Johanna Mazur

TL;DR: Standard, data-driven feature selection approaches to feature selection driven by prior knowledge of drug targets, target pathways, and gene expression signatures are compared, finding small feature sets selected using prior knowledge are more predictive for drugs targeting specific genes and pathways, while models with wider feature sets perform better for drugs affecting general cellular mechanisms.

TL;DR: In this article, a probabilistic model for joint inference of the evolutionary tree on copy number events and copy number calling is proposed, which employs an efficient MCMC procedure to search the space of possible model structures and parameters and utilizes both perbin and per-breakpoint data.

TL;DR: It is found that TP53 mutations decrease sensitivity to BRAF inhibitors in BRAF-mutated cell lines and patient tumors, suggesting a therapeutic benefit of combining inhibition of oncogenic BRAF with reactivation of the tumor suppressor TP53.

TL;DR: In this paper, the authors apply a methodology that utilizes the previously developed concept of coherent expression of genes in signatures to identify translatable signatures before scoring their activity in single tumors and present a web interface (www.rosettasx.com) that applies their methodology to expression data from the Cancer Cell Line Encyclopaedia and The Cancer Genome Atlas.

TL;DR: Based on GDSC drug data, it is found that feature selection driven by prior knowledge tends to yield better results for drugs targeting specific genes and pathways, while models with the genome-wide features perform better for drugs affecting general mechanisms such as metabolism and DNA replication.