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Du XX, Liu Y, Wang B, Zhang JF. lncRNA-disease association prediction method based on the nearest neighbor matrix completion model. Sci Rep 2022; 12:21653. [PMID: 36522410 PMCID: PMC9755128 DOI: 10.1038/s41598-022-25730-0] [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: 08/20/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
State-of-the-art medical studies proved that long noncoding ribonucleic acids (lncRNAs) are closely related to various diseases. However, their large-scale detection in biological experiments is problematic and expensive. To aid screening and improve the efficiency of biological experiments, this study introduced a prediction model based on the nearest neighbor concept for lncRNA-disease association prediction. We used a new similarity algorithm in the model that fused potential associations. The experimental validation of the proposed algorithm proved its superiority over the available Cosine, Pearson, and Jaccard similarity algorithms. Satisfactory results in the comparative leave-one-out cross-validation test (with AUC = 0.96) confirmed its excellent predictive performance. Finally, the proposed model's reliability was confirmed by performing predictions using a new dataset, yielding AUC = 0.92.
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Affiliation(s)
- Xiao-xin Du
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
| | - Yan Liu
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
| | - Bo Wang
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
| | - Jian-fei Zhang
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
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2
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Zhang LQ, Yang H, Liu JJ, Zhang LR, Hao YD, Guo JM, Lin H. Recognition of driver genes with potential prognostic implications in lung adenocarcinoma based on H3K79me2. Comput Struct Biotechnol J 2022; 20:5535-5546. [PMID: 36249560 PMCID: PMC9556929 DOI: 10.1016/j.csbj.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 10/01/2022] [Accepted: 10/02/2022] [Indexed: 11/21/2022] Open
Abstract
The efficacy of H3K79me2 on gene expression regulation is affirmed in LUAD. An open-source algorithm for identifying LUAD-related driver genes is presented. 12 H3K79me2-targeted driver genes with clinical values are verified by qPCR. The regions with obvious H3K79me2 signals changes on driver genes are pinpointed.
Lung adenocarcinoma is a malignancy with a low overall survival and a poor prognosis. Studies have shown that lung adenocarcinoma progression relates to locus-specific/global changes in histone modifications. To explore the relationship between histone modification and gene expression changes, we focused on 11 histone modifications and quantitatively analyzed their influences on gene expression. We found that, among the studied histone modifications, H3K79me2 displayed the greatest impact on gene expression regulation. Based on the Shannon entropy, 867 genes with differential H3K79me2 levels during tumorigenesis were identified. Enrichment analyses showed that these genes were involved in 16 common cancer pathways and 11 tumors and were target-regulated by trans-regulatory elements, such as Tp53 and WT1. Then, an open-source computational framework was presented (https://github.com/zlq-imu/Identification-of-potential-LUND-driver-genes). Twelve potential driver genes were extracted from the genes with differential H3K79me2 levels during tumorigenesis. The expression levels of these potential driver genes were significantly increased/decreased in tumor cells, as assayed by RT–qPCR. A risk score model comprising these driver genes was further constructed, and this model was strongly negatively associated with the overall survival of patients in different datasets. The proportional hazards assumption and outlier test indicated that this model could robustly distinguish patients with different survival rates. Immune analyses and responses to immunotherapeutic and chemotherapeutic agents showed that patients in the high and low-risk groups may have distinct tendencies for clinical selection. Finally, the regions with clear H3K79me2 signal changes on these driver genes were accurately identified. Our research may offer potential molecular biomarkers for lung adenocarcinoma treatment.
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Affiliation(s)
- Lu-Qiang Zhang
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China,Corresponding authors.
| | - Hao Yang
- Department of Radiation Oncology, Inner Mongolia Cancer Hospital and Affiliated People's Hospital of Inner Mongolia Medical University, Hohhot 010020, China
| | - Jun-Jie Liu
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Li-Rong Zhang
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Yu-Duo Hao
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Jun-Mei Guo
- Department of Radiation Oncology, Inner Mongolia Cancer Hospital and Affiliated People's Hospital of Inner Mongolia Medical University, Hohhot 010020, China
| | - Hao Lin
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China,Corresponding authors.
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Xie G, Jiang J, Sun Y. LDA-LNSUBRW: lncRNA-Disease Association Prediction Based on Linear Neighborhood Similarity and Unbalanced bi-Random Walk. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:989-997. [PMID: 32870798 DOI: 10.1109/tcbb.2020.3020595] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Increasing number of experiments show that lncRNAs are involved in many biological processes, and their mutations and disorders are associated with many diseases. However, verifying the relationships between lncRNAs and diseases is time consuming and laborio. Searching for effective computational methods will contribute to our understanding of the underlying mechanisms of disease and identifying biomarkers of diseases. Therefore, we proposed a method called lncRNA-disease association prediction based on linear neighborhood similarity and unbalanced bi-random walk (LDA-LNSUBRW). Given that the known lncRNA-disease associations are rare, a pretreatment step should be performed to obtain the interaction possibility of unknown cases, so as to help us predict the potential associations. In the framework of leave-one-out cross-validation (LOOCV)and fivefold cross-validation (5-fold CV), LDA-LNSUBRW achieved effective performance with AUC of 0.8874 and 0.8632 ± 0.0051, respectively. The experimental results in this paper show that the proposed method is superior to five other state-of-the-art methods. In addition, case studies of three diseases (lung cancer, breast cancer, and osteosarcoma)were carried out to illustrate that LDA-LNSUBRW could predict the relevant lncRNAs.
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Ding P, Ouyang W, Luo J, Kwoh CK. Heterogeneous information network and its application to human health and disease. Brief Bioinform 2021; 21:1327-1346. [PMID: 31566212 DOI: 10.1093/bib/bbz091] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 06/29/2019] [Accepted: 06/30/2019] [Indexed: 12/11/2022] Open
Abstract
The molecular components with the functional interdependencies in human cell form complicated biological network. Diseases are mostly caused by the perturbations of the composite of the interaction multi-biomolecules, rather than an abnormality of a single biomolecule. Furthermore, new biological functions and processes could be revealed by discovering novel biological entity relationships. Hence, more and more biologists focus on studying the complex biological system instead of the individual biological components. The emergence of heterogeneous information network (HIN) offers a promising way to systematically explore complicated and heterogeneous relationships between various molecules for apparently distinct phenotypes. In this review, we first present the basic definition of HIN and the biological system considered as a complex HIN. Then, we discuss the topological properties of HIN and how these can be applied to detect network motif and functional module. Afterwards, methodologies of discovering relationships between disease and biomolecule are presented. Useful insights on how HIN aids in drug development and explores human interactome are provided. Finally, we analyze the challenges and opportunities for uncovering combinatorial patterns among pharmacogenomics and cell-type detection based on single-cell genomic data.
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Affiliation(s)
- Pingjian Ding
- School of Computer Science, University of South China, Hengyang, China
| | - Wenjue Ouyang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Chee-Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
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Zhang Y, Chen M, Li A, Cheng X, Jin H, Liu Y. LDAI-ISPS: LncRNA-Disease Associations Inference Based on Integrated Space Projection Scores. Int J Mol Sci 2020; 21:E1508. [PMID: 32098405 PMCID: PMC7073162 DOI: 10.3390/ijms21041508] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 02/18/2020] [Accepted: 02/19/2020] [Indexed: 12/14/2022] Open
Abstract
Long non-coding RNAs (long ncRNAs, lncRNAs) of all kinds have been implicated in a range of cell developmental processes and diseases, while they are not translated into proteins. Inferring diseases associated lncRNAs by computational methods can be helpful to understand the pathogenesis of diseases, but those current computational methods still have not achieved remarkable predictive performance: such as the inaccurate construction of similarity networks and inadequate numbers of known lncRNA-disease associations. In this research, we proposed a lncRNA-disease associations inference based on integrated space projection scores (LDAI-ISPS) composed of the following key steps: changing the Boolean network of known lncRNA-disease associations into the weighted networks via combining all the global information (e.g., disease semantic similarities, lncRNA functional similarities, and known lncRNA-disease associations); obtaining the space projection scores via vector projections of the weighted networks to form the final prediction scores without biases. The leave-one-out cross validation (LOOCV) results showed that, compared with other methods, LDAI-ISPS had a higher accuracy with area-under-the-curve (AUC) value of 0.9154 for inferring diseases, with AUC value of 0.8865 for inferring new lncRNAs (whose associations related to diseases are unknown), with AUC value of 0.7518 for inferring isolated diseases (whose associations related to lncRNAs are unknown). A case study also confirmed the predictive performance of LDAI-ISPS as a helper for traditional biological experiments in inferring the potential LncRNA-disease associations and isolated diseases.
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Affiliation(s)
- Yi Zhang
- School of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China
| | - Min Chen
- Hunan Institute of Technology, School of Computer Science and Technology, Hengyang 421002, China
| | - Ang Li
- Hunan Institute of Technology, School of Computer Science and Technology, Hengyang 421002, China
| | - Xiaohui Cheng
- School of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China
| | - Hong Jin
- School of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China
| | - Yarong Liu
- School of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China
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6
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Sumathipala M, Maiorino E, Weiss ST, Sharma A. Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION. Front Physiol 2019; 10:888. [PMID: 31379598 PMCID: PMC6646690 DOI: 10.3389/fphys.2019.00888] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Accepted: 06/26/2019] [Indexed: 11/13/2022] Open
Abstract
Recently, long-non-coding RNAs (lncRNAs) have attracted attention because of their emerging role in many important biological mechanisms. The accumulating evidence indicates that the dysregulation of lncRNAs is associated with complex diseases. However, only a few lncRNA-disease associations have been experimentally validated and therefore, predicting potential lncRNAs that are associated with diseases become an important task. Current computational approaches often use known lncRNA-disease associations to predict potential lncRNA-disease links. In this work, we exploited the topology of multi-level networks to propose the LncRNA rankIng by NetwOrk DiffusioN (LION) approach to identify lncRNA-disease associations. The multi-level complex network consisted of lncRNA-protein, protein–protein interactions, and protein-disease associations. We applied the network diffusion algorithm of LION to predict the lncRNA-disease associations within the multi-level network. LION achieved an AUC value of 96.8% for cardiovascular diseases, 91.9% for cancer, and 90.2% for neurological diseases by using experimentally verified lncRNAs associated with diseases. Furthermore, compared to a similar approach (TPGLDA), LION performed better for cardiovascular diseases and cancer. Given the versatile role played by lncRNAs in different biological mechanisms that are perturbed in diseases, LION’s accurate prediction of lncRNA-disease associations helps in ranking lncRNAs that could function as potential biomarkers and potential drug targets.
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Affiliation(s)
- Marissa Sumathipala
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.,Harvard College, Cambridge, MA, United States
| | - Enrico Maiorino
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.,Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Amitabh Sharma
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.,Department of Medicine, Harvard Medical School, Boston, MA, United States.,Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
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7
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Huang ZA, Huang YA, You ZH, Zhu Z, Sun Y. Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph. BMC Med Genomics 2018; 11:113. [PMID: 30598112 PMCID: PMC6311942 DOI: 10.1186/s12920-018-0429-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Background Current knowledge and data on miRNA-lncRNA interactions is still limited and little effort has been made to predict target lncRNAs of miRNAs. Accumulating evidences suggest that the interaction patterns between lncRNAs and miRNAs are closely related to relative expression level, forming a titration mechanism. It could provide an effective approach for characteristic feature extraction. In addition, using the coding non-coding co-expression network and sequence data could also help to measure the similarities among miRNAs and lncRNAs. By mathematically analyzing these types of similarities, we come up with two findings that (i) lncRNAs/miRNAs tend to collaboratively interact with miRNAs/lncRNAs of similar expression profiles, and vice versa, and (ii) those miRNAs interacting with a cluster of common target genes tend to jointly target at the common lncRNAs. Methods In this work, we developed a novel group preference Bayesian collaborative filtering model called GBCF for picking up a top-k probability ranking list for an individual miRNA or lncRNA based on the known miRNA-lncRNA interaction network. Results To evaluate the effectiveness of GBCF, leave-one-out and k-fold cross validations as well as a series of comparison experiments were carried out. GBCF achieved the values of area under ROC curve of 0.9193, 0.8354+/− 0.0079, 0.8615+/− 0.0078, and 0.8928+/− 0.0082 based on leave-one-out, 2-fold, 5-fold, and 10-fold cross validations respectively, demonstrating its reliability and robustness. Conclusions GBCF could be used to select potential lncRNA targets of specific miRNAs and offer great insights for further researches on ceRNA regulation network. Electronic supplementary material The online version of this article (10.1186/s12920-018-0429-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zhi-An Huang
- College of Computer Science and Software Engineering, Shenzhen Universit1y, Shenzhen, 518060, China.,Department of Computer Science, City University of Hong Kong, Hong Kong, 999077, China
| | - Yu-An Huang
- Department of Computing, Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Zhu-Hong You
- Department of Computing, Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Zexuan Zhu
- College of Computer Science and Software Engineering, Shenzhen Universit1y, Shenzhen, 518060, China
| | - Yiwen Sun
- School of Medicine, Shenzhen University, Shenzhen, 518060, China.
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Shi JY, Zhang AQ, Zhang SW, Mao KT, Yiu SM. A unified solution for different scenarios of predicting drug-target interactions via triple matrix factorization. BMC SYSTEMS BIOLOGY 2018; 12:136. [PMID: 30598094 PMCID: PMC6311903 DOI: 10.1186/s12918-018-0663-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Background During the identification of potential candidates, computational prediction of drug-target interactions (DTIs) is important to subsequent expensive validation in wet-lab. DTI screening considers four scenarios, depending on whether the drug is an existing or a new drug and whether the target is an existing or a new target. However, existing approaches have the following limitations. First, only a few of them can address the most difficult scenario (i.e., predicting interactions between new drugs and new targets). More importantly, none of the existing approaches could provide the explicit information for understanding the mechanism of forming interactions, such as the drug-target feature pairs contributing to the interactions. Results In this paper, we propose a Triple Matrix Factorization-based model (TMF) to tackle these problems. Compared with former state-of-the-art predictive methods, TMF demonstrates its significant superiority by assessing the predictions on four benchmark datasets over four kinds of screening scenarios. Also, it exhibits its outperformance by validating predicted novel interactions. More importantly, by using PubChem fingerprints of chemical structures as drug features and occurring frequencies of amino acid trimer as protein features, TMF shows its ability to find out the features determining interactions, including dominant feature pairs, frequently occurring substructures, and conserved triplet of amino acids. Conclusions Our TMF provides a unified framework of DTI prediction for all the screening scenarios. It also presents a new insight for the underlying mechanism of DTIs by indicating dominant features, which play important roles in the forming of DTI. Electronic supplementary material The online version of this article (10.1186/s12918-018-0663-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi'An, China.
| | - An-Qi Zhang
- School of Life Sciences, Northwestern Polytechnical University, Xi'An, China
| | - Shao-Wu Zhang
- School of Automations, Northwestern Polytechnical University, Xi'An, China
| | - Kui-Tao Mao
- School of Computer Science, Northwestern Polytechnical University, Xi'An, China
| | - Siu-Ming Yiu
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
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Shi JY, Huang H, Zhang YN, Cao JB, Yiu SM. BMCMDA: a novel model for predicting human microbe-disease associations via binary matrix completion. BMC Bioinformatics 2018; 19:281. [PMID: 30367598 PMCID: PMC6101089 DOI: 10.1186/s12859-018-2274-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Human Microbiome Project reveals the significant mutualistic influence between human body and microbes living in it. Such an influence lead to an interesting phenomenon that many noninfectious diseases are closely associated with diverse microbes. However, the identification of microbe-noninfectious disease associations (MDAs) is still a challenging task, because of both the high cost and the limitation of microbe cultivation. Thus, there is a need to develop fast approaches to screen potential MDAs. The growing number of validated MDAs enables us to meet the demand in a new insight. Computational approaches, especially machine learning, are promising to predict MDA candidates rapidly among a large number of microbe-disease pairs with the advantage of no limitation on microbe cultivation. Nevertheless, a few computational efforts at predicting MDAs are made so far. RESULTS In this paper, grouping a set of MDAs into a binary MDA matrix, we propose a novel predictive approach (BMCMDA) based on Binary Matrix Completion to predict potential MDAs. The proposed BMCMDA assumes that the incomplete observed MDA matrix is the summation of a latent parameterizing matrix and a noising matrix. It also assumes that the independently occurring subscripts of observed entries in the MDA matrix follows a binomial model. Adopting a standard mean-zero Gaussian distribution for the nosing matrix, we model the relationship between the parameterizing matrix and the MDA matrix under the observed microbe-disease pairs as a probit regression. With the recovered parameterizing matrix, BMCMDA deduces how likely a microbe would be associated with a particular disease. In the experiment under leave-one-out cross-validation, it exhibits the inspiring performance (AUC = 0.906, AUPR =0.526) and demonstrates its superiority by ~ 7% and ~ 5% improvements in terms of AUC and AUPR respectively in the comparison with the pioneering approach KATZHMDA. CONCLUSIONS Our BMCMDA provides an effective approach for predicting MDAs and can be also extended to other similar predicting tasks of binary relationship (e.g. protein-protein interaction, drug-target interaction).
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Affiliation(s)
- Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi’an, 70072 China
| | - Hua Huang
- School of Software and Microelectronics, Northwestern Polytechnical University, Xi’an, 70072 China
| | - Yan-Ning Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi’an, 70072 China
| | - Jiang-Bo Cao
- School of Life Sciences, Northwestern Polytechnical University, Xi’an, 70072 China
| | - Siu-Ming Yiu
- Department of Computer Science, The University of Hong Kong, Hong Kong, 999077 China
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10
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Shi JY, Shang XQ, Gao K, Zhang SW, Yiu SM. An Integrated Local Classification Model of Predicting Drug-Drug Interactions via Dempster-Shafer Theory of Evidence. Sci Rep 2018; 8:11829. [PMID: 30087377 PMCID: PMC6081396 DOI: 10.1038/s41598-018-30189-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 07/24/2018] [Indexed: 12/19/2022] Open
Abstract
Drug-drug interactions (DDIs) may trigger adverse drug reactions, which endanger the patients. DDI identification before making clinical medications is critical but bears a high cost in clinics. Computational approaches, including global model-based and local model based, are able to screen DDI candidates among a large number of drug pairs by utilizing preliminary characteristics of drugs (e.g. drug chemical structure). However, global model-based approaches are usually slow and don't consider the topological structure of DDI network, while local model-based approaches have the degree-induced bias that a new drug tends to link to the drug having many DDI. All of them lack an effective ensemble method to combine results from multiple predictors. To address the first two issues, we propose a local classification-based model (LCM), which considers the topology of DDI network and has the relaxation of the degree-induced bias. Furthermore, we design a novel supervised fusion rule based on the Dempster-Shafer theory of evidence (LCM-DS), which aggregates the results from multiple LCMs. To make the final prediction, LCM-DS integrates three aspects from multiple classifiers, including the posterior probabilities output by individual classifiers, the proximity between their instance decision profiles and their reference profiles, as well as the quality of their reference profiles. Last, the substantial comparison with three state-of-the-art approaches demonstrates the effectiveness of our LCM, and the comparison with both individual LCM implementations and classical fusion algorithms exhibits the superiority of our LCM-DS.
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Affiliation(s)
- Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, China.
| | - Xue-Qun Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Ke Gao
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Shao-Wu Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Siu-Ming Yiu
- Department of Computer Science, The University of Hong Kong, Hong Kong, 999077, China
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Schönbach C, Li J, Ma L, Horton P, Sjaugi MF, Ranganathan S. A bioinformatics potpourri. BMC Genomics 2018; 19:920. [PMID: 29363432 PMCID: PMC5780851 DOI: 10.1186/s12864-017-4326-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
The 16th International Conference on Bioinformatics (InCoB) was held at Tsinghua University, Shenzhen from September 20 to 22, 2017. The annual conference of the Asia-Pacific Bioinformatics Network featured six keynotes, two invited talks, a panel discussion on big data driven bioinformatics and precision medicine, and 66 oral presentations of accepted research articles or posters. Fifty-seven articles comprising a topic assortment of algorithms, biomolecular networks, cancer and disease informatics, drug-target interactions and drug efficacy, gene regulation and expression, imaging, immunoinformatics, metagenomics, next generation sequencing for genomics and transcriptomics, ontologies, post-translational modification, and structural bioinformatics are the subject of this editorial for the InCoB2017 supplement issues in BMC Genomics, BMC Bioinformatics, BMC Systems Biology and BMC Medical Genomics. New Delhi will be the location of InCoB2018, scheduled for September 26-28, 2018.
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Affiliation(s)
- Christian Schönbach
- International Research Center for Medical Sciences, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, 860-0811 Japan
| | - Jinyan Li
- The Advanced Analytics Institute, University of Technology Sydney, Sydney, NSW 2007 Australia
| | - Lan Ma
- Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055 People’s Republic of China
| | - Paul Horton
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, 135-0064 Japan
| | | | - Shoba Ranganathan
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109 Australia
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