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Clinical trials investigating drugs that target specific genetic alterations in tumors are important for promoting personalized cancer therapy. The goal of this project is to create a knowledge base of cancer treatment trials with annotations about genetic alterations from ClinicalTrials.gov.

As targeted cancer therapies and molecular profiling become widespread, the era of "precision oncology" is at hand. However, cancer genomes are complex, making mutation-specific outcomes difficult to track. We created a proof-of-principle, CUSTOM-SEQ: Continuously Updating System for Tracking Outcome by Mutation, to Support Evidence-based Querying, to automatically calculate and display mutation-specific survival statistics from electronic health record data.

"Precision medicine" (PM) requires researchers to identify actionable genetic risks and for clinicians to interpret genetic testing results to patients. Whether PM will equally benefit all populations or exacerbate existing disparities is uncertain.

Cancer has been extensively characterized on the basis of genomics. The integration of genetic information about cancers with data on how the cancers respond to target based therapy to help to optimum cancer treatment.

Genomic profiling information is frequently available to oncologists, enabling targeted cancer therapy. Because clinically relevant information is rapidly emerging in the literature and elsewhere, there is a need for informatics technologies to support targeted therapies. To this end, we have developed a system for Automated Identification of Molecular Effects of Drugs, to help biomedical scientists curate this literature to facilitate decision support.

Precision medicine approaches disease treatment and prevention by taking patients' individual variability in genes, environment, and lifestyle into account. Although the ideas underlying precision medicine are not new, opportunities for its more widespread use in practice have been enhanced by the development of large-scale databases, new methods for categorizing and representing patients, and computational tools for analyzing large datasets. New research methods may create uncertainty for both healthcare professionals and [...]