Biology subjects

Snow, O.

Publications and source records attributed to Snow, O..

2 recordsLinked to original sources

bioRxiv2019

BDKANN - Biological Domain Knowledge-based Artificial Neural Network for drug response prediction

MotivationOne of the main goals of precision oncology is to predict the response of a patient to a given cancer treatment based on their genomic profile. Although current models for drug response prediction are becoming more accurate, they are also black boxes and cannot explain their predictions, which is of particular importance in cancer treatment. Many models also do not leverage prior biological knowledge, such as the hierarchical information on how proteins form complexes and act together in pathways. ResultsIn this work, we use this prior biological knowledge to form the architecture of a deep neural network to predict cancer drug response from cell line gene expression data. We find that our approach not only has a low prediction error compared to baseline models but also allows meaningful interpretation of the network. These interpretations can both explain predictions made and discover novel connections in the biological knowledge that may lead to new hypotheses about mechanisms of drug action. AvailabilityCode at https://github.com/osnow/BDKANN Supplementary informationIncluded with submission

bioinformatics↗

bioRxiv2019

Uncovering the subtype-specific temporal order of cancer pathway dysregulation

Cancer is driven by genetic mutations that dysregulate pathways important for proper cell function. Therefore, discovering these cancer pathways and their dysregulation order is key to understanding and treating cancer. However, the heterogeneity of mutations between different individuals makes this challenging and requires that cancer progression is studied in a subtype-specific way. To address this challenge, we provide a mathematical model, called Subtype-specific Pathway Linear Progression Model (SPM), that simultaneously captures cancer subtypes and pathways and order of dysregulation of the pathways within each subtype. Experiments with synthetic data indicate the robustness of SPM to problem specifics including noise compared to an existing method. Moreover, experimental results on glioblastoma multiforme and colorectal adenocarcinoma show the consistency of SPMs results with the existing knowledge and its superiority to an existing method in certain cases. The implementation of our method is available at https://github.com/Dalton386/SPM.

bioinformatics↗