Computational models for clinical drug response prediction: aligning transcriptomic data of patients and pre-clinical models

Extensive efforts in cancer research over the past decades have markedly improved diagnosis and treatments, leading to better outcomes for cancer patients. Paradoxically, however, these discoveries have begun to shed light on a level of complexity that rules out the emergence of a universal cancer treatment. As any tumor is now known to be...

ELISL: early-late integrated synthetic lethality prediction in cancer

Motivation<br/><br/>Anti-cancer therapies based on synthetic lethality (SL) exploit tumour vulnerabilities for treatment with reduced side effects, by targeting a gene that is jointly essential with another whose function is lost. Computational prediction is key to expedite SL screening, yet existing methods are vulnerable to prevalent selection...

Finding cancer genes in copy number data and insertional mutagenesis data

Cancer is a genetic disease. Step-wise alteration of genes that have a normal function in the cell can lead to the transformation of a healthy cell into a malignant cancer cell. Cancer genes provide several traits to the cell that allow it to become malignant. These traits have been researched for many years, and currently one knows quite well...