1
|
Karimi N, Motovali-Bashi M, Ghaderi-Zefrehei M. Gene network reveals LASP1, TUBA1C, and S100A6 are likely playing regulatory roles in multiple sclerosis. Front Neurol 2023; 14:1090631. [PMID: 36970516 PMCID: PMC10035600 DOI: 10.3389/fneur.2023.1090631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 02/10/2023] [Indexed: 03/11/2023] Open
Abstract
IntroductionMultiple sclerosis (MS), a non-contagious and chronic disease of the central nervous system, is an unpredictable and indirectly inherited disease affecting different people in different ways. Using Omics platforms genomics, transcriptomics, proteomics, epigenomics, interactomics, and metabolomics database, it is now possible to construct sound systems biology models to extract full knowledge of the MS and recognize the pathway to uncover the personalized therapeutic tools.MethodsIn this study, we used several Bayesian Networks in order to find the transcriptional gene regulation networks that drive MS disease. We used a set of BN algorithms using the R add-on package bnlearn. The BN results underwent further downstream analysis and were validated using a wide range of Cytoscape algorithms, web based computational tools and qPCR amplification of blood samples from 56 MS patients and 44 healthy controls. The results were semantically integrated to improve understanding of the complex molecular architecture underlying MS, distinguishing distinct metabolic pathways and providing a valuable foundation for the discovery of involved genes and possibly new treatments.ResultsResults show that the LASP1, TUBA1C, and S100A6 genes were most likely playing a biological role in MS development. Results from qPCR showed a significant increase (P < 0.05) in LASP1 and S100A6 gene expression levels in MS patients compared to that in controls. However, a significant down regulation of TUBA1C gene was observed in the same comparison.ConclusionThis study provides potential diagnostic and therapeutic biomarkers for enhanced understanding of gene regulation underlying MS.
Collapse
Affiliation(s)
- Nafiseh Karimi
- Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
| | - Majid Motovali-Bashi
- Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
- *Correspondence: Majid Motovali-Bashi
| | | |
Collapse
|
2
|
Jiang L, Hao S, Lin L, Gao X, Xu J. fRNC: Uncovering the dynamic and condition-specific RBP-ncRNA circuits from multi-omics data. Comput Struct Biotechnol J 2023; 21:2276-2285. [PMID: 37035550 PMCID: PMC10073992 DOI: 10.1016/j.csbj.2023.03.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 03/15/2023] [Accepted: 03/21/2023] [Indexed: 03/30/2023] Open
Abstract
The RNA binding protein (RBP) and non-coding RNA (ncRNA) interacting networks are increasingly recognized as the main mechanism in gene regulation, and are tightly associated with cellular malfunction and disease. Here, we present fRNC, a systems biology tool to uncover the dynamic spectrum of RBP-ncRNA circuits (RNC) by integrating transcriptomics, interactomics and proteomics data. fRNC constructs the RBP-ncRNA network derived from CLIP-seq or PARE experiments. Given scoring on nodes and edges according to differential analysis of expression data, it finds an RNC containing global maximum significant RBPs and ncRNAs. Alternatively, it can also capture the locally maximum scoring RNC according to user-defined starting nodes with the greedy search. When compared with existing tools, fRNC can detect more accurate and robust sub-network with scalability. As shown in the cases of esophageal carcinoma, breast cancer and Alzheimer's disease, fRNC enables users to analyze the collective behaviors between RBP and the interacting ncRNAs, and reveal novel insights into the disease-associated processes. The fRNC R package is available at https://github.com/BioinformaticsSTU/fRNC.
Collapse
|
3
|
Manipur I, Giordano M, Piccirillo M, Parashuraman S, Maddalena L. Community Detection in Protein-Protein Interaction Networks and Applications. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:217-237. [PMID: 34951849 DOI: 10.1109/tcbb.2021.3138142] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.
Collapse
|
4
|
Valori M, Lehikoinen J, Jansson L, Clancy J, Lundgren SA, Mustjoki S, Tienari P. High prevalence of low-allele-fraction somatic mutations in STAT3 in peripheral blood CD8+ cells in multiple sclerosis patients and controls. PLoS One 2022; 17:e0278245. [PMID: 36441748 PMCID: PMC9704626 DOI: 10.1371/journal.pone.0278245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/14/2022] [Indexed: 11/30/2022] Open
Abstract
Somatic mutations have a central role in cancer, but there are also a few rare autoimmune diseases in which somatic mutations play a major role. We have recently shown that nonsynonymous somatic mutations with low allele fractions are preferentially detectable in CD8+ cells and that the STAT3 gene is a promising target for screening. Here, we analyzed somatic mutations in the STAT3 SH2 domain in peripheral blood CD8+ cells in a set of 94 multiple sclerosis (MS) patients and 99 matched controls. PCR amplicons targeting the exons 20 and 21 of STAT3 were prepared and sequenced using the Illumina MiSeq instrument with 2x300bp reads. We designed a novel variant calling method, optimized for large number of samples, high sequencing depth (>25,000x) and small target genomic area. Overall, we discovered 64 STAT3 somatic mutations in the 193 donors, of which 63 were non-synonymous and 77% have been previously reported in cancer or lymphoproliferative disease. The overall median variant allele fraction was 0.065% (range 0.007-1.2%), without significant difference between MS and controls (p = 0.82). There were 26 (28%) MS patients vs. 24 (24%) controls with mutations (p = 0.62). Two or more mutations were found in 9 MS patients vs. 2 controls (p = 0.03, pcorr = 0.12). Carriership of mutations associated with older age and lower neutrophil counts. These results demonstrate that STAT3 SH2 domain is a hotspot for somatic mutations in CD8+ cells with a prevalence of 26% among the participants. There were no significant differences in the mutation prevalences between MS patients and controls. Further research is needed to elucidate the role of antigenic stimuli in the expansion of the mutant clones. Furthermore, the high discovered prevalence of STAT3 somatic mutations makes it feasible to analyze these mutations directly in tissue-infiltrating CD8+ cells in autoimmune diseases.
Collapse
Affiliation(s)
- Miko Valori
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- * E-mail:
| | - Joonas Lehikoinen
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- Neurocenter, Helsinki University Hospital, Helsinki, Finland
| | - Lilja Jansson
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- Neurocenter, Helsinki University Hospital, Helsinki, Finland
| | - Jonna Clancy
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Sofie A. Lundgren
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- Hematology Research Unit Helsinki, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
| | - Satu Mustjoki
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- Hematology Research Unit Helsinki, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
| | - Pentti Tienari
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- Neurocenter, Helsinki University Hospital, Helsinki, Finland
| |
Collapse
|
5
|
Ding Q, Yang W, Luo M, Xu C, Xu Z, Pang F, Cai Y, Anashkina AA, Su X, Chen N, Jiang Q. CBLRR: a cauchy-based bounded constraint low-rank representation method to cluster single-cell RNA-seq data. Brief Bioinform 2022; 23:6649282. [DOI: 10.1093/bib/bbac300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/17/2022] [Accepted: 07/02/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
The rapid development of single-cel+l RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for exploring biological phenomena at the single-cell level. The discovery of cell types is one of the major applications for researchers to explore the heterogeneity of cells. Some computational methods have been proposed to solve the problem of scRNA-seq data clustering. However, the unavoidable technical noise and notorious dropouts also reduce the accuracy of clustering methods. Here, we propose the cauchy-based bounded constraint low-rank representation (CBLRR), which is a low-rank representation-based method by introducing cauchy loss function (CLF) and bounded nuclear norm regulation, aiming to alleviate the above issue. Specifically, as an effective loss function, the CLF is proven to enhance the robustness of the identification of cell types. Then, we adopt the bounded constraint to ensure the entry values of single-cell data within the restricted interval. Finally, the performance of CBLRR is evaluated on 15 scRNA-seq datasets, and compared with other state-of-the-art methods. The experimental results demonstrate that CBLRR performs accurately and robustly on clustering scRNA-seq data. Furthermore, CBLRR is an effective tool to cluster cells, and provides great potential for downstream analysis of single-cell data. The source code of CBLRR is available online at https://github.com/Ginnay/CBLRR.
Collapse
Affiliation(s)
- Qian Ding
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| | - Wenyi Yang
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| | - Meng Luo
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| | - Chang Xu
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| | - Zhaochun Xu
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| | - Fenglan Pang
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| | - Yideng Cai
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| | - Anastasia A Anashkina
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences , Moscow, Russia
| | - Xi Su
- Foshan Maternity & Child Healthcare Hospital, Southern Medical University , Foshan, Guangdong, China
| | - Na Chen
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University , Jinan, Shandong, China
| | - Qinghua Jiang
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| |
Collapse
|
6
|
A Method for Bridging Population-Specific Genotypes to Detect Gene Modules Associated with Alzheimer's Disease. Cells 2022; 11:cells11142219. [PMID: 35883662 PMCID: PMC9319087 DOI: 10.3390/cells11142219] [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: 05/29/2022] [Revised: 07/12/2022] [Accepted: 07/14/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Genome-wide association studies have successfully identified variants associated with multiple conditions. However, generalizing discoveries across diverse populations remains challenging due to large variations in genetic composition. Methods that perform gene expression imputation have attempted to address the transferability of gene discoveries across populations, but with limited success. METHODS Here, we introduce a pipeline that combines gene expression imputation with gene module discovery, including a dense gene module search and a gene set variation analysis, to address the transferability issue. Our method feeds association probabilities of imputed gene expression with a selected phenotype into tissue-specific gene-module discovery over protein interaction networks to create higher-level gene modules. RESULTS We demonstrate our method's utility in three case-control studies of Alzheimer's disease (AD) for three different race/ethnic populations (Whites, African descent and Hispanics). We discovered 182 AD-associated genes from gene modules shared between these populations, highlighting new gene modules associated with AD. CONCLUSIONS Our innovative framework has the potential to identify robust discoveries across populations based on gene modules, as demonstrated in AD.
Collapse
|
7
|
Liu A, Manuel AM, Dai Y, Zhao Z. Prioritization of risk genes in multiple sclerosis by a refined Bayesian framework followed by tissue-specificity and cell type feature assessment. BMC Genomics 2022; 23:362. [PMID: 35545758 PMCID: PMC9092676 DOI: 10.1186/s12864-022-08580-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 04/22/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Multiple sclerosis (MS) is a debilitating immune-mediated disease of the central nervous system that affects over 2 million people worldwide, resulting in a heavy burden to families and entire communities. Understanding the genetic basis underlying MS could help decipher the pathogenesis and shed light on MS treatment. We refined a recently developed Bayesian framework, Integrative Risk Gene Selector (iRIGS), to prioritize risk genes associated with MS by integrating the summary statistics from the largest GWAS to date (n = 115,803), various genomic features, and gene-gene closeness. RESULTS We identified 163 MS-associated prioritized risk genes (MS-PRGenes) through the Bayesian framework. We replicated 35 MS-PRGenes through two-sample Mendelian randomization (2SMR) approach by integrating data from GWAS and Genotype-Tissue Expression (GTEx) expression quantitative trait loci (eQTL) of 19 tissues. We demonstrated that MS-PRGenes had more substantial deleterious effects and disease risk. Moreover, single-cell enrichment analysis indicated MS-PRGenes were more enriched in activated macrophages and microglia macrophages than non-activated ones in control samples. Biological and drug enrichment analyses highlighted inflammatory signaling pathways. CONCLUSIONS In summary, we predicted and validated a high-confidence MS risk gene set from diverse genomic, epigenomic, eQTL, single-cell, and drug data. The MS-PRGenes could further serve as a benchmark of MS GWAS risk genes for future validation or genetic studies.
Collapse
Affiliation(s)
- Andi Liu
- grid.267308.80000 0000 9206 2401Department of Epidemiology, School of Public Health, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA ,grid.267308.80000 0000 9206 2401Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Astrid M. Manuel
- grid.267308.80000 0000 9206 2401Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Yulin Dai
- grid.267308.80000 0000 9206 2401Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Zhongming Zhao
- grid.267308.80000 0000 9206 2401Department of Epidemiology, School of Public Health, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA ,grid.267308.80000 0000 9206 2401Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA ,grid.267308.80000 0000 9206 2401Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| |
Collapse
|
8
|
Yang J, Li H, Wang F, Xiao F, Yan W, Hu G. Network-Based Target Prioritization and Drug Candidate Identification for Multiple Sclerosis: From Analyzing "Omics Data" to Druggability Simulations. ACS Chem Neurosci 2021; 12:917-929. [PMID: 33565875 DOI: 10.1021/acschemneuro.1c00011] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Multiple sclerosis (MS) is the most common chronic inflammatory demyelinating disease of the central nervous system. While the drugs currently available for MS provide symptomatic benefit, there is no curative treatment. The emergence of large-scale multiomics data and network theory provide new opportunities for drug discovery in MS, as these are promising strategies for developing novel drugs. In this study, we proposed a computational framework that combined biomolecular network modeling and structural dynamics analysis to facilitate the discovery of new drugs with potential activity in MS. First, we developed a new shortest path-based algorithm that prioritized differentially expressed genes using a newly topological and functional exploration of protein-protein interaction network. Then, pathway enrichment analysis and an assessment of target druggability suggested that TNF-α-induced protein 3 (TNFAIP3), which is involved in NF-κ B signaling, could be a potential therapeutic target for MS. Finally, druggability simulations and mutation enrichment analysis of the TNFAIP3 dimer presented two druggable sites. Follow-up pharmacophore model-based virtual screening of the two sites yielded 30 hit compounds with low energy scores. In summary, this novel method based on analyzing "omics data" and performing druggability simulations, is a systematic approach that unravels disease mechanisms and links them to the chemical space to develop treatments and can be applied to other complex diseases.
Collapse
Affiliation(s)
- Ji Yang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Hongchun Li
- Research Center for Computer-Aided Drug Discovery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Fan Wang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Fei Xiao
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Wenying Yan
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| |
Collapse
|
9
|
Dai Y, O'Brien TD, Pei G, Zhao Z, Jia P. Characterization of genome-wide association study data reveals spatiotemporal heterogeneity of mental disorders. BMC Med Genomics 2020; 13:192. [PMID: 33371872 PMCID: PMC7771094 DOI: 10.1186/s12920-020-00832-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 11/23/2020] [Indexed: 12/15/2022] Open
Abstract
Background Psychiatric disorders such as schizophrenia (SCZ), bipolar disorder (BIP), major depressive disorder (MDD), attention deficit-hyperactivity disorder (ADHD), and autism spectrum disorder (ASD) are often related to brain development. Both shared and unique biological and neurodevelopmental processes have been reported to be involved in these disorders. Methods In this work, we developed an integrative analysis framework to seek for the sensitive spatiotemporal point during brain development underlying each disorder. Specifically, we first identified spatiotemporal gene co-expression modules for four brain regions three developmental stages (prenatal, birth to 11 years old, and older than 13 years), totaling 12 spatiotemporal sites. By integrating GWAS summary statistics and the spatiotemporal co-expression modules, we characterized the risk genes and their co-expression partners for five disorders. Results We found that SCZ and BIP, ASD and ADHD tend to cluster with each other and keep a distance from other psychiatric disorders. At the gene level, we identified several genes that were shared among the most significant modules, such as CTNNB1 and LNX1, and a hub gene, ATF2, in multiple modules. Moreover, we pinpointed two spatiotemporal points in the prenatal stage with active expression activities and highlighted one postnatal point for BIP. Further functional analysis of the disorder-related module highlighted the apoptotic signaling pathway for ASD and the immune-related and cell-cell adhesion function for SCZ, respectively. Conclusion Our study demonstrated the dynamic changes of disorder-related genes at the network level, shedding light on the spatiotemporal regulation during brain development.
Collapse
Affiliation(s)
- Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA
| | - Timothy D O'Brien
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA
| | - Guangsheng Pei
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA. .,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. .,MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA. .,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA.
| |
Collapse
|
10
|
Zhang C, Mathé E, Ning X, Zhao Z, Wang K, Li L, Guo Y. The International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019): computational methods and applications in medical genomics. BMC Med Genomics 2020; 13:47. [PMID: 32241271 PMCID: PMC7119270 DOI: 10.1186/s12920-020-0678-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
In this editorial, we briefly summarized the International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019) that was held on June 9-11, 2019 at Columbus, Ohio, USA. We further introduced the 19 research articles included in this supplement issue, covering four major areas, namely computational method development, genomics analysis, network-based analysis and biomarker prediction. The selected papers perform cutting edge computational research applied to a broad range of human diseases such as cancer, neural degenerative and chronic inflammatory disease. They also proposed solutions for fundamental medical genomics problems range from basic data processing and quality control to functional interpretation, biomarker and drug prediction, and database releasing.
Collapse
Affiliation(s)
- Chi Zhang
- Department of Medical & Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN 46202 USA
| | - Ewy Mathé
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210 USA
| | - Xia Ning
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210 USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
| | - Lang Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210 USA
| | - Yan Guo
- Department of internal medicine, comprehensive cancer center, University of New Mexico, Albuquerque, NM 87131 USA
| |
Collapse
|