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PubMed2004

Inferring pathways from gene lists using a literature-derived network of biological relationships.

MOTIVATION: A number of omic technologies such as transcriptional profiling, proteomics, literature searches, genetic association, etc. help in the identification of sets of important genes. A subset of these genes may act in a coordinated manner, possibly because they are part of the same biological pathway. Interpreting such gene lists and relating them to pathways is a challenging task. Databases of biological relationships between thousands of mammalian genes can help in deciphering omics data. The relationships between genes can be assembled into a biological network with each protein as a node and each relationship as an edge between two proteins (or nodes). This network may then be searched for subnetworks consisting largely of interesting genes from the omics experiment. The subset of genes in the subnetwork along with the web of relationships between them helps to decipher the underlying pathways. Finding such subnetworks that maximally include all proteins from the query set but few others is the focus for this paper. RESULTS: We present a heuristic algorithm and a scoring function that work well both on simulated data and on data from known pathways. The scoring function is an extension of a previous study for a single biological experiment. We use a simple set of heuristics that provide a more efficient solution than the simulated annealing method. We find that our method works on reasonably complex curated networks containing approximately 9000 biological entities (genes and metabolites), and approximately 30,000 biological relationships. We also show that our method can pick up a pathway signal from a query list including a moderate number of genes unrelated to the pathway. In addition, we quantify the sensitivity and specificity of the technique.

Algorithms↗

PubMed2004

MedKit: a helper toolkit for automatic mining of MEDLINE/PubMed citations.

UNLABELLED: MEDLINE/PubMed is one of the most important information sources for bioinformatics text mining. However, there remain limitations in working with MEDLINE/PubMed citations. For example, PubMed imposes an upper limit of 10,000 for downloading PMID list or citations; and MEDLINE files are too large for most off-the-shelf XML parsers. We developed a Java package, MedKit, to work-around the limitations, as well as provide other useful functionalities, e.g. random sampling. Its four modules (querier, sampler, fetcher and parser) can work independently, or be pipelined in various combinations. It can be used as a stand-alone GUI application, or integrated into other text-mining systems. Text mining researchers and others may download and use the toolkit free for non-commercial purposes. AVAILABILITY: http://metnetdb.gdcb.iastate.edu/medkit CONTACT: berleant@iastate.edu.

Abstracting and Indexing↗

PubMed2004

COBrA: a bio-ontology editor.

COBrA is a Java-based ontology editor for bio-ontologies that distinguishes itself from other editors by supporting the linking of concepts between two ontologies, and providing sophisticated analysis and verification functions. In addition to the Gene Ontology and Open Biology Ontologies formats, COBrA can import and export ontologies in the Semantic Web formats RDF, RDFS and OWL.

Cell Physiological Phenomena↗

PubMed2004

PDZBase: a protein-protein interaction database for PDZ-domains.

SUMMARY: PDZBase is a database that aims to contain all known PDZ-domain-mediated protein-protein interactions. Currently, PDZBase contains approximately 300 such interactions, which have been manually extracted from > 200 articles. The database can be queried through both sequence motif and keyword-based searches, and the sequences of interacting proteins can be visually inspected through alignments (for the comparison of several interactions), or as residue-based diagrams including schematic secondary structure information (for individual complexes).

Database Management Systems↗

PubMed2005

A probabilistic model for mining implicit 'chemical compound-gene' relations from literature.

MOTIVATION: The importance of chemical compounds has been emphasized more in molecular biology, and 'chemical genomics' has attracted a great deal of attention in recent years. Thus an important issue in current molecular biology is to identify biological-related chemical compounds (more specifically, drugs) and genes. Co-occurrence of biological entities in the literature is a simple, comprehensive and popular technique to find the association of these entities. Our focus is to mine implicit 'chemical compound and gene' relations from the co-occurrence in the literature. RESULTS: We propose a probabilistic model, called the mixture aspect model (MAM), and an algorithm for estimating its parameters to efficiently handle different types of co-occurrence datasets at once. We examined the performance of our approach not only by a cross-validation using the data generated from the MEDLINE records but also by a test using an independent human-curated dataset of the relationships between chemical compounds and genes in the ChEBI database. We performed experimentation on three different types of co-occurrence datasets (i.e. compound-gene, gene-gene and compound-compound co-occurrences) in both cases. Experimental results have shown that MAM trained by all datasets outperformed any simple model trained by other combinations of datasets with the difference being statistically significant in all cases. In particular, we found that incorporating compound-compound co-occurrences is the most effective in improving the predictive performance. We finally computed the likelihoods of all unknown compound-gene (more specifically, drug-gene) pairs using our approach and selected the top 20 pairs according to the likelihoods. We validated them from biological, medical and pharmaceutical viewpoints.

Artificial Intelligence↗

PubMed2005

Implementing the iHOP concept for navigation of biomedical literature.

MOTIVATION: The World Wide Web has profoundly changed the way in which we access information. Searching the internet is easy and fast, but more importantly, the interconnection of related contents makes it intuitive and closer to the associative organization of human memory. However, the information retrieval tools currently available to researchers in biology and medicine lag far behind the possibilities that the layman has come to expect from the internet. RESULTS: By using genes and proteins as hyperlinks between sentences and abstracts, the information in PubMed can be converted into one navigable resource. iHOP (Information Hyperlinked over Proteins) is an online service that provides this gene-guided network as a natural way of accessing millions of PubMed abstracts and brings all the advantages of the internet to scientific literature research. Navigating across interrelated sentences within this network is closer to human intuition than the use of conventional keyword searches and allows for stepwise and controlled acquisition of information. Moreover, this literature network can be superimposed upon experimental interaction data to facilitate the simultaneous analysis of novel and existing knowledge. The network presented in iHOP currently contains5 million sentences and 40 000 genes from Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Danio rerio, Arabidopsis thaliana, Saccharomyces cerevisiae and Escherichia coli. AVAILABILITY: iHOP is freely accessible at http://www.pdg.cnb.uam.es/UniPub/iHOP/

Biomedical Engineering↗

PubMed2005

Web servicing the biological office.

UNLABELLED: Biologists routinely use Microsoft Office applications for standard analysis tasks. Despite ubiquitous internet resources, information needed for everyday work is often not directly and seamlessly available. Here we describe a very simple and easily extendable mechanism using Web Services to enrich standard MS Office applications with internet resources. We demonstrate its capabilities by providing a Web-based thesaurus for biological objects, which maps names to database identifiers and vice versa via an appropriate synonym list. The client application ProTag makes these features available in MS Office applications using Smart Tags and Add-Ins. AVAILABILITY: http://services.bio.ifi.lmu.de/prothesaurus/

Computational Biology↗

PubMed2004

Constructing ontology-driven protein family databases.

MOTIVATION: Protein family databases provide a central focus for scientific communities as well as providing useful resources to aide research. However, such resources require constant curation and often become outdated and discontinued. We have developed an ontology-driven system for capturing and managing protein family data that addresses the problems of maintenance and sustainability. RESULTS: Using protein phosphatases and ABC transporters as model protein families, we constructed two protein family database resources around a central DAML+OIL ontology. Each resource contains specialist information about each protein family, providing specialized domain-specific resources based on the same template structure. The formal structure, combined with the extraction of biological data using GO terms, allows for automated update strategies. Despite the functional differences between the two protein families, the ontology model was equally applicable to both, demonstrating the generic nature of the system. AVAILABILITY: The protein phosphatase resource, PhosphaBase, is freely available on the internet (http://www.bioinf.man.ac.uk/phosphabase). The DAML+OIL ontology for the protein phosphatases and the ABC transporters is available on request from the authors. CONTACT: kwolstencroft@cs.man.ac.uk.

ATP-Binding Cassette Transporters↗

PubMed2004

An architecture for biological information extraction and representation.

MOTIVATIONS: Technological advances in biomedical research are generating a plethora of heterogeneous data at a high rate. There is a critical need for extraction, integration and management tools for information discovery and synthesis from these heterogeneous data. RESULTS: In this paper, we present a general architecture, called ALFA, for information extraction and representation from diverse biological data. The ALFA architecture consists of: (i) a networked, hierarchical, hyper-graph object model for representing information from heterogeneous data sources in a standardized, structured format; and (ii) a suite of integrated, interactive software tools for information extraction and representation from diverse biological data sources. As part of our research efforts to explore this space, we have currently prototyped the ALFA object model and a set of interactive software tools for searching, filtering, and extracting information from scientific text. In particular, we describe BioFerret, a meta-search tool for searching and filtering relevant information from the web, and ALFA Text Viewer, an interactive tool for user-guided extraction, disambiguation, and representation of information from scientific text. We further demonstrate the potential of our tools in integrating the extracted information with experimental data and diagrammatic biological models via the common underlying ALFA representation. CONTACT: aditya_vailaya@agilent.com.

Abstracting and Indexing↗

PubMed2005

PatGen--a consolidated resource for searching genetic patent sequences.

UNLABELLED: Compared to the wealth of online resources covering genomic, proteomic and derived data the Bioinformatics community is rather underserved when it comes to patent information related to biological sequences. The current online resources are either incomplete or rather expensive. This paper describes, PatGen, an integrated database containing data from bioinformatic and patent resources. This effort addresses the inconsistency of publicly available genetic patent data coverage by providing access to a consolidated dataset. AVAILABILITY: PatGen can be searched at http://www.patgendb.com CONTACT: rjdrouse@patentinformatics.com.

Abstracting and Indexing↗

PubMed2004

GPSDB: a new database for synonyms expansion of gene and protein names.

UNLABELLED: We present a new database, GPSDB (Gene and Protein Synonyms DataBase) which collects gene/protein names, in a species specific way, from 14 main biological resources. A web-based search interface gives access to the database: given a gene/protein name, it retrieves all synonyms for this entity and queries Medline with a set of user-selected terms. AVAILABILITY: GPSDB is freely available from http://biomint.oefai.at/ CONTACT: johann@oefai.at.

Database Management Systems↗

PubMed2005

Co-occurrence based meta-analysis of scientific texts: retrieving biological relationships between genes.

MOTIVATION: The advent of high-throughput experiments in molecular biology creates a need for methods to efficiently extract and use information for large numbers of genes. Recently, the associative concept space (ACS) has been developed for the representation of information extracted from biomedical literature. The ACS is a Euclidean space in which thesaurus concepts are positioned and the distances between concepts indicates their relatedness. The ACS uses co-occurrence of concepts as a source of information. In this paper we evaluate how well the system can retrieve functionally related genes and we compare its performance with a simple gene co-occurrence method. RESULTS: To assess the performance of the ACS we composed a test set of five groups of functionally related genes. With the ACS good scores were obtained for four of the five groups. When compared to the gene co-occurrence method, the ACS is capable of revealing more functional biological relations and can achieve results with less literature available per gene. Hierarchical clustering was performed on the ACS output, as a potential aid to users, and was found to provide useful clusters. Our results suggest that the algorithm can be of value for researchers studying large numbers of genes. AVAILABILITY: The ACS program is available upon request from the authors.

Artificial Intelligence↗

PubMed2005

Concept-based annotation of enzyme classes.

MOTIVATION: Given the explosive growth of biomedical data as well as the literature describing results and findings, it is getting increasingly difficult to keep up to date with new information. Keeping databases synchronized with current knowledge is a time-consuming and expensive task-one which can be alleviated by automatically gathering findings from the literature using linguistic approaches. We describe a method to automatically annotate enzyme classes with disease-related information extracted from the biomedical literature for inclusion in such a database. RESULTS: Enzyme names for the 3901 enzyme classes in the BRENDA database, a repository for quantitative and qualitative enzyme information, were identified in more than 100,000 abstracts retrieved from the PubMed literature database. Phrases in the abstracts were assigned to concepts from the Unified Medical Language System (UMLS) utilizing the MetaMap program, allowing for the identification of disease-related concepts by their semantic fields in the UMLS ontology. Assignments between enzyme classes and diseases were created based on their co-occurrence within a single sentence. False positives could be removed by a variety of filters including minimum number of co-occurrences, removal of sentences containing a negation and the classification of sentences based on their semantic fields by a Support Vector Machine. Verification of the assignments with a manually annotated set of 1500 sentences yielded favorable results of 92% precision at 50% recall, sufficient for inclusion in a high-quality database. AVAILABILITY: Source code is available from the author upon request. SUPPLEMENTARY INFORMATION: ftp.uni-koeln.de/institute/biochemie/pub/brenda/info/diseaseSupp.pdf.

Algorithms↗

PubMed2005

BioIE: extracting informative sentences from the biomedical literature.

SUMMARY: BioIE is a rule-based system that extracts informative sentences relating to protein families, their structures, functions and diseases from the biomedical literaturE. Based on manual definition of templates and rules, it aims at precise sentence extraction rather than wide recall. After uploading source text or retrieving abstracts from MEDLINE, users can extract sentences based on predefined or user-defined template categories. BioIE also provides a brief insight into the syntactic and semantic context of the source-text by looking at word, N-gram and MeSH-term distributions. Important Applications of BioIE are in, for example, annotation of microarray data and of protein databases. AVAILABILITY: http://umber.sbs.man.ac.uk/dbbrowser/bioie/

Database Management Systems↗

PubMed2005

A word-oriented approach to alignment validation.

MOTIVATION: Multiple sequence alignment at the level of whole proteomes requires a high degree of automation, precluding the use of traditional validation methods such as manual curation. Since evolutionary models are too general to describe the history of each residue in a protein family, there is no single algorithm/model combination that can yield a biologically or evolutionarily optimal alignment. We propose a 'shotgun' strategy where many different algorithms are used to align the same family, and the best of these alignments is then chosen with a reliable objective function. We present WOOF, a novel 'word-oriented' objective function that relies on the identification and scoring of conserved amino acid patterns (words) between pairs of sequences. RESULTS: Tests on a subset of reference protein alignments from BAliBASE showed that WOOF tended to rank the (manually curated) reference alignment highest among 1060 alternative (automatically generated) alignments for a majority of protein families. Among the automated alignments, there was a strong positive relationship between the WOOF score and similarity to the reference alignment. The speed of WOOF and its independence from explicit considerations of three-dimensional structure make it an excellent tool for analyzing large numbers of protein families. AVAILABILITY: On request from the authors.

Algorithms↗

PubMed2005

Integration of GO annotations in Correspondence Analysis: facilitating the interpretation of microarray data.

MOTIVATION: The functional interpretation of microarray datasets still represents a time-consuming and challenging task. Up to now functional categories that are relevant for one or more experimental context(s) have been commonly extracted from a set of regulated genes and presented in long lists. RESULTS: To facilitate interpretation, we integrated Gene Ontology (GO) annotations into Correspondence Analysis to display genes, experimental conditions and gene-annotations in a single plot. The position of the annotations in these plots can be directly used for the functional interpretation of clusters of genes or experimental conditions without the need for comparing long lists of annotations. Correspondence Analysis is not limited in the number of experimental conditions that can be compared simultaneously, allowing an easy identification of characterizing annotations even in complex experimental settings. Due to the rapidly increasing amount of annotation data available, we apply an annotation filter. Hereby the number of displayed annotations can be significantly reduced to a set of descriptive ones, further enhancing the interpretability of the plot. We validated the method on transcription data from Saccharomyces cerevisiae and human pancreatic adenocarcinomas. AVAILABILITY: The M-CHiPS software is accessible for collaborators at http://www.mchips.org

Algorithms↗

PubMed2005

Using the biological taxonomy to access biological literature with PathBinderH.

UNLABELLED: PathBinderH allows users to make queries that retrieve sentences and the abstracts containing them from PubMed. Another aspect of PathBinderH is that users can specify biological taxa in order to limit searches by mentioning either the specified taxa, or their subordinate taxa, in the biological taxonomy. Although the current project requires this function only for plant taxa, the principle is extensible to the entire taxonomy. AVAILABILITY: www.plantgenomics.iastate.edu/PathBinderH. Source code and databases on request.

Abstracting and Indexing↗

PubMed2005

POSBIOTM-NER: a trainable biomedical named-entity recognition system.

SUMMARY: POSBIOTM-NER is a trainable biomedical named-entity recognition system. POSBIOTM-NER can be automatically trained and adapted to new datasets without performance degradation, using CRF (conditional random field) machine learning techniques and automatic linguistic feature analysis. Currently, we have trained our system on three different datasets. GENIA-NER was trained based on GENIA Corpus, GENE-NER based on BioCreative data and GPCR-NER based on our own POSBIOTM/NE corpus, respectively, which would be used in GPCR-related pathway extraction.

Algorithms↗