ArticlePLoS computational biology2011
Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis.
Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 39 citations in OpenAlex.
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- Bayesian network analysis of panomic biological big data identifies the importance of triglyceride-rich LDL in atherosclerosis development.Frontiers in cardiovascular medicine · 2022Article
- Coupling of Co-expression Network Analysis and Machine Learning Validation Unearthed Potential Key Genes Involved in Rheumatoid Arthritis.Frontiers in genetics · 2021Article
- Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health.BMJ global health · 2020Article
- Alterations in DNA methylation profiles in cancellous bone of postmenopausal women with osteoporosis.FEBS open bio · 2020Article
- Application of Machine Learning Models to Evaluate Hypoglycemia Risk in Type 2 Diabetes.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2020Article
- Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: a longitudinal cohort study and validation.The Lancet. Neurology · 2017Article
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- Identification of Filamin-A and -B as potential biomarkers for prostate cancer.Future science OA · 2017Article
- Data-driven reverse engineering of signaling pathways using ensembles of dynamic models.PLoS computational biology · 2017Article
- A Bayesian mathematical model of motor and cognitive outcomes in Parkinson's disease.PloS one · 2017Article
- A BAYESIAN GRAPHICAL MODEL FOR GENOME-WIDE ASSOCIATION STUDIES (GWAS).The annals of applied statistics · 2016Article
- A Six-Stage Workflow for Robust Application of Systems Pharmacology.CPT: pharmacometrics & systems pharmacology · 2016Article
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- Blood transcriptomics and metabolomics for personalized medicine.Computational and structural biotechnology journal · 2016Review
- Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes: An Application of Machine Learning Using Electronic Health Records.Journal of diabetes science and technology · 2015Article
- Applications of systems approaches in the study of rheumatic diseases.The Korean journal of internal medicine · 2015Review
- Systems toxicology: from basic research to risk assessment.Chemical research in toxicology · 2014Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86--a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28.
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Registered trials
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.