Other Computer Sciences Commons^™

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 …

Chipathlon: A Competitive Assessment For Gene Regulation Tools., Avi Knecht, Adam Caprez, Istvan Ladunga

UCARE Research Products

When gene regulation of the cell cycle malfunctions, it frequently causes cancer.

Adult, differentiated cells can be reprogrammed to induced pluripotent stem cell; which can then be reprogrammed to heart muscle, skin, etc, to repair damaged tissue (to limited extent in clinical practice).

ChIPathlon: Evaluate the performance of all transcription factor mapping (peak calling) methods. To this end, we will develop a scalable and easy to use super computing pipeline to stage data, compare many different peak calling and differential binding site tools, and store all results into a single database.