MOTIVATION: Since early 2020, the coronavirus disease 2019 (COVID-19) pandemic has confronted the biomedical community with an unprecedented challenge. The rapid spread of COVID-19 and ease of transmission seen worldwide is due to increased population flow and international trade. Front-line medical care, treatment research and vaccine development also require rapid and informative interpretation of the literature and COVID-19 data produced around the world, with 177 500 papers published between January 2020 and November 2021, i.e. almost 8500 papers per month. To extract knowledge and enable interoperability across resources, we developed the COVID-19 Vocabulary (COVoc), an application ontology related to the research on this pandemic. The main objective of COVoc development was to enable seamless navigation from biomedical literature to core databases and tools of ELIXIR, a European-wide intergovernmental organization for life sciences. RESULTS: This collaborative work provided data integration into SIB Literature services, an application ontology (COVoc) and a triage service named COVTriage and based on annotation processing to search for COVID-related information across pre-defined aspects with daily updates. Thanks to its interoperability potential, COVoc lends itself to wider applications, hopefully through further connections with other novel COVID-19 ontologies as has been established with Coronavirus Infectious Disease Ontology. AVAILABILITY AND IMPLEMENTATION: The data at https://github.com/EBISPOT/covoc and the service at https://candy.hesge.ch/COVTriage.

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. AVAILABILITY AND IMPLEMENTATION: Variomes is publicly available at https://candy.hesge.ch/Variomes. Source code is freely available at https://github.com/variomes/sibtm-variomes. SynVar is publicly available at https://goldorak.hesge.ch/synvar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The large increase in the size of patent collections has led to the need of efficient search strategies. But the development of advanced text-mining applications dedicated to patents of the biomedical field remains rare, in particular to address the needs of the pharmaceutical & biotech industry, which intensively uses patent libraries for competitive intelligence and drug development. METHODS: We describe here the development of an advanced retrieval engine to search information in patent collections in the field of medicinal chemistry. We investigate and combine different strategies and evaluate their respective impact on the performance of the search engine applied to various search tasks, which covers the putatively most frequent search behaviours of intellectual property officers in medical chemistry: 1) a prior art search task; 2) a technical survey task; and 3) a variant of the technical survey task, sometimes called known-item search task, where a single patent is targeted. RESULTS: The optimal tuning of our engine resulted in a top-precision of 6.76% for the prior art search task, 23.28% for the technical survey task and 46.02% for the variant of the technical survey task. We observed that co-citation boosting was an appropriate strategy to improve prior art search tasks, while IPC classification of queries was improving retrieval effectiveness for technical survey tasks. Surprisingly, the use of the full body of the patent was always detrimental for search effectiveness. It was also observed that normalizing biomedical entities using curated dictionaries had simply no impact on the search tasks we evaluate. The search engine was finally implemented as a web-application within Novartis Pharma. The application is briefly described in the report. CONCLUSIONS: We have presented the development of a search engine dedicated to patent search, based on state of the art methods applied to patent corpora. We have shown that a proper tuning of the system to adapt to the various search tasks clearly increases the effectiveness of the system. We conclude that different search tasks demand different information retrieval engines' settings in order to yield optimal end-user retrieval.

BACKGROUND: The annotation of protein post-translational modifications (PTMs) is an important task of UniProtKB curators and, with continuing improvements in experimental methodology, an ever greater number of articles are being published on this topic. To help curators cope with this growing body of information we have developed a system which extracts information from the scientific literature for the most frequently annotated PTMs in UniProtKB. RESULTS: The procedure uses a pattern-matching and rule-based approach to extract sentences with information on the type and site of modification. A ranked list of protein candidates for the modification is also provided. For PTM extraction, precision varies from 57% to 94%, and recall from 75% to 95%, according to the type of modification. The procedure was used to track new publications on PTMs and to recover potential supporting evidence for phosphorylation sites annotated based on the results of large scale proteomics experiments. CONCLUSIONS: The information retrieval and extraction method we have developed in this study forms the basis of a simple tool for the manual curation of protein post-translational modifications in UniProtKB/Swiss-Prot. Our work demonstrates that even simple text-mining tools can be effectively adapted for database curation tasks, providing that a thorough understanding of the working process and requirements are first obtained. This system can be accessed at http://eagl.unige.ch/PTM/.

BACKGROUND: We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k). RESULTS: We received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively. CONCLUSIONS: By using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible. Using the inferred ground truth we show measures of comparative performance between teams. Finally, by comparing team rankings on gold standard vs. inferred ground truth, we further demonstrate that the inferred ground truth is as effective as the gold standard for detecting good team performance.

Automatic document categorization is an important research problem in Information Science and Natural Language Processing. Many applications, including Word Sense Disambiguation and Information Retrieval in large collections, can benefit from such categorization. This paper focuses on automatic categorization of documents from the biomedical literature into broad discipline-based categories. Two different systems are described and contrasted: CISMeF, which uses rules based on human indexing of the documents by the Medical Subject Headings(®) (MeSH(®)) controlled vocabulary in order to assign metaterms (MTs), and Journal Descriptor Indexing (JDI) based on human categorization of about 4,000 journals and statistical associations between journal descriptors (JDs) and textwords in the documents. We evaluate and compare the performance of these systems against a gold standard of humanly assigned categories for one hundred MEDLINE documents, using six measures selected from trec_eval. The results show that for five of the measures, performance is comparable, and for one measure, JDI is superior. We conclude that these results favor JDI, given the significantly greater intellectual overhead involved in human indexing and maintaining a rule base for mapping MeSH terms to MTs. We also note a JDI method that associates JDs with MeSH indexing rather than textwords, and it may be worthwhile to investigate whether this JDI method (statistical) and CISMeF (rule based) might be combined and then evaluated showing they are complementary to one another.

BACKGROUND: 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. RESULTS: 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%). CONCLUSIONS: Argumentative representation levels and conceptual density estimation using Gene Ontology contents appear complementary for functional annotation in proteomics.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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: http://research.isb-sib.ch/unimed.

MOTIVATION: We report on the development of a generic text categorization system designed to automatically assign biomedical categories to any input text. Unlike usual automatic text categorization systems, which rely on data-intensive models extracted from large sets of training data, our categorizer is largely data-independent. METHODS: In order to evaluate the robustness of our approach we test the system on two different biomedical terminologies: the Medical Subject Headings (MeSH) and the Gene Ontology (GO). Our lightweight categorizer, based on two ranking modules, combines a pattern matcher and a vector space retrieval engine, and uses both stems and linguistically-motivated indexing units. RESULTS AND CONCLUSION: Results show the effectiveness of phrase indexing for both GO and MeSH categorization, but we observe the categorization power of the tool depends on the controlled vocabulary: precision at high ranks ranges from above 90% for MeSH to <20% for GO, establishing a new baseline for categorizers based on retrieval methods.

OBJECTIVE: Develop and analyze results from an image retrieval test collection. METHODS: After participating research groups obtained and assessed results from their systems in the image retrieval task of Cross-Language Evaluation Forum, we assessed the results for common themes and trends. In addition to overall performance, results were analyzed on the basis of topic categories (those most amenable to visual, textual, or mixed approaches) and run categories (those employing queries entered by automated or manual means as well as those using visual, textual, or mixed indexing and retrieval methods). We also assessed results on the different topics and compared the impact of duplicate relevance judgments. RESULTS: A total of 13 research groups participated. Analysis was limited to the best run submitted by each group in each run category. The best results were obtained by systems that combined visual and textual methods. There was substantial variation in performance across topics. Systems employing textual methods were more resilient to visually oriented topics than those using visual methods were to textually oriented topics. The primary performance measure of mean average precision (MAP) was not necessarily associated with other measures, including those possibly more pertinent to real users, such as precision at 10 or 30 images. CONCLUSIONS: We developed a test collection amenable to assessing visual and textual methods for image retrieval. Future work must focus on how varying topic and run types affect retrieval performance. Users' studies also are necessary to determine the best measures for evaluating the efficacy of image retrieval systems.

BACKGROUND: In the context of the BioCreative competition, where training data were very sparse, we investigated two complementary tasks: 1) given a Swiss-Prot triplet, containing a protein, a GO (Gene Ontology) term and a relevant article, extraction of a short passage that justifies the GO category assignment; 2) given a Swiss-Prot pair, containing a protein and a relevant article, automatic assignment of a set of categories. METHODS: Sentence is the basic retrieval unit. Our classifier computes a distance between each sentence and the GO category provided with the Swiss-Prot entry. The Text Categorizer computes a distance between each GO term and the text of the article. Evaluations are reported both based on annotator judgements as established by the competition and based on mean average precision measures computed using a curated sample of Swiss-Prot. RESULTS: Our system achieved the best recall and precision combination both for passage retrieval and text categorization as evaluated by official evaluators. However, text categorization results were far below those in other data-poor text categorization experiments The top proposed term is relevant in less that 20% of cases, while categorization with other biomedical controlled vocabulary, such as the Medical Subject Headings, we achieved more than 90% precision. We also observe that the scoring methods used in our experiments, based on the retrieval status value of our engines, exhibits effective confidence estimation capabilities. CONCLUSION: From a comparative perspective, the combination of retrieval and natural language processing methods we designed, achieved very competitive performances. Largely data-independent, our systems were no less effective that data-intensive approaches. These results suggests that the overall strategy could benefit a large class of information extraction tasks, especially when training data are missing. However, from a user perspective, results were disappointing. Further investigations are needed to design applicable end-user text mining tools for biologists.

We report on the comparison of different strategies for correcting spelling errors resulting in non-existent words. Unlike interactive spelling checkers, where usually only the left context is available, the system we developed takes advantage of the entire context surrounding misspelling. Moreover, unlike traditional systems, based exclusively on a string-to-string edit distance and a word language model, we explore the use of the part-of-speech for selecting candidates. In conclusion, we show that spelling correction improves by extending the context. The best results are obtained when combining a part-of-speech filter with a word language model, and using both the left and right adjacent contexts.