Term Mapping Using Matrix Operations

We believe that gene name identification is a modular process involving term recognition, classification and mapping. This work\u27s focus is on gene name mapping, and we assume that names are already recognized and classified. We use a combination of two methods to map recognized entities to their appropriate gene identifiers (Entrez GeneIDs): the Trigram Method, and the Network Method. Both methods require preprocessing, using resources from Entrez Gene, to construct a set of method-specific matrices. We first address lexical variation by transforming gene names into their unique "trigrams" (groups of three alphanumeric characters), and perform trigram matching against the preprocessed gene dictionary. For ambiguous gene names, we additionally perform a contextual analysis of the abstract that contains the recognized entity. We have formalized our method as a sequence of matrix manipulations, allowing for a fast and coherent implementation of the algorithm. In this talk, we also show how gene name identification, and text mining in general, can play a critical role in translational medicine. We demonstrate how term identification is useful for establishing a biobibliometric distance between genes and psychiatric disorders

Mining Images in Biomedical Publications: Detection and Analysis of Gel Diagrams

Authors of biomedical publications use gel images to report experimental results such as protein-protein interactions or protein expressions under different conditions. Gel images offer a concise way to communicate such findings, not all of which need to be explicitly discussed in the article text. This fact together with the abundance of gel images and their shared common patterns makes them prime candidates for automated image mining and parsing. We introduce an approach for the detection of gel images, and present a workflow to analyze them. We are able to detect gel segments and panels at high accuracy, and present preliminary results for the identification of gene names in these images. While we cannot provide a complete solution at this point, we present evidence that this kind of image mining is feasible.Comment: arXiv admin note: substantial text overlap with arXiv:1209.148

Minimotif Miner, a tool for investigating protein function

In addition to large domains, many short motifs mediate functional post-translational modification of proteins as well as protein-protein interactions and protein trafficking functions. We have constructed a motif database comprising 312 unique motifs and a web-based tool for identifying motifs in proteins. Functional motifs predicted by MnM can be ranked by several approaches, and we validated these scores by analyzing thousands of confirmed examples and by confirming prediction of previously unidentified 14-3-3 motifs in EFF-1

Introducing meta-services for biomedical information extraction

We introduce the first meta-service for information extraction in molecular biology, the BioCreative MetaServer (BCMS; http://bcms.bioinfo.cnio.es/). This prototype platform is a joint effort of 13 research groups and provides automatically generated annotations for PubMed/Medline abstracts. Annotation types cover gene names, gene IDs, species, and protein-protein interactions. The annotations are distributed by the meta-server in both human and machine readable formats (HTML/XML). This service is intended to be used by biomedical researchers and database annotators, and in biomedical language processing. The platform allows direct comparison, unified access, and result aggregation of the annotations