Some cancers are driven by targetable genomic alterations that dysregulate key pathways
influencing cell growth and survival. These tumors lend themselves to significant, although …

There is an urgent need to elicit and validate highly efficacious targets for combinatorial
intervention from large scale ongoing molecular characterization efforts of tumors. We …

Increasingly complex in-silico modeling approaches offer a way to simultaneously access
cancerous processes at different spatio-temporal scales. High-level models, such as those …

In this perspective we discuss how tumor heterogeneity and therapy resistance necessitate
a focus on more personalized approaches, prompting a shift toward precision medicine. At …

Over the last two decades, cancer researchers have taken the promise offered by the
Human Genome Project and have expanded its capacity to use sequencing to identify the …

It's what we've all searched for at one time or another. That seemingly magical effect when
two plus two appears to add up to more than four. We search for it in datasets. We try to …

Cancer is a complex disease with diverse molecular mechanisms that affect patient
prognosis. Network-based approaches are effective in revealing a holistic picture of cancer …

Computational methods that simulate tumors mathematically to describe cellular and
molecular interactions are emerging as promising tools to simulate the impact of therapy …

The application of data science in cancer research has been boosted by major advances in
three primary areas:(1) Data: diversity, amount, and availability of biomedical data;(2) …

Selecting appropriate cancer models is a key prerequisite for maximizing translational
potential and clinical relevance of in vitro oncology studies. We developed CELLector: an R …