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Biochemistry, Biophysics, and Structural Biology Commons™
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- Machine learning (3)
- Biochemical pathways (2)
- Gene signatures (2)
- Information theory (2)
- Mutation (2)
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- Systems biology (2)
- Tyrosine kinase inhibitors (2)
- Binding Sites (1)
- Binding sites (1)
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- Oligonucleotide Array Sequence Analysis (1)
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- Polymorphism, Single Nucleotide (1)
- Position-Specific Scoring Matrices (1)
- Position-specific scoring matrices (1)
- Protein Binding (1)
- Reproducibility of Results (1)
Articles 1 - 5 of 5
Full-Text Articles in Biochemistry, Biophysics, and Structural Biology
Pathway‐Extended Gene Expression Signatures Integrate Novel Biomarkers That Improve Predictions Of Patient Responses To Kinase Inhibitors, Ashis Bagchee‐Clark, Eliseos J. Mucaki, Tyson Whitehead, Peter Rogan
Pathway‐Extended Gene Expression Signatures Integrate Novel Biomarkers That Improve Predictions Of Patient Responses To Kinase Inhibitors, Ashis Bagchee‐Clark, Eliseos J. Mucaki, Tyson Whitehead, Peter Rogan
Biochemistry Publications
Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning to derive multi‐gene expression signatures that predict individual patient responses to specific tyrosine kinase inhibitors, including erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Support vector machine (SVM) learning was used to train mathematical models that distinguished sensitivity from resistance to these drugs using a novel systems biology‐based approach. This began with expression of genes previously implicated in specific drug responses, then expanded to evaluate genes whose products were related through biochemical pathways and interactions. Optimal pathway‐extended SVMs predicted responses in …
Pathway-Extended Gene Expression Signatures Integrate Novel Biomarkers That Improve Predictions Of Patient Responses To Kinase Inhibitors, Ashis Jem Bagchee-Clark, Eliseos J. Mucaki, Tyson Whitehead, Peter Rogan
Pathway-Extended Gene Expression Signatures Integrate Novel Biomarkers That Improve Predictions Of Patient Responses To Kinase Inhibitors, Ashis Jem Bagchee-Clark, Eliseos J. Mucaki, Tyson Whitehead, Peter Rogan
Biochemistry Publications
No abstract provided.
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 …
Discovery And Validation Of Information Theory-Based Transcription Factor And Cofactor Binding Site Motifs., Ruipeng Lu, Eliseos J Mucaki, Peter K Rogan
Discovery And Validation Of Information Theory-Based Transcription Factor And Cofactor Binding Site Motifs., Ruipeng Lu, Eliseos J Mucaki, Peter K Rogan
Biochemistry Publications
Data from ChIP-seq experiments can derive the genome-wide binding specificities of transcription factors (TFs) and other regulatory proteins. We analyzed 765 ENCODE ChIP-seq peak datasets of 207 human TFs with a novel motif discovery pipeline based on recursive, thresholded entropy minimization. This approach, while obviating the need to compensate for skewed nucleotide composition, distinguishes true binding motifs from noise, quantifies the strengths of individual binding sites based on computed affinity and detects adjacent cofactor binding sites that coordinate with the targets of primary, immunoprecipitated TFs. We obtained contiguous and bipartite information theory-based position weight matrices (iPWMs) for 93 sequence-specific TFs, …
Validation Of Predicted Mrna Splicing Mutations Using High-Throughput Transcriptome Data, Coby Viner, Stephanie Dorman, Ben Shirley, Peter Rogan
Validation Of Predicted Mrna Splicing Mutations Using High-Throughput Transcriptome Data, Coby Viner, Stephanie Dorman, Ben Shirley, Peter Rogan
Biochemistry Publications
Interpretation of variants present in complete genomes or exomes reveals numerous sequence changes, only a fraction of which are likely to be pathogenic. Mutations have been traditionally inferred from allele frequencies and inheritance patterns in such data. Variants predicted to alter mRNA splicing can be validated by manual inspection of transcriptome sequencing data, however this approach is intractable for large datasets. These abnormal mRNA splicing patterns are characterized by reads demonstrating either exon skipping, cryptic splice site use, and high levels of intron inclusion, or combinations of these properties. We present, Veridical, an in silico method for the automatic validation …