PaperMaker: validation of biomedical scientific publications.

MOTIVATION: The automatic analysis of scientific literature can support authors in writing their manuscripts. IMPLEMENTATION: PaperMaker is a novel IT solution that receives a scientific manuscript via a Web interface, automatically analyses the publication, evaluates consistency parameters and interactively delivers feedback to the author. It analyses the proper use of acronyms and their definitions, and the use of specialized terminology. It provides Gene Ontology (GO) and Medline Subject Headings (MeSH) categorization of text passages, the retrieval of relevant publications from public scientific literature repositories, and the identification of missing or unused references. RESULT: The author receives a summary of findings, the manuscript in its corrected form and a digital abstract containing the GO and MeSH annotations in the NLM/PubMed format. AVAILABILITY: http://www.ebi.ac.uk/Rebholz-srv/PaperMaker.

Combining evidence, specificity, and proximity towards the normalization of Gene Ontology terms in text.

Structured information provided by manual annotation of proteins with Gene Ontology concepts represents a high-quality reliable data source for the research community. However, a limited scope of proteins is annotated due to the amount of human resources required to fully annotate each individual gene product from the literature. We introduce a novel method for automatic identification of GO terms in natural language text. The method takes into consideration several features: (1) the evidence for a GO term given by the words occurring in text, (2) the proximity between the words, and (3) the specificity of the GO terms based on their information content. The method has been evaluated on the BioCreAtIvE corpus and has been compared to current state of the art methods. The precision reached 0.34 at a recall of 0.34 for the identified terms at rank 1. In our analysis, we observe that the identification of GO terms in the "cellular component" subbranch of GO is more accurate than for terms from the other two subbranches. This observation is explained by the average number of words forming the terminology over the different subbranches.

Medical informatics and bioinformatics: a bibliometric study.

This paper reports on an analysis of the bioinformatics and medical informatics literature with the objective to identify upcoming trends that are shared among both research fields to derive benefits from potential collaborative initiatives for their future. Our results present the main characteristics of the two fields and show that these domains are still relatively separated.

Resolving abbreviations to their senses in Medline.

MOTIVATION: Biological literature contains many abbreviations with one particular sense in each document. However, most abbreviations do not have a unique sense across the literature. Furthermore, many documents do not contain the long forms of the abbreviations. Resolving an abbreviation in a document consists of retrieving its sense in use. Abbreviation resolution improves accuracy of document retrieval engines and of information extraction systems. RESULTS: We combine an automatic analysis of Medline abstracts and linguistic methods to build a dictionary of abbreviation/sense pairs. The dictionary is used for the resolution of abbreviations occurring with their long forms. Ambiguous global abbreviations are resolved using support vector machines that have been trained on the context of each instance of the abbreviation/sense pairs, previously extracted for the dictionary set-up. The system disambiguates abbreviations with a precision of 98.9% for a recall of 98.2% (98.5% accuracy). This performance is superior in comparison with previously reported research work. AVAILABILITY: The abbreviation resolution module is available at http://www.ebi.ac.uk/Rebholz/software.html.