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Identification of Differentially Expressed Gene Modules in Heterogeneous Diseases
[article]
2020
bioRxiv
pre-print
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Results: Here we present DESMOND, a new method for identification of Differentially ExpreSsed gene MOdules iN Diseases. ...
Motivation: Identification of differentially expressed genes is necessary for unraveling disease pathogenesis. ...
Iterative signature algorithm for the analysis of large-scale gene expression data. Physical Review E, 67(3). Bollobás, B., Borgs, C., Chayes, J. et al (2003). Directed scale-free graphs. ...
doi:10.1101/2020.04.23.055004
fatcat:nar2ms7gb5hgdpy26unvlblsoa
Identification of Differentially Expressed Gene Modules in Heterogeneous Diseases
2021
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They are capable of identifying genes with a similar expression pattern in a previously unknown subset of samples. After an overview [...] ...
It further focuses on biclustering methods which seem to be very promising in the context of disease heterogeneity. ...
To solve the formulated problem, a new method for identification of Differentially ExpreSsed gene MOdules iN Diseases called DESMOND has been developed [298] . ...
doi:10.4119/unibi/2954162
fatcat:56wt3qyycbapfldgqeir45xzj4
Analysis of gene expression and connectivity on hippocampus of Alzheimer's disease by a new comprehensive approach
[article]
2020
bioRxiv
pre-print
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Our results suggest that changes of gene expression in hippocampus of AD patients are highly heterogeneous at the individual gene level, while biological pathways annotated for PPI modules identified based ...
Gene expression and gene connectivity describe two different functional aspects of a gene. These two different measures reveal different information about the involvement of genes in disorders. ...
(3) AD is heterogeneous at individual gene level, but many differential genes are involved in the same nucleus-associated pathways and disease regulatory modules. ...
doi:10.1101/2020.01.14.906446
fatcat:4du7wpojzvb5zffgvkuxgariqu
Novel meta-analysis pipeline of heterogeneous high-throughput gene expression datasets reveals dysregulated interactions and pathways in asthma
[article]
2019
medRxiv
pre-print
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Then, The datasets are pre-processed and subjected to Weighted Gene Co-expression Network Analysis (WGCNA) for identification of functional modules. ...
However, there is a poor consensus in our understanding of the molecular factors involved in the mechanism of this disease due to inherent genetic heterogeneity. ...
The funders had no role in the designing of the research, decision to publish, or authoring of manuscript. Figure S1 . Box plots of quantile-normalized gene expression matrices. File 1. ...
doi:10.1101/19012377
fatcat:z4o372ksgrcmjfufzgdpvmbbq4
Integrative Analysis for Elucidating Transcriptomics Landscapes of Glucocorticoid-Induced Osteoporosis
2020
Frontiers in Cell and Developmental Biology
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Differential expression analysis revealed 1047 and 844 differentially expressed genes in the two datasets. ...
After integrating differentially expressed glucocorticoid-related genes, we found that most of the robust differentially expressed genes were up-regulated. ...
Differential Expression Analysis of Glucocorticoid-Related Genes The occurrence of diseases is often accompanied by gene expression disorders. ...
doi:10.3389/fcell.2020.00252
pmid:32373610
pmcid:PMC7176994
fatcat:ekhfkk2o6bfvpewnuchhwgov7m
Identifying dysfunctional miRNA-mRNA regulatory modules by inverse activation, cofunction, and high interconnection of target genes: A case study of glioblastoma
2013
Neuro-Oncology
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We identified dysfunctional miRNAs, which were differentially expressed and inversely regulated most of their target genes, by integrating paired miRNA and mRNA expression profiles and miRNA target information ...
In this study, we proposed a multistep method to identify dysfunctional miRNA-mRNA regulatory modules (dMiMRMs) in a specific disease, in which a group of miRNAs cooperatively regulate a group of target ...
Conflict of interest statement. None declared. ...
doi:10.1093/neuonc/not018
pmid:23516263
pmcid:PMC3688007
fatcat:grxz6amzdvcbtjnskjvhm3xpuq
Unravelling personalized dysfunctional gene network of complex diseases based on differential network model
2015
Journal of Translational Medicine
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However, due to the heterogeneity of disease samples, many disease genes are even not always consistently up-/ down-regulated, leading to be under-estimated. ...
network) even when disease samples are heterogeneous, and thus can provide new features like gene-pairs, in addition to the conventional individual genes, to the analysis of the personalized diagnosis ...
samples (e.g., heterogeneity of diseases [5] ). ...
doi:10.1186/s12967-015-0546-5
pmid:26070628
pmcid:PMC4467679
fatcat:zifnm2ynrvbqzjv4334tbk2qwm
Understanding Genotype-Phenotype Effects in Cancer via Network Approaches
2016
PLoS Computational Biology
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Cancer is now increasingly studied from the perspective of dysregulated pathways, rather than as a disease resulting from mutations of individual genes. ...
A pathway-centric view acknowledges the heterogeneity between genomic profiles from different cancer patients while assuming that the mutated genes are likely to belong to the same pathway and cause similar ...
Also, in the case in which alteration refers to differential gene expression, each cancer case will have many differentially expressed genes because of indirect impact. ...
doi:10.1371/journal.pcbi.1004747
pmid:26963104
pmcid:PMC4786343
fatcat:p2zqfax3gfag7jutp3tq52tqve
MSIGNET: A Bayesian Approach for Disease-associated Gene Network Identification
2020
OBM Genetics
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MSIGNET integrates disease gene expression data and human protein-protein interactions in a Bayesian network, and identifies interactions of genes specifically expressed under the disease condition. ...
Yet, for many heterogeneous diseases, the number of known disease-associated genes is limited. Identifying disease-associated genes is still an open challenge. ...
The conditional probability represents the likelihood of genes in that are differentially expressed between disease subjects and control subjects in the gene expression dataset . ...
doi:10.21926/obm.genet.2002107
fatcat:247isvcgk5cjpp3swm4pdoqjmi
Single Cell Atlas of Human Putamen Reveals Disease Specific Changes in Synucleinopathies: Parkinson's Disease and Multiple System Atrophy
[article]
2021
bioRxiv
pre-print
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Differentially expressed genes in major cell types are enriched for genes associated with PD-GWAS loci. ...
We also identified disease associated gene modules using a network biology approach. ...
GFAP and CD44 (B) Differential modulation of enriched biological pathways in MSA and PD clusters compared to Control, underscores contrasting altered heterogeneity in PD and MSA (C) Scaled average expression ...
doi:10.1101/2021.05.06.442950
fatcat:2xxhyvajrbdg5lxds5wjcsujwa
Single-Cell RNA Sequencing of the Cardiovascular System: New Looks for Old Diseases
2019
Frontiers in Cardiovascular Medicine
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Cardiovascular disease encompasses a wide range of conditions, resulting in the highest number of deaths worldwide. ...
a wide variety of diseases. ...
This was the first study showing a GWAS-identified gene mediating SMC phenotypic modulation in vivo in the setting of coronary artery disease. ...
doi:10.3389/fcvm.2019.00173
pmid:31921894
pmcid:PMC6914766
fatcat:5keydeeyw5frtebwit76mf7nkm
An integrated approach to identify causal network modules of complex diseases with application to colorectal cancer
2013
JAMIA Journal of the American Medical Informatics Association
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Many methods have been developed to identify disease genes and further module biomarkers of complex diseases based on gene expression data. ...
It is generally difficult to distinguish whether the variations in gene expression are causative or merely the effect of a disease. ...
Acknowledgements The authors would like to thank Dr Tao Zeng (Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences) for his helpful discussions and suggestions. ...
doi:10.1136/amiajnl-2012-001168
pmid:22967703
pmcid:PMC3721155
fatcat:eipl4asyufabro72mlzkn4lv2u
Deeper insights into long-term survival heterogeneity of Pancreatic Ductal Adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses
[article]
2020
bioRxiv
pre-print
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Findings: We identified 173 differentially expressed genes (DEGs) in ST and LT survivors and five modules (including 38 DEGs) showing associations to clinical traits such as tumor size and chemotherapy ...
Furthermore, we applied two gene prioritization approaches: random walk-based Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics ...
Group based DEGs analysis: Differential Gene analysis and functional follow-up We used DEseq2 4 for the identification of differentially expressed genes (DEG), with the thresholds log2 fold change ≥2 and ...
doi:10.1101/2020.06.01.116194
fatcat:at46sigm3vh4naqy2qinsbmo3i
Identification of Novel Potential Genes Involved in Cancer by Integrated Comparative Analyses
2020
International Journal of Molecular Sciences
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We identified differentially-expressed genes, pathways that are significantly dysregulated across treatments, and characterized genes among those involved in induced cell death. ...
Here, we analyzed gene expression data from induction of programmed cell death and stress response in Homo sapiens and compared the results with Saccharomyces cerevisiae gene expression during the response ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/ijms21249560
pmid:33334055
fatcat:4astbldnmbh47by4wcp6pfaacq
A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma
2015
Human Molecular Genetics
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Recent advances in genetics have spurred rapid progress towards the systematic identification of genes involved in complex diseases. ...
We find that the asthma disease module is enriched with modest GWAS P-values against the background of random variation, and with differentially expressed genes from normal and asthmatic fibroblast cells ...
Funding This research received funding from Janssen R&D and was supported in part by National Institutes of Health (NIH) grants P50-HG004233-CEGS, MapGen grant (1U01HL108630-01) and 5P01-HL083069-5, U01 ...
doi:10.1093/hmg/ddv001
pmid:25586491
pmcid:PMC4447811
fatcat:7iuvo4qqsrbv3g7ipab3pzfh7e
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