Background: Diagnosis and treatment decisions in cancer increasingly depend on a detailed analysis of the\nmutational status of a patientâ??s genome. This analysis relies on previously published information regarding the\nassociation of variations to disease progression and possible interventions. Clinicians to a large degree use biomedical\nsearch engines to obtain such information; however, the vast majority of scientific publications focus on basic science\nand have no direct clinical impact. We develop the Variant-Information Search Tool (VIST), a search engine designed\nfor the targeted search of clinically relevant publications given an oncological mutation profile.\nResults: VIST indexes all PubMed abstracts and content from ClinicalTrials.gov. It applies advanced text mining to\nidentify mentions of genes, variants and drugs and uses machine learning based scoring to judge the clinical\nrelevance of indexed abstracts. Its functionality is available through a fast and intuitive web interface. We perform\nseveral evaluations, showing that VISTâ??s ranking is superior to that of PubMed or a pure vector space model with\nregard to the clinical relevance of a documentâ??s content.\nConclusion: Different user groups search repositories of scientific publications with different intentions. This diversity\nis not adequately reflected in the standard search engines, often leading to poor performance in specialized settings.\nWe develop a search engine for the specific case of finding documents that are clinically relevant in the course of\ncancer treatment. We believe that the architecture of our engine, heavily relying on machine learning algorithms, can\nalso act as a blueprint for search engines in other, equally specific domains.
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