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A hybrid framework integrating Random Forest and Logistic Regression is proposed and implemented for genome-wide epistasis study. The two-stage approach first uses random forest model to capture a pool of epistasis-prone single nucleotide polymorphisms (SNPs), followed by using logistic regression to identify the significant pair-wise epistasis SNPs. We tested the proposed framework on data obtained...
Following the availability of whole genome sequence of Mycobacterium tuberculosis (MTB) in public database, the anticipation of its benefits would definitely falls into the contribution towards development of improved vaccine or drug discovery to fight against the fatal disease, tuberculosis (TB). This research aimed to scan the whole genome sequence of MTB by application of bioinformatics approaches...
Gene association analysis of cancer microarray data provides a wealth of information on gene expression patterns and cancer pathways to enhance the identification of potential biomarkers for cancer diagnosis, prognosis, and prediction of therapeutic responsiveness. However, achieving these biological/clinical objectives relies heavily on the functional capabilities and accuracy of the various analytical...
Genome Wide Association (GWA) studies are powerful tools to identify genes involved in common human diseases, and are becoming increasingly important in genetic epidemiology research. However, the statistical approaches behind GWA studies lack capability in taking into account the possible interactions among genetic markers; and true disease variants may be lost in statistical noise due to high threshold...
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