGG (Kyoto Encyclopedia of Genes and Genomes) gene set enrichment scores and overlap with DisGeNET disease genes. AVAILABILITY AND IMPLEMENTATION A Python 3 implementation and scripts to reproduce the results reported in this article are available on GitHub: https://github.com/bionetslab/robust, https://github.com/bionetslab/robust-eval. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Judith Bernett
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Dominik Krupke
- Department of Computer Science, TU Braunschweig, 38106 Braunschweig, Germany
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany.,Institute for Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
| | - Sándor P Fekete
- Department of Computer Science, TU Braunschweig, 38106 Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), 38106 Braunschweig, Germany
| | - Tim Kacprowski
- Braunschweig Integrated Centre of Systems Biology (BRICS), 38106 Braunschweig, Germany.,Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technical University of Braunschweig and Hannover Medical School, 38106 Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - David B Blumenthal
- Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
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24
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Levi H, Rahmanian N, Elkon R, Shamir R. The DOMINO web-server for active module identification analysis. Bioinformatics 2022; 38:2364-2366. [PMID: 35139202 PMCID: PMC9004647 DOI: 10.1093/bioinformatics/btac067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 01/06/2022] [Accepted: 02/01/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Active module identification (AMI) is an essential step in many omics analyses. Such algorithms receive a gene network and a gene activity profile as input and report subnetworks that show significant over-representation of accrued activity signal ('active modules'). Such modules can point out key molecular processes in the analyzed biological conditions. RESULTS We recently introduced a novel AMI algorithm called DOMINO and demonstrated that it detects active modules that capture biological signals with markedly improved rate of empirical validation. Here, we provide an online server that executes DOMINO, making it more accessible and user-friendly. To help the interpretation of solutions, the server provides GO enrichment analysis, module visualizations and accessible output formats for customized downstream analysis. It also enables running DOMINO with various gene identifiers of different organisms. AVAILABILITY AND IMPLEMENTATION The server is available at http://domino.cs.tau.ac.il. Its codebase is available at https://github.com/Shamir-Lab.
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Affiliation(s)
- Hagai Levi
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
| | | | - Ran Elkon
- To whom correspondence should be addressed. or
| | - Ron Shamir
- To whom correspondence should be addressed. or
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25
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Network pharmacology: curing causal mechanisms instead of treating symptoms. Trends Pharmacol Sci 2021; 43:136-150. [PMID: 34895945 DOI: 10.1016/j.tips.2021.11.004] [Citation(s) in RCA: 523] [Impact Index Per Article: 130.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 10/05/2021] [Accepted: 11/04/2021] [Indexed: 12/15/2022]
Abstract
For complex diseases, most drugs are highly ineffective, and the success rate of drug discovery is in constant decline. While low quality, reproducibility issues, and translational irrelevance of most basic and preclinical research have contributed to this, the current organ-centricity of medicine and the 'one disease-one target-one drug' dogma obstruct innovation in the most profound manner. Systems and network medicine and their therapeutic arm, network pharmacology, revolutionize how we define, diagnose, treat, and, ideally, cure diseases. Descriptive disease phenotypes are replaced by endotypes defined by causal, multitarget signaling modules that also explain respective comorbidities. Precise and effective therapeutic intervention is achieved by synergistic multicompound network pharmacology and drug repurposing, obviating the need for drug discovery and speeding up clinical translation.
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