SIB Text Mining at TREC 2017 Precision Medicine Track
Creators: name: Pasche, Emilie
name: Gobeill, Julien
name: Mottin, Luc
name: Mottaz, Anaïs
name: Teodoro, Douglas
name: Rijen, Josephus SA Van
name: Ruch, Patrick
Publisher: name: F1000 Research Limited
Year: 2017

description: The TREC 2017 Precision Medicine Track aims at providing useful precision medicine-related information to clinicians who have to treat in oncology. It includes two tasks: 1) retrieving scientific abstracts addressing treatment, prevention or prognosis of a disease and 2) retrieving clinical trials for which a patient is eligible. The SIB Text Mining group participated in both tasks. Regarding the retrieval of scientific abstracts, we designed a set of different queries with decreasing levels of specificity. Indeed, it often happens that an abstract of interest does not mention the specified variant, but for instance another variant affecting the gene in a similar manner. Similarly, an abstract about the variant of interest for another diagnosis may still be relevant from a clinical point of view. Therefore, our approach is based on the generation of a set of queries (e.g. disease + gene + variant; disease + gene; gene + variant) – each differentially weighted. Moreover, we apply query expansion strategies. In particular, a SNVs generator, based on the HGVS nomenclature, but able to recognize non-standard formats frequently found in the literature, is used to generate a list of variant synonyms. We thus expect to retrieve a maximum of relevant abstracts. We then apply different strategies to re-rank the abstracts. First, we assume that an abstract with a high frequency of drug names is more probably relevant to support our task. Therefore, we pre-annotated all the collection with DrugBank, thus enabling to retrieve the number of occurrences of drug names per abstract. Second, we assume that the presence of some specific keywords (e.g. “treat”) in the abstract should increase the relevance of the paper, while the presence of specific keywords (e.g. “marker”) should decrease its relevance. Third, we assume that some publications, such as clinical trials, should receive higher relevance for this task. Regarding the retrieval of clinical trials, we investigated different combinations of filtering and information retrieval strategies, mostly based on ontologies. Preliminary analysis of the collection showed that (1) demographic features (age and gender) have an important role to design trials, these entities are stored in a perfectly-structured form and can be handled with strict filtering (2) the collection contains very few mentions of the requested genes and variants (3) diseases are stored in very inconsistent forms, as they are free text entities and can be mentioned in different fields such as condition, keywords, summary, criteria, etc. We assume that identifying clinical trials dealing with the correct disease is the most challenging issue. We thus perform Name Entity Recognition with the NCI thesaurus in order to recognize diseases in topics and in different fields of the clinical trials. Thus, we are able to handle with several issues of free text descriptions, such as synonyms (“Cancer” is “Carcinoma”) and hierarchies (“Colon carcinoma” is a type of “Colorectal carcinoma”). Then, for each topic, we apply different strategies of trials filtering, according to fields where the disease was identified, and hierarchies. Finally, classical information retrieval is performed with genes and variants as queries. The strictest filtering leads to an average of 62 retrieved trials per topic and tends to favour high precision, while the most relaxed filtering leads to an average of 379 retrieved trials per topic and tends to favour high recall.