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Park H, Miyano S. Computational Tactics for Precision Cancer Network Biology. Int J Mol Sci 2022; 23:ijms232214398. [PMID: 36430875 PMCID: PMC9695754 DOI: 10.3390/ijms232214398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/12/2022] [Accepted: 11/17/2022] [Indexed: 11/22/2022] Open
Abstract
Network biology has garnered tremendous attention in understanding complex systems of cancer, because the mechanisms underlying cancer involve the perturbations in the specific function of molecular networks, rather than a disorder of a single gene. In this article, we review the various computational tactics for gene regulatory network analysis, focused especially on personalized anti-cancer therapy. This paper covers three major topics: (1) cell line's (or patient's) cancer characteristics specific gene regulatory network estimation, which enables us to reveal molecular interplays under varying conditions of cancer characteristics of cell lines (or patient); (2) computational approaches to interpret the multitudinous and massive networks; (3) network-based application to uncover molecular mechanisms of cancer and related marker identification. We expect that this review will help readers understand personalized computational network biology that plays a significant role in precision cancer medicine.
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Affiliation(s)
- Heewon Park
- M&D Data Science Center, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
- Correspondence:
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
- Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, Tokyo 108-8639, Japan
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Sheh A, Artim SC, Burns MA, Molina-Mora JA, Lee MA, Dzink-Fox J, Muthupalani S, Fox JG. Alterations in common marmoset gut microbiome associated with duodenal strictures. Sci Rep 2022; 12:5277. [PMID: 35347206 PMCID: PMC8960757 DOI: 10.1038/s41598-022-09268-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 03/21/2022] [Indexed: 12/13/2022] Open
Abstract
Chronic gastrointestinal (GI) diseases are the most common diseases in captive common marmosets (Callithrix jacchus). Despite standardized housing, diet and husbandry, a recently described gastrointestinal syndrome characterized by duodenal ulcers and strictures was observed in a subset of marmosets sourced from the New England Primate Research Center. As changes in the gut microbiome have been associated with GI diseases, the gut microbiome of 52 healthy, non-stricture marmosets (153 samples) were compared to the gut microbiome of 21 captive marmosets diagnosed with a duodenal ulcer/stricture (57 samples). No significant changes were observed using alpha diversity metrics, and while the community structure was significantly different when comparing beta diversity between healthy and stricture cases, the results were inconclusive due to differences observed in the dispersion of both datasets. Differences in the abundance of individual taxa using ANCOM, as stricture-associated dysbiosis was characterized by Anaerobiospirillum loss and Clostridium perfringens increases. To identify microbial and serum biomarkers that could help classify stricture cases, we developed models using machine learning algorithms (random forest, classification and regression trees, support vector machines and k-nearest neighbors) to classify microbiome, serum chemistry or complete blood count (CBC) data. Random forest (RF) models were the most accurate models and correctly classified strictures using either 9 ASVs (amplicon sequence variants), 4 serum chemistry tests or 6 CBC tests. Based on the RF model and ANCOM results, C. perfringens was identified as a potential causative agent associated with the development of strictures. Clostridium perfringens was also isolated by microbiological culture in 4 of 9 duodenum samples from marmosets with histologically confirmed strictures. Due to the enrichment of C. perfringens in situ, we analyzed frozen duodenal tissues using both 16S microbiome profiling and RNAseq. Microbiome analysis of the duodenal tissues of 29 marmosets from the MIT colony confirmed an increased abundance of Clostridium in stricture cases. Comparison of the duodenal gene expression from stricture and non-stricture marmosets found enrichment of genes associated with intestinal absorption, and lipid metabolism, localization, and transport in stricture cases. Using machine learning, we identified increased abundance of C. perfringens, as a potential causative agent of GI disease and intestinal strictures in marmosets.
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Affiliation(s)
- Alexander Sheh
- Division of Comparative Medicine, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Stephen C Artim
- Division of Comparative Medicine, Massachusetts Institute of Technology, Cambridge, MA, USA
- Merck Research Laboratories, Merck, South San Francisco, CA, USA
| | - Monika A Burns
- Division of Comparative Medicine, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jose Arturo Molina-Mora
- Centro de Investigación en Enfermedades Tropicales (CIET), Universidad de Costa Rica, San José, Costa Rica
| | - Mary Anne Lee
- Division of Comparative Medicine, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biological Sciences, Wellesley College, Wellesley, MA, USA
| | - JoAnn Dzink-Fox
- Division of Comparative Medicine, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - James G Fox
- Division of Comparative Medicine, Massachusetts Institute of Technology, Cambridge, MA, USA.
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Park H, Maruhashi K, Yamaguchi R, Imoto S, Miyano S. Global gene network exploration based on explainable artificial intelligence approach. PLoS One 2020; 15:e0241508. [PMID: 33156825 PMCID: PMC7647077 DOI: 10.1371/journal.pone.0241508] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 09/03/2020] [Indexed: 12/26/2022] Open
Abstract
In recent years, personalized gene regulatory networks have received significant attention, and interpretation of the multilayer networks has been a critical issue for a comprehensive understanding of gene regulatory systems. Although several statistical and machine learning approaches have been developed and applied to reveal sample-specific regulatory pathways, integrative understanding of the massive multilayer networks remains a challenge. To resolve this problem, we propose a novel artificial intelligence (AI) strategy for comprehensive gene regulatory network analysis. In our strategy, personalized gene networks corresponding specific clinical characteristic are constructed and the constructed network is considered as a second-order tensor. Then, an explainable AI method based on deep learning is applied to decompose the multilayer networks, thus we can reveal all-encompassing gene regulatory systems characterized by clinical features of patients. To evaluate the proposed methodology, we apply our method to the multilayer gene networks under varying conditions of an epithelial–mesenchymal transition (EMT) process. From the comprehensive analysis of multilayer networks, we identified novel markers, and the biological mechanisms of the identified genes and their reciprocal mechanisms are verified through the literature. Although any biological knowledge about the identified genes was not incorporated in our analysis, our data-driven approach based on AI approach provides biologically reliable results. Furthermore, the results provide crucial evidences to reveal biological mechanism related to various diseases, e.g., keratinocyte proliferation. The use of explainable AI method based on the tensor decomposition enables us to reveal global and novel mechanisms of gene regulatory system from the massive multiple networks, which cannot be demonstrated by existing methods. We expect that the proposed method provides a new insight into network biology and it will be a useful tool to integrative gene network analysis related complex architectures of diseases.
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Affiliation(s)
- Heewon Park
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
- * E-mail:
| | | | - Rui Yamaguchi
- Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Aichi, Japan
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Seiya Imoto
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
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Park H, Yamada M, Imoto S, Miyano S. Robust Sample-Specific Stability Selection with Effective Error Control. J Comput Biol 2019; 26:202-217. [PMID: 30638394 DOI: 10.1089/cmb.2018.0180] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Identifying individual characteristics is a crucial issue in personalized genome research. To effectively identify sample-specific characteristics, we propose a novel strategy called robust sample-specific stability selection. Although stability selection shows effective feature selection results and has attractive theoretical property (i.e., per-family error rate control), the method's results are sensitive to the value of the regularization parameter because the method performs feature selection based only on the particular parameter value that maximizes the selection probability. To resolve this issue, we propose robust stability selection and show that our method provides an effective theoretical property (i.e., effective per-family error rate control). We also propose a sample-specific random lasso based on the kernel-based L1-type regularization and weighted random sampling. The proposed robust sample-specific stability selection estimates the selection probabilities of variables using the sample-specific random lasso and then selects variables based on robust stability selection. Our method controls the effect of samples on sample-specific analysis by the two-stage strategy (i.e., the weighted random sampling and the kernel-based L1-type approach in the random lasso), and thus we can effectively perform sample-specific analysis without disturbances of samples having characteristics different from those of the target sample. We observe from the numerical studies that our strategies can effectively perform sample-specific analysis and provide biologically reliable results in gene selection.
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Affiliation(s)
- Heewon Park
- 1 Faculty of Global and Science Studies, Yamaguchi University, Yamaguchi-shi, Japan
| | - Makoto Yamada
- 2 Kyoto University, Graduate School of Informatics, Sakyo-ku, Kyoto, Japan. RIKEN, Center for Advanced Intelligence Project, Chuo-gu, Tokyo, Japan. PRESTO, Japan Science and Technological Agency, Kawaguchi-shi, Saitama, Japan
| | - Seiya Imoto
- 3 Health Intelligence Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Satoru Miyano
- 4 Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
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