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Articles 1 - 3 of 3
Full-Text Articles in Life Sciences
Multigene Signatures Of Responses To Chemotherapy Derived By Biochemically-Inspired Machine Learning., Peter K. Rogan
Multigene Signatures Of Responses To Chemotherapy Derived By Biochemically-Inspired Machine Learning., Peter K. Rogan
Biochemistry Publications
Pharmacogenomic responses to chemotherapy drugs can be modeled by supervised machine learning of expression and copy number of relevant gene combinations. Such biochemical evidence can form the basis of derived gene signatures using cell line data, which can subsequently be examined in patients that have been treated with the same drugs. These gene signatures typically contain elements of multiple biochemical pathways which together comprise multiple origins of drug resistance or sensitivity. The signatures can capture variation in these responses to the same drug among different patients.
Radiation Dose Estimation By Completely Automated Interpretation Of The Dicentric Chromosome Assay, Peter Rogan, Yanxin Li, Ben Shirley, Ruth Wilkins, Farrah Norton, Joan Knoll
Radiation Dose Estimation By Completely Automated Interpretation Of The Dicentric Chromosome Assay, Peter Rogan, Yanxin Li, Ben Shirley, Ruth Wilkins, Farrah Norton, Joan Knoll
Biochemistry Publications
Accuracy of the automated dicentric chromosome (DC) assay relies on metaphase image selection. This study validates a software framework to find the best image selection models that mitigate inter-sample variability. Evaluation methods to determine model quality include the Poisson goodness-of-fit of DC distributions for each sample, residuals after calibration curve fitting and leave-one-out dose estimation errors. The process iteratively searches a pool of selection model candidates by modifying statistical and filter cut-offs to rank the best candidates according to their respective evaluation scores. Evaluation scores minimize the sum of squared errors relative to the actual radiation dose of the calibration …
Transcription Factor Binding Site Clusters Identify Target Genes With Similar Tissue-Wide Expression And Buffer Against Mutations., Peter Rogan, Ruipeng Lu
Transcription Factor Binding Site Clusters Identify Target Genes With Similar Tissue-Wide Expression And Buffer Against Mutations., Peter Rogan, Ruipeng Lu
Biochemistry Publications
Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML). Methods: Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were …