ArticleJournal of the American Medical Informatics Association : JAMIA
Identifying disease genes and module biomarkers by differential interactions.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed, 136 citations in OpenAlex.
- Trial
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- Natural language processing of biomedical text to map and prioritize protein-disease associations in HFpEF.Computers in biology and medicine · 2026Article
- In silico prediction of differentially expressed genes and functionally grouped networks in patients with inflamed pulp for screening pulpitis biomarkers.European oral research · 2025Article
- Dynamic network biomarker to determine the critical point of breast cancer stage progression.Breast cancer (Tokyo, Japan) · 2023Article
- DM-MOGA: a multi-objective optimization genetic algorithm for identifying disease modules of non-small cell lung cancer.BMC bioinformatics · 2023Article
- Exploring drought-responsive crucial genes iniScience · 2022Article
- Identifying network biomarkers of cancer by sample-specific differential network.BMC bioinformatics · 2022Article
- Data analysis methods for defining biomarkers from omics data.Analytical and bioanalytical chemistry · 2022Review
- Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers.NPJ systems biology and applications · 2021Article
- Locally Adjust Networks Based on Connectivity and Semantic Similarities for Disease Module Detection.Frontiers in genetics · 2021Article
- Evaluation of Single Sample Network Inference Methods for Metabolomics-Based Systems Medicine.Journal of proteome research · 2021Article
- Identification of HCC-Related Genes Based on Differential Partial Correlation Network.Frontiers in genetics · 2021Article
- Article
- Robust edge-based biomarker discovery improves prediction of breast cancer metastasis.BMC bioinformatics · 2020Article
- Identification of Long Noncoding RNA Biomarkers for Hepatocellular Carcinoma Using Single-Sample Networks.BioMed research international · 2020Article
- Theoretical andFrontiers in genetics · 2020Article
- Detecting Diagnostic Biomarkers of Alzheimer's Disease by Integrating Gene Expression Data in Six Brain Regions.Frontiers in genetics · 2019Article
- Mathematical modeling and computational prediction of cancer drug resistance.Briefings in bioinformatics · 2018Review
- Extracting proteins involved in disease progression using temporally connected networks.BMC systems biology · 2018Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveA complex disease is generally caused by the mutation of multiple genes or by the dysfunction of multiple biological processes. Systematic identification of causal disease genes and module biomarkers can provide insights into the mechanisms underlying complex diseases, and help develop efficient therapies or effective drugs. MATERIALS AND
methodsIn this paper, we present a novel approach to predict disease genes and identify dysfunctional networks or modules, based on the analysis of differential interactions between disease and control samples, in contrast to the analysis of differential gene or protein expressions widely adopted in existing methods. RESULTS AND DISCUSSION: As an example, we applied our method to the study of three-stage microarray data for gastric cancer. We identified network modules or module biomarkers that include a set of genes related to gastric cancer, implying the predictive power of our method. The results on holdout validation data sets show that our identified module can serve as an effective module biomarker for accurately detecting or diagnosing gastric cancer, thereby validating the efficiency of our method.
conclusionWe proposed a new approach to detect module biomarkers for diseases, and the results on gastric cancer demonstrated that the differential interactions are useful to detect dysfunctional modules in the molecular interaction network, which in turn can be used as robust module biomarkers.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.