Combination of text-mining algorithms increases the performance

MOTIVATION: Recently, several information extraction systems have been developed to retrieve relevant information out of biomedical text. However, these methods represent individual efforts. In this paper, we show that by combining different algorithms and their outcome, the results improve significantly. For this reason, CONAN has been created, a system which combines different programs and their outcome. Its methods include tagging of gene/protein names, finding interaction and mutation data, tagging of biological concepts and linking to MeSH and Gene Ontology terms. RESULTS: In this paper, we will present data that show that combining different text-mining algorithms significantly improves the results. Not only is CONAN a full-scale approach that will ultimately cover all of PubMed/MEDLINE, we also show that this universality has no effect on quality: our system performs as well as or better than existing systems. AVAILABILITY: The LDD corpus presented is available by request to the author. The system will be available shortly. For information and updates on CONAN please visit http://www.cs.uu.nl/people/rainer/conan.html.     

MeKE: discovering the functions of gene products from biomedical literature via sentence alignment

MOTIVATION: Research on roles of gene products in cells is accumulating and changing rapidly, but most of the results are still reported in text form and are not directly accessible by computers. To expedite the progress of functional bioinformatics, it is, therefore, important to efficiently process large amounts of biomedical literature and transform the knowledge extracted into a structured format usable by biologists and medical researchers. Our aim was to develop an intelligent text-mining system that will extract from biomedical documents knowledge about the functions of gene products and thus facilitate computing with function. RESULTS: We have developed an ontology-based text-mining system to efficiently extract from biomedical literature knowledge about the functions of gene products. We also propose methods of sentence alignment and sentence classification to discover the functions of gene products discussed in digital texts. AVAILABILITY: http://ismp.csie.ncku.edu.tw/~yuhc/meke/     

GeneCruiser: a web service for the annotation of microarray data

SUMMARY: GeneCruiser is a web service allowing users to annotate their genomic data by mapping microarray feature identifiers to gene identifiers from databases, such as UniGene, while providing links to web resources, such as the UCSC Genome Browser. It relies on a regularly updated database that retrieves and indexes the mappings between microarray probes and genomic databases. Genes are identified using the Life Sciences Identifier standard. AVAILABILITY: GeneCruiser is freely available in the following forms: Web service and Web application, http://www.genecruiser.org; GenePattern, GeneCruiser access has been integrated into our microarray analysis platform, GenePattern. http://www.genepattern.org.     

A cascaded approach to normalising gene mentions in biomedical literature

Linking gene and protein names mentioned in the literature to unique identifiers in referent genomic databases is an essential step in accessing and integrating knowledge in the biomedical domain. However, it remains a challenging task due to lexical and terminological variation, and ambiguity of gene name mentions in documents. We present a generic and effective rule-based approach to link gene mentions in the literature to referent genomic databases, where pre-processing of both gene synonyms in the databases and gene mentions in text are first applied. The mapping method employs a cascaded approach, which combines exact, exact-like and token-based approximate matching by using flexible representations of a gene synonym dictionary and gene mentions generated during the pre-processing phase. We also consider multi-gene name mentions and permutation of components in gene names. A systematic evaluation of the suggested methods has identified steps that are beneficial for improving either precision or recall in gene name identification. The results of the experiments on the BioCreAtIvE2 data sets (identification of human gene names) demonstrated that our methods achieved highly encouraging results with F-measure of up to 81.20%.     

Integrating Various Resources for Gene Name Normalization

The recognition and normalization of gene mentions in biomedical literature are crucial steps in biomedical text mining. We present a system for extracting gene names from biomedical literature and normalizing them to gene identifiers in databases. The system consists of four major components: gene name recognition, entity mapping, disambiguation and filtering. The first component is a gene name recognizer based on dictionary matching and semi-supervised learning, which utilizes the co-occurrence information of a large amount of unlabeled MEDLINE abstracts to enhance feature representation of gene named entities. In the stage of entity mapping, we combine the strategies of exact match and approximate match to establish linkage between gene names in the context and the EntrezGene database. For the gene names that map to more than one database identifiers, we develop a disambiguation method based on semantic similarity derived from the Gene Ontology and MEDLINE abstracts. To remove the noise produced in the previous steps, we design a filtering method based on the confidence scores in the dictionary used for NER. The system is able to adjust the trade-off between precision and recall based on the result of filtering. It achieves an F-measure of 83% (precision: 82.5% recall: 83.5%) on BioCreative II Gene Normalization (GN) dataset, which is comparable to the current state-of-the-art.     

BioGRID REST Service, BiogridPlugin2 and BioGRID WebGraph: new tools for access to interaction data at BioGRID

The Biological General Repository for Interaction Datasets (BioGRID) representational state transfer (REST) service allows full URL-based access to curated protein and genetic interaction data at the BioGRID database. Appending URL parameters allows filtering of data by various attributes including gene names and identifiers, PubMed ID and evidence type. We also describe two visualization tools that interface with the REST service, the BiogridPlugin2 for Cytoscape and the BioGRID WebGraph. Availability and implementation: BioGRID data and applications are completely free for commercial and non-commercial use. http://webservice.thebiogrid.org/resources/interactions (REST Service), http://wiki.thebiogrid.org/doku.php/biogridrest(REST Service parameter list and help), http://webservice.thebiogrid.org/resources/application.wadl(REST Service WADL), http://thebiogrid.org/download.php (BiogridPlugin2, v2.1 download), http://wiki.thebiogrid.org/doku.php/biogridplugin2 (BiogridPlugin2 help) and http://tyerslab.bio.ed.ac.uk/tools/BioGRID_webgraph.php(BioGRID WebGraph).     

NetiNeti: discovery of scientific names from text using machine learning methods

ABSTRACT: BACKGROUND: A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information. RESULTS: We present NetiNeti (Name Extraction from Textual Information-Name Extraction for Taxonomic Indexing), a machine learning based approach for recognition of scientific names including the discovery of new species names from text that will also handle misspellings, OCR errors and other variations in names. The system generates candidate names using rules for scientific names and applies probabilistic machine learning methods to classify names based on structural features of candidate names and features derived from their contexts. NetiNeti can also disambiguate scientific names from other names using the contextual information. We evaluated NetiNeti on legacy biodiversity texts and biomedical literature (MEDLINE). NetiNeti performs better (precision = 98.9 % and recall = 70.5 %) compared to a popular dictionary based approach (precision = 97.5 % and recall = 54.3 %) on a 600-page biodiversity book that was manually marked by an annotator. On a small set of PubMed Central's full text articles annotated with scientific names, the precision and recall values are 98.5 % and 96.2 % respectively. NetiNeti found more than 190,000 unique binomial and trinomial names in more than 1,880,000 PubMed records when used on the full MEDLINE database. NetiNeti also successfully identifies almost all of the new species names mentioned within web pages. Additionally, we present the comparison results of various machine learning algorithms on our annotated corpus. Naive Bayes and Maximum Entropy with Generalized Iterative Scaling (GIS) parameter estimation are the top two performing algorithms. CONCLUSIONS: We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org.     

SR4GN: a species recognition software tool for gene normalization

As suggested in recent studies, species recognition and disambiguation is one of the most critical and challenging steps in many downstream text-mining applications such as the gene normalization task and protein-protein interaction extraction. We report SR4GN: an open source tool for species recognition and disambiguation in biomedical text. In addition to the species detection function in existing tools, SR4GN is optimized for the Gene Normalization task. As such it is developed to link detected species with corresponding gene mentions in a document. SR4GN achieves 85.42% in accuracy and compares favorably to the other state-of-the-art techniques in benchmark experiments. Finally, SR4GN is implemented as a standalone software tool, thus making it convenient and robust for use in many text-mining applications. SR4GN can be downloaded at: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/downloads/SR4GN.     

DAtA: database of Arabidopsis thaliana annotation

The Database of Arabidopsis thaliana Annotation (D At A) was created to enable easy access to and analysis of all the Arabidopsis genome project annotation. The database was constructed using the completed A.thaliana genomic sequence data currently in GenBank. An automated annotation process was used to predict coding sequences for GenBank records that do not include annotation. D At A also contains protein motifs and protein similarities derived from searches of the proteins in D At A with motif databases and the non-redundant protein database. The database is routinely updated to include new GenBank submissions for Arabidopsis genomic sequences and new Blast and protein motif search results. A web interface to D At A allows coding sequences to be searched by name, comment, blast similarity or motif field. In addition, browse options present lists of either all the protein names or identified motifs present in the sequenced A.thaliana genome. The database can be accessed at http://baggage. stanford.edu/group/arabprotein/     

Large-Scale Event Extraction from Literature with Multi-Level Gene Normalization

Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons – Attribution – Share Alike (CC BY-SA) license.     

Textpresso: an ontology-based information retrieval and extraction system for biological literature

We have developed Textpresso, a new text-mining system for scientific literature whose capabilities go far beyond those of a simple keyword search engine. Textpresso's two major elements are a collection of the full text of scientific articles split into individual sentences, and the implementation of categories of terms for which a database of articles and individual sentences can be searched. The categories are classes of biological concepts (e.g., gene, allele, cell or cell group, phenotype, etc.) and classes that relate two objects (e.g., association, regulation, etc.) or describe one (e.g., biological process, etc.). Together they form a catalog of types of objects and concepts called an ontology. After this ontology is populated with terms, the whole corpus of articles and abstracts is marked up to identify terms of these categories. The current ontology comprises 33 categories of terms. A search engine enables the user to search for one or a combination of these tags and/or keywords within a sentence or document, and as the ontology allows word meaning to be queried, it is possible to formulate semantic queries. Full text access increases recall of biological data types from 45% to 95%. Extraction of particular biological facts, such as gene-gene interactions, can be accelerated significantly by ontologies, with Textpresso automatically performing nearly as well as expert curators to identify sentences; in searches for two uniquely named genes and an interaction term, the ontology confers a 3-fold increase of search efficiency. Textpresso currently focuses on Caenorhabditis elegans literature, with 3,800 full text articles and 16,000 abstracts. The lexicon of the ontology contains 14,500 entries, each of which includes all versions of a specific word or phrase, and it includes all categories of the Gene Ontology database. Textpresso is a useful curation tool, as well as search engine for researchers, and can readily be extended to other organism-specific corpora of text. Textpresso can be accessed at http://www.textpresso.org or via WormBase at http://www.wormbase.org.     

Gene name ambiguity of eukaryotic nomenclatures

MOTIVATION: With more and more scientific literature published online, the effective management and reuse of this knowledge has become problematic. Natural language processing (NLP) may be a potential solution by extracting, structuring and organizing biomedical information in online literature in a timely manner. One essential task is to recognize and identify genomic entities in text. 'Recognition' can be accomplished using pattern matching and machine learning. But for 'identification' these techniques are not adequate. In order to identify genomic entities, NLP needs a comprehensive resource that specifies and classifies genomic entities as they occur in text and that associates them with normalized terms and also unique identifiers so that the extracted entities are well defined. Online organism databases are an excellent resource to create such a lexical resource. However, gene name ambiguity is a serious problem because it affects the appropriate identification of gene entities. In this paper, we explore the extent of the problem and suggest ways to address it. RESULTS: We obtained gene information from 21 organisms and quantified naming ambiguities within species, across species, with English words and with medical terms. When the case (of letters) was retained, official symbols displayed negligible intra-species ambiguity (0.02%) and modest ambiguities with general English words (0.57%) and medical terms (1.01%). In contrast, the across-species ambiguity was high (14.20%). The inclusion of gene synonyms increased intra-species ambiguity substantially and full names contributed greatly to gene-medical-term ambiguity. A comprehensive lexical resource that covers gene information for the 21 organisms was then created and used to identify gene names by using a straightforward string matching program to process 45,000 abstracts associated with the mouse model organism while ignoring case and gene names that were also English words. We found that 85.1% of correctly retrieved mouse genes were ambiguous with other gene names. When gene names that were also English words were included, 233% additional 'gene' instances were retrieved, most of which were false positives. We also found that authors prefer to use synonyms (74.7%) to official symbols (17.7%) or full names (7.6%) in their publications. CONTACT: lifeng.chen@dbmi.columbia.edu     

The Booly aliasing resource: a database of grouped biological identifiers

Redundancy among sequence identifiers is a recurring problem in bioinformatics. Here, we present a rapid and efficient method of fingerprinting identifiers to ascertain whether two or more aliases are identical. A number of tools and approaches have been developed to resolve differing names for the same genes and proteins, however, these methods each have their own limitations associated with their various goals. We have taken a different approach to the aliasing problem by simplifying the way aliases are stored and curated with the objective of simultaneously achieving speed and flexibility. Our approach (Booly-hashing) is to link identifiers with their corresponding hash keys derived from unique fingerprints such as gene or protein sequences. This tool has proven invaluable for designing a new data integration platform known as Booly, and has wide applicability to situations in which a dedicated efficient aliasing system is required. Compared with other aliasing techniques, Booly-hashing methodology provides 1) reduced run time complexity, 2) increased flexibility (aliasing of other data types, e.g. pharmaceutical drugs), 3) no required assumptions regarding gene clusters or hierarchies, and 4) simplicity in data addition, updating, and maintenance. The new Booly-hashing aliasing model has been incorporated as a central component of the Booly data integration platform we have recently developed and shoud be broadly applicable to other situations in which an efficient streamlined aliasing systems is required. This aliasing tool and database, which allows users to quickly group the same genes and proteins together can be accessed at: http://booly.ucsd.edu/alias. AVAILABILITY: The database is available for free at http://booly.ucsd.edu/alias.     

Identifying the 3'-terminal exon in human DNA.

MOTIVATION: We present JTEF, a new program for finding 3' terminal exons in human DNA sequences. This program is based on quadratic discriminant analysis, a standard non-linear statistical pattern recognition method. The quadratic discriminant functions used for building the algorithm were trained on a set of 3' terminal exons of type 3tuexon (those containing the true STOP codon). RESULTS: We showed that the average predictive accuracy of JTEF is higher than the presently available best programs (GenScan and Genemark.hmm) based on a test set of 65 human DNA sequences with 121 genes. In particular JTEF performs well on larger genomic contigs containing multiple genes and significant amounts of intergenic DNA. It will become a valuable tool for genome annotation and gene functional studies. AVAILABILITY: JTEF is available free for academic users on request from ftp://cshl.org/pub/science/mzhanglab/JTEF and will be made available through the World Wide Web (http://argon.cshl.org/).