SIB Literature Services: RESTful customizable search engines in biomedical literature, enriched with automatically mapped biomedical concepts
Open accessAbstract
Thanks to recent efforts by the text mining community, biocurators have now access to plenty of good tools and Web interfaces for identifying and visualizing biomedical entities in literature. Yet, many of these systems start with a PubMed query, which is limited by strong Boolean constraints. Some semantic search engines exploit entities for Information Retrieval, and/or deliver relevance-based ranked results. Yet, they are not designed for supporting a specific curation workflow, and allow very limited control on the search process. The Swiss Institute of Bioinformatics Literature Services (SIBiLS) provide personalized Information Retrieval in the biological literature. Indeed, SIBiLS allow fully customizable search in semantically enriched contents, based on keywords and/or mapped biomedical entities from a growing set of standardized and legacy vocabularies. The services have been used and favourably evaluated to assist the curation of genes and gene products, by delivering customized literature triage engines to different curation teams. SIBiLS (https://candy.hesge.ch/SIBiLS) are freely accessible via REST APIs and are ready to empower any curation workflow, built on modern technologies scalable with big data: MongoDB and Elasticsearch. They cover MEDLINE and PubMed Central Open Access enriched by nearly 2 billion of mapped biomedical entities, and are daily updated.
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Machine learning and natural language processing methods allow database curators to retrieve and curate more accurately and efficiently relevant literature than traditional keyword-based PubMed search methods.
“showing that machine learning and natural language processing methods (15,16) allow database curators to retrieve and curate more accurately and efficiently relevant literature, than traditional keyword-based PubMed search methods.”
From LitSuggest: a web-based system for literature recommendation and curation using machine learning (2021) · cites this paper as [15]
In Use Cases, Case 2 (page unavailable), SIBiLS retrieves more relevant articles and scores higher on precision than PubMed in a precision-medicine benchmark. Case 1 describes its use by curators for literature triage, but the paper does not establish that curation itself is more accurate or efficient than curation using traditional PubMed searches. The claim extends a retrieval comparison to the broader curation workflow. (LLM, conf=0.85)
Experimental — accuracy not yet measured
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LitSuggest: a web-based system for literature recommendation and curation using machine learning
Allot, A., Lee, K., Chen, Q., Luo, L., & Lu, Z. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab326 · Cited by 61 · see citing works
“Their effectiveness has been demonstrated in multiple publications (12–14), showing that machine learning and natural language processing methods (15,16) allow database curators to retrieve and cu-rate more accurately and efficiently relevant literature, than traditional keyword-based PubMed search…”
Background
- Authors
- Gobeill, J., Caucheteur, D., Michel, P.-A., Mottin, L., Pasche, E., & Ruch, P.
- Affiliations
- SIB Swiss Institute of Bioinformatics (CH)
- HES-SO University of Applied Sciences and Arts Western Switzerland (CH)
- Corresponding author
- Gobeill, Julien
- Ruch, Patrick
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-05-07
- Volume
- 48
- Issue
- W1
- Pages
- W12-W16
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC7319474
- OpenAlex
- W3023596762
- Cited by
- 20
- References
- 36
- Citations by year
- 2026: 5 · 2025: 1 · 2024: 4 · 2023: 4 · 2022: 3 · 2021: 1 · 2020: 1 · 2018: 1
- FWCI
- 0.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ArODES (HES-SO (https://www.hes-so.ch/)) (submittedVersion)
- Funders
- European Commission (676559)
- HES-SO
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- literature search engines, semantic search, gene curation, REST API, MongoDB, Elasticsearch, PubMed, MEDLINE, text mining, information retrieval
- MeSH terms
- MEDLINE, Data Mining, Precision Medicine, Search Engine
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