ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2010
A fast algorithm for learning epistatic genomic relationships.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- Modeling miRNA-mRNA interactions that cause phenotypic abnormality in breast cancer patients.PloS one · 2017Article
- epiACO - a method for identifying epistasis based on ant Colony optimization algorithm.BioData mining · 2017Article
- Discovering causal interactions using Bayesian network scoring and information gain.BMC bioinformatics · 2016Article
- Evaluation of a two-stage framework for prediction using big genomic data.Briefings in bioinformatics · 2015Article
- LEAP: biomarker inference through learning and evaluating association patterns.Genetic epidemiology · 2015Article
- An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions.BioMed research international · 2015Article
- Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring.PloS one · 2015Article
- Study of integrated heterogeneous data reveals prognostic power of gene expression for breast cancer survival.PloS one · 2015Article
- A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets.Journal of the American Medical Informatics Association : JAMIA · 2014Article
- Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data.Cancer informatics · 2014Review
- Modeling the altered expression levels of genes on signaling pathways in tumors as causal bayesian networks.Cancer informatics · 2014Article
- eQTL Epistasis - Challenges and Computational Approaches.Frontiers in genetics · 2013Article
- Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionality.PloS one · 2012Article
- Performance analysis of novel methods for detecting epistasis.BMC bioinformatics · 2011Article
- Systems biology data analysis methodology in pharmacogenomics.Pharmacogenomics · 2011Review
- Causal graph-based analysis of genome-wide association data in rheumatoid arthritis.Biology direct · 2011Article
- Evaluating de novo locus-disease discoveries in GWAS using the signal-to-noise ratio.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2011Article
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Authors and funding
5 authors.
Funding
Abstract
Genetic epidemiologists strive to determine the genetic profile of diseases. Epistasis is the interaction between two or more genes to affect phenotype. Due to the often non-linearity of the interaction, it is difficult to detect statistical patterns of epistasis. Combinatorial methods for detecting epistasis investigate a subset of combinations of genes without employing a search strategy. Therefore, they do not scale to handling the high-dimensional data found in genome-wide association studies (GWAS). We represent genome-phenome interactions using a Bayesian network rule, which is a specialized Bayesian network. We develop an efficient search algorithm to learn from data a high scoring rule that may contain two or more interacting genes. Our experimental results using synthetic data indicate that this algorithm detects interacting genes as well as a Bayesian network combinatorial method, and it is much faster. Our results also indicate that the algorithm can successfully learn genome-phenome relationships using a real GWAS dataset.
Indexed as
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21346997PMC3041370What OpenQuestion holds
Registered trials
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