Information Retrieval for Precision Oncology

Abstract

Diagnosis and treatment decisions in cancer increasingly depend on a detailed analysis of the mutational status of a patient’s genome. This analysis relies on previously published information regarding the association of variations to disease progression and possible interventions. Clinicians to a large degree use biomedical search engines to obtain such information; however, the vast majority of search results in the common search engines focuses on basic science and is clinically irrelevant. We developed the Variant-Information Search Tool, a search engine designed for the targeted search of clinically relevant publications given a mutation profile. VIST indexes all PubMed abstracts, applies advanced text mining to identify mentions of genes and variants and uses machine-learning based scoring to judge the relevancy of documents. Its functionality is available through a fast and intuitive web interface. We also performed a comparative evaluation, showing that VIST’s ranking is superior to that of PubMed or vector space models.