Combining global and local semantic contexts for improving biomedical information retrieval

Présenté lors de l'European Conference on Information Retrieval 2011International audienceIn the context of biomedical information retrieval (IR), this paper explores the relationship between the document's global context and the query's local context in an attempt to overcome the term mismatch problem between the user query and documents in the collection. Most solutions to this problem have been focused on expanding the query by discovering its context, either \textit{global} or \textit{local}. In a global strategy, all documents in the collection are used to examine word occurrences and relationships in the corpus as a whole, and use this information to expand the original query. In a local strategy, the top-ranked documents retrieved for a given query are examined to determine terms for query expansion. We propose to combine the document's global context and the query's local context in an attempt to increase the term overlap between the user query and documents in the collection via document expansion (DE) and query expansion (QE). The DE technique is based on a statistical method (IR-based) to extract the most appropriate concepts (global context) from each document. The QE technique is based on a blind feedback approach using the top-ranked documents (local context) obtained in the first retrieval stage. A comparative experiment on the TREC 2004 Genomics collection demonstrates that the combination of the document's global context and the query's local context shows a significant improvement over the baseline. The MAP is significantly raised from 0.4097 to 0.4532 with a significant improvement rate of +10.62\% over the baseline. The IR performance of the combined method in terms of MAP is also superior to official runs participated in TREC 2004 Genomics and is comparable to the performance of the best run (0.4075)

Automatic medical encoding with SNOMED categories

BACKGROUND: In this paper, we describe the design and preliminary evaluation of a new type of tools to speed up the encoding of episodes of care using the SNOMED CT terminology. METHODS: The proposed system can be used either as a search tool to browse the terminology or as a categorization tool to support automatic annotation of textual contents with SNOMED concepts. The general strategy is similar for both tools and is based on the fusion of two complementary retrieval strategies with thesaural resources. The first classification module uses a traditional vector-space retrieval engine which has been fine-tuned for the task, while the second classifier is based on regular variations of the term list. For evaluating the system, we use a sample of MEDLINE. SNOMED CT categories have been restricted to Medical Subject Headings (MeSH) using the SNOMED-MeSH mapping provided by the UMLS (version 2006). RESULTS: Consistent with previous investigations applied on biomedical terminologies, our results show that performances of the hybrid system are significantly improved as compared to each single module. For top returned concepts, a precision at high ranks (P0) of more than 80% is observed. In addition, a manual and qualitative evaluation on a dozen of MEDLINE abstracts suggests that SNOMED CT could represent an improvement compared to existing medical terminologies such as MeSH. CONCLUSION: Although the precision of the SNOMED categorizer seems sufficient to help professional encoders, it is concluded that clinical benchmarks as well as usability studies are needed to assess the impact of our SNOMED encoding method in real settings. AVAILABILITIES : The system is available for research purposes on: http://eagl.unige.ch/SNOCat

The COMBREX Project: Design, Methodology, and Initial Results

© 2013 Brian P. et al.Prior to the “genomic era,” when the acquisition of DNA sequence involved significant labor and expense, the sequencing of genes was strongly linked to the experimental characterization of their products. Sequencing at that time directly resulted from the need to understand an experimentally determined phenotype or biochemical activity. Now that DNA sequencing has become orders of magnitude faster and less expensive, focus has shifted to sequencing entire genomes. Since biochemistry and genetics have not, by and large, enjoyed the same improvement of scale, public sequence repositories now predominantly contain putative protein sequences for which there is no direct experimental evidence of function. Computational approaches attempt to leverage evidence associated with the ever-smaller fraction of experimentally analyzed proteins to predict function for these putative proteins. Maximizing our understanding of function over the universe of proteins in toto requires not only robust computational methods of inference but also a judicious allocation of experimental resources, focusing on proteins whose experimental characterization will maximize the number and accuracy of follow-on predictions.COMBREX is funded by a GO grant from the National Institute of General Medical Sciences (NIGMS) (1RC2GM092602-01).Peer Reviewe

DisProt: intrinsic protein disorder annotation in 2020

The Database of Protein Disorder (DisProt, URL: https://disprot.org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies. The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved. The new annotation interface has made the curation process faster and more effective. We recently showed that new DisProt annotations can be effectively used to train and validate disorder predictors. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the ‘dark’ proteome