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Articles 1 - 6 of 6
Full-Text Articles in Medicine and Health Sciences
Integration Of Random Forest Classifiers And Deep Convolutional Neural Networks For Classification And Biomolecular Modeling Of Cancer Driver Mutations, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker
Integration Of Random Forest Classifiers And Deep Convolutional Neural Networks For Classification And Biomolecular Modeling Of Cancer Driver Mutations, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker
Mathematics, Physics, and Computer Science Faculty Articles and Research
Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to …
Structure-Functional Prediction And Analysis Of Cancer Mutation Effects In Protein Kinases, Anshuman Dixit, Gennady M. Verkhivker
Structure-Functional Prediction And Analysis Of Cancer Mutation Effects In Protein Kinases, Anshuman Dixit, Gennady M. Verkhivker
Mathematics, Physics, and Computer Science Faculty Articles and Research
A central goal of cancer research is to discover and characterize the functional effects of mutated genes that contribute to tumorigenesis. In this study, we provide a detailed structural classification and analysis of functional dynamics for members of protein kinase families that are known to harbor cancer mutations. We also present a systematic computational analysis that combines sequence and structure-based prediction models to characterize the effect of cancer mutations in protein kinases. We focus on the differential effects of activating point mutations that increase protein kinase activity and kinase-inactivating mutations that decrease activity. Mapping of cancer mutations onto the conformational …
Probing Molecular Mechanisms Of The Hsp90 Chaperone: Biophysical Modeling Identifies Key Regulators Of Functional Dynamics, Anshuman Dixit, Gennady M. Verkhivker
Probing Molecular Mechanisms Of The Hsp90 Chaperone: Biophysical Modeling Identifies Key Regulators Of Functional Dynamics, Anshuman Dixit, Gennady M. Verkhivker
Mathematics, Physics, and Computer Science Faculty Articles and Research
Deciphering functional mechanisms of the Hsp90 chaperone machinery is an important objective in cancer biology aiming to facilitate discovery of targeted anti-cancer therapies. Despite significant advances in understanding structure and function of molecular chaperones, organizing molecular principles that control the relationship between conformational diversity and functional mechanisms of the Hsp90 activity lack a sufficient quantitative characterization. We combined molecular dynamics simulations, principal component analysis, the energy landscape model and structure-functional analysis of Hsp90 regulatory interactions to systematically investigate functional dynamics of the molecular chaperone. This approach has identified a network of conserved regions common to the Hsp90 chaperones that could …
Simulating Molecular Mechanisms Of The Mdm2-Mediated Regulatory Interactions: A Conformational Selection Model Of The Mdm2 Lid Dynamics, Gennady M. Verkhivker
Simulating Molecular Mechanisms Of The Mdm2-Mediated Regulatory Interactions: A Conformational Selection Model Of The Mdm2 Lid Dynamics, Gennady M. Verkhivker
Mathematics, Physics, and Computer Science Faculty Articles and Research
Diversity and complexity of MDM2 mechanisms govern its principal function as the cellular antagonist of the p53 tumor suppressor. Structural and biophysical studies have demonstrated that MDM2 binding could be regulated by the dynamics of a pseudo-substrate lid motif. However, these experiments and subsequent computational studies have produced conflicting mechanistic models of MDM2 function and dynamics. We propose a unifying conformational selection model that can reconcile experimental findings and reveal a fundamental role of the lid as a dynamic regulator of MDM2-mediated binding. In this work, structure, dynamics and energetics of apo-MDM2 are studied as a function of posttranslational modifications …
Modeling Measurement Error In Tumor Characterization Studies, Cyril Rakovski, Daniel J. Weisenberger, Paul Marjoram, Peter W. Laird, Kimberly D. Siegmund
Modeling Measurement Error In Tumor Characterization Studies, Cyril Rakovski, Daniel J. Weisenberger, Paul Marjoram, Peter W. Laird, Kimberly D. Siegmund
Mathematics, Physics, and Computer Science Faculty Articles and Research
Background: Etiologic studies of cancer increasingly use molecular features such as gene expression, DNA methylation and sequence mutation to subclassify the cancer type. In large population-based studies, the tumor tissues available for study are archival specimens that provide variable amounts of amplifiable DNA for molecular analysis. As molecular features measured from small amounts of tumor DNA are inherently noisy, we propose a novel approach to improve statistical efficiency when comparing groups of samples. We illustrate the phenomenon using the MethyLight technology, applying our proposed analysis to compare MLH1 DNA methylation levels in males and females studied in the Colon …
The Energy Landscape Analysis Of Cancer Mutations In Protein Kinases, Anshuman Dixit, Gennady M. Verkhivker
The Energy Landscape Analysis Of Cancer Mutations In Protein Kinases, Anshuman Dixit, Gennady M. Verkhivker
Mathematics, Physics, and Computer Science Faculty Articles and Research
The growing interest in quantifying the molecular basis of protein kinase activation and allosteric regulation by cancer mutations has fueled computational studies of allosteric signaling in protein kinases. In the present study, we combined computer simulations and the energy landscape analysis of protein kinases to characterize the interplay between oncogenic mutations and locally frustrated sites as important catalysts of allostetric kinase activation. While structurally rigid kinase core constitutes a minimally frustrated hub of the catalytic domain, locally frustrated residue clusters, whose interaction networks are not energetically optimized, are prone to dynamic modulation and could enable allosteric conformational transitions. The results …