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Full-Text Articles in Physical Sciences and Mathematics
Genetic Studies Of Complex Human Diseases: Characterizing Snp-Disease Associations Using Bayesian Networks, Bing Han, Xue-Wen Chen, Zohreh Talebizadeh, Hua Xu
Genetic Studies Of Complex Human Diseases: Characterizing Snp-Disease Associations Using Bayesian Networks, Bing Han, Xue-Wen Chen, Zohreh Talebizadeh, Hua Xu
Wayne State University Associated BioMed Central Scholarship
Abstract
Background
Detecting epistatic interactions plays a significant role in improving pathogenesis, prevention, diagnosis, and treatment of complex human diseases. Applying machine learning or statistical methods to epistatic interaction detection will encounter some common problems, e.g., very limited number of samples, an extremely high search space, a large number of false positives, and ways to measure the association between disease markers and the phenotype.
Results
To address the problems of computational methods in epistatic interaction detection, we propose a score-based Bayesian network structure learning method, EpiBN, to detect epistatic interactions. We apply the proposed method to both simulated datasets and …
Down-Weighting Overlapping Genes Improves Gene Set Analysis, Adi Tarca, Sorin Draghici, Gaurav Bhatti, Roberto Romero
Down-Weighting Overlapping Genes Improves Gene Set Analysis, Adi Tarca, Sorin Draghici, Gaurav Bhatti, Roberto Romero
Wayne State University Associated BioMed Central Scholarship
Abstract
Background
The identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research. Most gene set analysis methods treat genes equally, regardless how specific they are to a given gene set.
Results
In this work we propose a new gene set analysis method that computes a gene set score as the mean of absolute values of weighted moderated gene t-scores. The gene weights are designed to emphasize the genes appearing in few gene sets, versus genes that appear in many gene sets. We demonstrate the …