Mapping proteins to disease terminologies: from UniProt to MeSH

<p>Abstract</p> <p>Background</p> <p>Although the UniProt KnowledgeBase is not a medical-oriented database, it contains information on more than 2,000 human proteins involved in pathologies. However, these annotations are not standardized, which impairs the interoperability between biological and clinical resources. In order to make these data easily accessible to clinical researchers, we have developed a procedure to link diseases described in the UniProtKB/Swiss-Prot entries to the MeSH disease terminology.</p> <p>Results</p> <p>We mapped disease names extracted either from the UniProtKB/Swiss-Prot entry comment lines or from the corresponding OMIM entry to the MeSH. Different methods were assessed on a benchmark set of 200 disease names manually mapped to MeSH terms. The performance of the retained procedure in term of precision and recall was 86% and 64% respectively. Using the same procedure, more than 3,000 disease names in Swiss-Prot were mapped to MeSH with comparable efficiency.</p> <p>Conclusions</p> <p>This study is a first attempt to link proteins in UniProtKB to the medical resources. The indexing we provided will help clinicians and researchers navigate from diseases to genes and from genes to diseases in an efficient way. The mapping is available at: <url>http://research.isb-sib.ch/unimed</url>.</p

Easy retrieval of single amino-acid polymorphisms and phenotype information using SwissVar

Summary: The SwissVar portal provides access to a comprehensive collection of single amino acid polymorphisms and diseases in the UniProtKB/Swiss-Prot database via a unique search engine. In particular, it gives direct access to the newly improved Swiss-Prot variant pages. The key strength of this portal is that it provides a possibility to query for similar diseases, as well as the underlying protein products and the molecular details of each variant. In the context of the recently proposed molecular view on diseases, the SwissVar portal should be in a unique position to provide valuable information for researchers and to advance research in this area. Availability: The SwissVar portal is available at www.expasy.org/swissvar Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction

<p>Abstract</p> <p>Background</p> <p>This paper describes and evaluates a sentence selection engine that extracts a GeneRiF (Gene Reference into Functions) as defined in ENTREZ-Gene based on a MEDLINE record. Inputs for this task include both a gene and a pointer to a MEDLINE reference. In the suggested approach we merge two independent sentence extraction strategies. The first proposed strategy (LASt) uses argumentative features, inspired by discourse-analysis models. The second extraction scheme (GOEx) uses an automatic text categorizer to estimate the density of Gene Ontology categories in every sentence; thus providing a full ranking of all possible candidate GeneRiFs. A combination of the two approaches is proposed, which also aims at reducing the size of the selected segment by filtering out non-content bearing rhetorical phrases.</p> <p>Results</p> <p>Based on the TREC-2003 Genomics collection for GeneRiF identification, the LASt extraction strategy is already competitive (52.78%). When used in a combined approach, the extraction task clearly shows improvement, achieving a Dice score of over 57% (+10%).</p> <p>Conclusions</p> <p>Argumentative representation levels and conceptual density estimation using Gene Ontology contents appear complementary for functional annotation in proteomics.</p

Mapping protein information to disease terminologies

In order to improve the accessibility of genomic and proteomic information to medical researchers, we have developed a procedure to link biological information on proteins involved in diseases to the MeSH and ICD-10 disease terminologies. For this purpose, we took advantage of the manually curated disease annotations in more than 2,000 human protein entries of the UniProt KnowledgeBase. We mapped disease names extracted from the entry comment lines or from the corresponding OMIM entry to the MeSH. The method was assessed on a benchmark set of 200 manually mapped disease comment lines. We obtained a recall of 54% for 91% precision. The same procedure was used to map the more than 3,000 diseases in Swiss-Prot to MeSH with comparable efficiency. Tested on ICD-10, the coverage of the mapped terms was lower, which could be explained by the coarse-grained structure of this terminology for hereditary disease description. The mapping is provided as supplementary material at http://research.isbsib.ch/unimed

BiTeM at CLEF eHealth Evaluation Lab 2016 Task 2 ::Multilingual Information Extraction

BiTeM/SIB Text Mining (http://bitem.hesge.ch/) is a University re-search group carrying over activities in semantic and text analytics applied to health and life sciences. This paper reports on the participation of our team at the CLEF eHealth 2016 evaluation lab. The processing applied to each evaluation corpus (QUAREO and CépiDC) was originally very similar. Our method is based on an Au-tomatic Text Categorization (ATC) system. First, the system is set with a specific input ontology (French UMLS), and ATC assigns a rank list of related concepts to each document received in input. Then, a second module relocates all of the positive matches in the text, and normalizes the extracted entities. For the CépiDC corpus, the system was loaded with the Swiss ICD-10 GM thesaurus. However a late minute data transformation issue forced us to implement an ad hoc solution based on simple pat-tern matching to comply with the constraints of the CépiDC challenge. We obtained an average precision of 62% on the QUAREO entity extraction (over MEDLINE/EMEA texts, and exact/inexact), 48% on normalizing this entities, and 59% on the CépiDC subtask. Enhancing the recall by expanding the coverage of the terminologies could be an interesting approach to improve this system at moderate labour costs

Designing retrieval models to contrast precision-driven ad hoc search vs. recall-driven treatment extraction in precision medicine

The TREC 2019 Precision Medicine Track repeats the general structure and evaluation of the 2018 track. Our team participated in both tasks of the track, relative to scientific abstracts and clinical trials. 40 topics where patient data are given (demographic data, disease, gene and genetic variant) were available for this competition. The aim was to retrieve scientific abstracts and clinical trials of interest regarding a topic, modelling the description of a clinical case. In the first task, we aim at retrieving scientific abstracts introducing some relevant treatments for a given case. Our system is first based on the collection of a large set of abstracts related to a particular case using various strategies such as search with keywords within abstracts, search with normalized entities within annotated abstracts and the linear combination of various queries. We then apply different strategies to re-rank the resulting scientific abstracts set. In particular, we tested two strategies to re-rank the abstracts set in order to have a large variety of treatments returned in the top articles. Almost two thirds of the top-10 returned documents are judged relevant, while nearly a quarter of the relevant treatments is returned in the top-10 abstracts. The second task aims at retrieving some clinical trials for which patients are eligible. Criteria used to determine the eligibility of patients are those found in the topics. Information such as trial location or status of clinical trials, which are important from a patient's point of view, are questionably not used in these topics. Several strategies have been tested, relaxing of constraints (data required or not), expansion of information requests thanks to synonyms or regex, and retrieval status value boosting for some criteria or fields. After judging, for almost half of the topics, a minimum of 50% of the documents retrieved are relevant, up to 90% for 10 of the 38 topics provided. Almost two thirds of the top-10 returned documents are judged relevant, while nearly a quarter of the relevant treatments is returned in the top-10 abstracts. Our best runs achieve highly competitive results depending on the measures, with on average being ranked #2 or #3 according to the official results for the literature task

SIB text mining at TREC precision medicine 2020

TREC 2020 Precision Medicine Track aimed at developing specialized algorithms able to retrieve the best available evidence for a specific cancer treatment. A set of 40 topics representing cases (i.e. a disease, caused by a gene and treated by a drug) were provided. Two assessments were performed: an assessment of the relevance of the documents and an assessment of the ranking of documents regarding the strength of the evidence. Our system collected a set of up to 1000 documents per topic and re-ranked the documents based on several strategies: classification of documents as precision medicine-related, classification of documents as focused on the topic and attribution of a set of evidence-related scores to documents. Our baseline run achieved competitive results (rank #3 for infNDCG according to the official results): more than half of the documents retrieved in the top-10 were judged as relevant regarding the topic. All the tested strategies decreased the performances in the phase-1 assessment, while the evidence-related re-ranking improved performance in the phase-2 assessment

Variomes ::a high recall search engine to support the curation of genomic variants

Motivation : Identification and interpretation of clinically actionable variants is a critical bottleneck. Searching for evidence in the literature is mandatory according to ASCO/AMP/CAP practice guidelines; however, it is both labor-intensive and error-prone. We developed a system to perform triage of publications relevant to support an evidence-based decision. The system is also able to prioritize variants. Our system searches within pre-annotated collections such as MEDLINE and PubMed Central. Results : We assess the search effectiveness of the system using three different experimental settings: literature triage; variant prioritization and comparison of Variomes with LitVar. Almost two-thirds of the publications returned in the top-5 are relevant for clinical decision-support. Our approach enabled identifying 81.8% of clinically actionable variants in the top-3. Variomes retrieves on average +21.3% more articles than LitVar and returns the same number of results or more results than LitVar for 90% of the queries when tested on a set of 803 queries; thus, establishing a new baseline for searching the literature about variants

SIB text mining at TREC 2018 precision medicine track

The TREC 2018 Precision Medicine Track largely repeats the structure and evaluation of the 2017 track. The collection remains identical. Again, our team participated in the both tasks of the track: 1) retrieving scientific abstracts addressing relevant treatments for a given case and 2) retrieving clinical trials for which a patient is eligible. Regarding the retrieval of scientific abstracts, we queried all abstracts concerning one of the entities of the topic (i.e. the disease, the gene or the genetic variant) using various strategies (e.g. search in annotations of the collection, free text search using or not using synonyms, search in the MeSH terms, etc.). Then, for a given topic, the complete set of abstracts was based on the generation of different queries with decreasing levels of specificity. The idea was to start with a very specific query containing gene, disease and variant, from which less specific queries would be inferred. Abstracts were then re-ranked based on different strategies to favor abstracts that we considered more relevant to the given task. In 2017 we tested the use of drug densities to identify abstracts related to treatment. For this year we refined this strategy by giving more weight to drugs related to cancer treatment. Secondly, we used demographic information to favor abstracts concerning patients of the specified age-group and gender, and disfavoring abstracts targeting other age-group or gender patients. For the third strategy we utilized a word-level convolutional neural network to increase the rank of abstracts related to precision medicine. The fourth strategy consisted to expand the query to parent and children diseases. Finally, we tested an exact run which only retrieved abstracts respecting all information given in the topic. Results showed that all strategies but the last one resulted in some improvement of the retrieval power of the engine. As expected, our final run, focusing of precision, resulted in our best results regarding precision at rank 10, while other measures were negatively impacted. Regarding the retrieval of scientific abstracts, we boosted our last year’s approach – which achieved competitive results – with supplementary strategies issued from other participants. Regarding the retrieval of clinical trials, we investigated filtering strategies for managing the condition (disease), and standard information retrieval for managing the gene and genetic variant. The results show that, despite the presence of a structured condition tag in the document, better performances are obtained when relaxing constraints: using synonyms and detecting the diseases in various fields, such as the summary