CiteULike: Group: T_lab - library [380 articles]

BioProber: Software System for Biomedical Relation Discovery from PubMed

Hyunchul Jang — 2007-09-19T05:02:26-00:00

Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE (2006), pp. 5779-5782.

The numbers of articles and journals that are published are increasing at a considerable rate, and the published information is growing continuously and fast. Because of this, researches to acquire knowledge automatically have been carried out in the areas of information retrieval, information extraction and text mining. Information retrieval approaches are good for specific topics that the number of related articles is small. But, if the number is bigger, searching skill and knowledge acquisition ability are useless. Though many efforts have been made to extract information from literature, many approaches have concentrated on specific entities, such as proteins, genes and their interactions, and much information is still remained in unstructured text. So, we have developed a system that discovers relations between various categories of biomedical entities. Our system collects abstracts from PubMed by queries representing a topic and visualizes relationship from the collection by automatic information extraction

BIOSMILE: A semantic role labeling system for biomedical verbs using a maximum-entropy model with automatically generated template features

Richard Tsai — 2007-09-02T22:23:24-00:00

BMC Bioinformatics, Vol. 8 (01 September 2007), 325.

Web services in the life sciences

Vasa Curcin — 2005-07-21T09:56:56-00:00

Drug Discovery Today, Vol. 10, No. 12. (15 June 2005), pp. 865-871.

Web services provide a standard way of publishing applications and data sources over the internet, enabling mass dissemination of knowledge. In the life sciences, the web-service approach is seen as being a road to standardizing the multitude of tools available from different providers. In this article, we present an overview of the technology (focusing on life-science applications), we list the currently available service providers and we discuss advanced issues raised by the concept.

PubMed vs. HighWire Press: A head-to-head comparison of two medical literature search engines.

TE Vanhecke — 2007-08-22T13:37:52-00:00

Comput Biol Med, Vol. 37, No. 9. (September 2007), pp. 1252-1258.

PubMed and HighWire Press are both useful medical literature search engines available for free to anyone on the internet. We measured retrieval accuracy, number of results generated, retrieval speed, features and search tools on HighWire Press and PubMed using the quick search features of each. We found that using HighWire Press resulted in a higher likelihood of retrieving the desired article and higher number of search results than the same search on PubMed. PubMed was faster than HighWire Press in delivering search results regardless of search settings. There are considerable differences in search features between these two search engines.

Clustering Microarray-Derived Gene Lists through Implicit Literature Relationships.

Mark F Burkart — 2007-08-15T14:23:13-00:00

Bioinformatics (30 May 2007)

MOTIVATION: Microarrays rapidly generate large quantities of gene expression information, but interpreting such data within a biological context is still relatively complex and laborious. New methods that can identify functionally related genes via shared literature concepts will be useful in addressing these needs. RESULTS: We have developed a novel method that uses implicit literature relationships (concepts related via shared, intermediate concepts) to cluster related genes. Genes are evaluated for implicit connections within a network of biomedical objects (other genes, ontological concepts, and diseases) that are connected via their co-occurrences in Medline titles and/or abstracts. On the basis of these implicit relationships, individual gene pairs are scored using a probability-based algorithm. Scores are generated for all pairwise combinations of genes, which are then clustered based on the scores. We applied this method to a test set composed of nine functional groups with known relationships. The method scored highly for all nine groups and significantly better than a benchmark co-occurrence-based method for six groups. We then applied this method to gene sets specific to two previously defined breast tumor subtypes. Analysis of the results recapitulated known biological relationships and identified novel pathway relationships unique to each tumor subtype. We demonstrate that this method provides a valuable new means of identifying and visualizing significantly related genes within gene lists via their implicit relationships in the literature. SUPPLEMENTARY INFORMATION: Supplemental Figures 1 - 5. Supplemental Tables 1 and 2.

OReFiL: an online resource finder for life sciences

Yasunori Yamamoto — 2007-08-06T06:44:13-00:00

BMC Bioinformatics, Vol. 8 (06 August 2007), 287.

A domain-based approach to predict protein-protein interactions

Mudita Singhal — 2007-06-13T21:51:28-00:00

BMC Bioinformatics, Vol. 8 (13 June 2007), 199.

Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

2007-06-07T05:46:18-00:00

Nature, Vol. 447, No. 7145. (7 June 2007), pp. 661-678.

A quantitative model for linking two disparate sets of articles in MEDLINE

Vetle Torvik — 2007-05-29T10:46:51-00:00

Bioinformatics (26 April 2007), btm161.

Background: Identifying information that implicitly links two disparate sets of articles is a fundamental and intuitive data mining strategy that can help investigators address real scientific questions. The Arrowsmith two-node search finds title words and phrases (so-called B-terms) that are shared across two sets of articles within MEDLINE and displays them in a manner that facilitates human assessment. A serious stumbling-block has been the lack of a quantitative model for predicting which of the hundreds if not thousands of B-terms computed for a given search are most likely to be relevant to the investigator. Methodology/Principal Findings: Using a public two-node search interface, field testers devised a set of two-node searches under real life conditions and a certain number of B-terms were marked relevant. These were employed as "gold standards;" each B-term was characterized according to eight complementary features that were strongly correlated with relevance. A logistic regression model was developed that permits one to estimate the probability of relevance for each B-term, to rank B-terms according to their likely relevance, and to estimate the overall number of relevant B-terms inherent in a given two-node search. Conclusions/Significance: The model greatly simplifies and streamlines the process of carrying out a two-node search, and may be applicable to a number of other literature-based discovery applications, including the so-called one-node search and related gene-centric strategies that incorporate implicit links to predict how genes may be related to each other and to human diseases. This should encourage much wider exploration of text mining for implicit information among the general scientific community. Availability: two-node searches can be carried out freely at http://arrowsmith.psych.uic.edu Supplementary information: included in a separate file. 10.1093/bioinformatics/btm161

Improved scoring of functional groups from gene expression data by decorrelating GO graph structure

Alexa — 2006-07-02T09:51:29-00:00

Bioinformatics, Vol. 22, No. 13. (1 July 2006), pp. 1600-1607.

Text-derived concept profiles support assessment of DNA microarray data for acute myeloid leukemia and for androgen receptor stimulation

Rob Jelier — 2007-01-19T05:58:51-00:00

BMC Bioinformatics, Vol. 8 (18 January 2007), 14.

Gene name identification and normalization using a model organism database

Alexander Morgan — 2006-06-21T21:50:35-00:00

J. of Biomedical Informatics, Vol. 37, No. 6. (December 2004), pp. 396-410.

Evaluation of BioCreAtIvE assessment of task 2.

C Blaschke — 2006-04-21T22:29:37-00:00

BMC Bioinformatics, Vol. 6 Suppl 1 (2005)

BACKGROUND: Molecular Biology accumulated substantial amounts of data concerning functions of genes and proteins. Information relating to functional descriptions is generally extracted manually from textual data and stored in biological databases to build up annotations for large collections of gene products. Those annotation databases are crucial for the interpretation of large scale analysis approaches using bioinformatics or experimental techniques. Due to the growing accumulation of functional descriptions in biomedical literature the need for text mining tools to facilitate the extraction of such annotations is urgent. In order to make text mining tools useable in real world scenarios, for instance to assist database curators during annotation of protein function, comparisons and evaluations of different approaches on full text articles are needed. RESULTS: The Critical Assessment for Information Extraction in Biology (BioCreAtIvE) contest consists of a community wide competition aiming to evaluate different strategies for text mining tools, as applied to biomedical literature. We report on task two which addressed the automatic extraction and assignment of Gene Ontology (GO) annotations of human proteins, using full text articles. The predictions of task 2 are based on triplets of protein--GO term--article passage. The annotation-relevant text passages were returned by the participants and evaluated by expert curators of the GO annotation (GOA) team at the European Institute of Bioinformatics (EBI). Each participant could submit up to three results for each sub-task comprising task 2. In total more than 15,000 individual results were provided by the participants. The curators evaluated in addition to the annotation itself, whether the protein and the GO term were correctly predicted and traceable through the submitted text fragment. CONCLUSION: Concepts provided by GO are currently the most extended set of terms used for annotating gene products, thus they were explored to assess how effectively text mining tools are able to extract those annotations automatically. Although the obtained results are promising, they are still far from reaching the required performance demanded by real world applications. Among the principal difficulties encountered to address the proposed task, were the complex nature of the GO terms and protein names (the large range of variants which are used to express proteins and especially GO terms in free text), and the lack of a standard training set. A range of very different strategies were used to tackle this task. The dataset generated in line with the BioCreative challenge is publicly available and will allow new possibilities for training information extraction methods in the domain of molecular biology.

BioCreAtIvE task 1A: gene mention finding evaluation.

A Yeh — 2006-04-21T22:26:39-00:00

BMC Bioinformatics, Vol. 6 Suppl 1 (2005)

BACKGROUND: The biological research literature is a major repository of knowledge. As the amount of literature increases, it will get harder to find the information of interest on a particular topic. There has been an increasing amount of work on text mining this literature, but comparing this work is hard because of a lack of standards for making comparisons. To address this, we worked with colleagues at the Protein Design Group, CNB-CSIC, Madrid to develop BioCreAtIvE (Critical Assessment for Information Extraction in Biology), an open common evaluation of systems on a number of biological text mining tasks. We report here on task 1A, which deals with finding mentions of genes and related entities in text. "Finding mentions" is a basic task, which can be used as a building block for other text mining tasks. The task makes use of data and evaluation software provided by the (US) National Center for Biotechnology Information (NCBI). RESULTS: 15 teams took part in task 1A. A number of teams achieved scores over 80% F-measure (balanced precision and recall). The teams that tried to use their task 1A systems to help on other BioCreAtIvE tasks reported mixed results. CONCLUSION: The 80% plus F-measure results are good, but still somewhat lag the best scores achieved in some other domains such as newswire, due in part to the complexity and length of gene names, compared to person or organization names in newswire.

Data preparation and interannotator agreement: BioCreAtIvE task 1B.

ME Colosimo — 2006-04-27T15:58:22-00:00

BMC Bioinformatics, Vol. 6 Suppl 1 (2005)

BACKGROUND: We prepared and evaluated training and test materials for an assessment of text mining methods in molecular biology. The goal of the assessment was to evaluate the ability of automated systems to generate a list of unique gene identifiers from PubMed abstracts for the three model organisms Fly, Mouse, and Yeast. This paper describes the preparation and evaluation of answer keys for training and testing. These consisted of lists of normalized gene names found in the abstracts, generated by adapting the gene list for the full journal articles found in the model organism databases. For the training dataset, the gene list was pruned automatically to remove gene names not found in the abstract; for the testing dataset, it was further refined by manual annotation by annotators provided with guidelines. A critical step in interpreting the results of an assessment is to evaluate the quality of the data preparation. We did this by careful assessment of interannotator agreement and the use of answer pooling of participant results to improve the quality of the final testing dataset. RESULTS: Interannotator analysis on a small dataset showed that our gene lists for Fly and Yeast were good (87% and 91% three-way agreement) but the Mouse gene list had many conflicts (mostly omissions), which resulted in errors (69% interannotator agreement). By comparing and pooling answers from the participant systems, we were able to add an additional check on the test data; this allowed us to find additional errors, especially in Mouse. This led to 1% change in the Yeast and Fly "gold standard" answer keys, but to an 8% change in the mouse answer key. CONCLUSION: We found that clear annotation guidelines are important, along with careful interannotator experiments, to validate the generated gene lists. Also, abstracts alone are a poor resource for identifying genes in paper, containing only a fraction of genes mentioned in the full text (25% for Fly, 36% for Mouse). We found that there are intrinsic differences between the model organism databases related to the number of synonymous terms and also to curation criteria. Finally, we found that answer pooling was much faster and allowed us to identify more conflicting genes than interannotator analysis.

Overview of BioCreAtIvE task 1B: normalized gene lists.

L Hirschman — 2006-04-21T22:27:54-00:00

BMC Bioinformatics, Vol. 6 Suppl 1 (2005)

BACKGROUND: Our goal in BioCreAtIve has been to assess the state of the art in text mining, with emphasis on applications that reflect real biological applications, e.g., the curation process for model organism databases. This paper summarizes the BioCreAtIvE task 1B, the "Normalized Gene List" task, which was inspired by the gene list supplied for each curated paper in a model organism database. The task was to produce the correct list of unique gene identifiers for the genes and gene products mentioned in sets of abstracts from three model organisms (Yeast, Fly, and Mouse). RESULTS: Eight groups fielded systems for three data sets (Yeast, Fly, and Mouse). For Yeast, the top scoring system (out of 15) achieved 0.92 F-measure (harmonic mean of precision and recall); for Mouse and Fly, the task was more difficult, due to larger numbers of genes, more ambiguity in the gene naming conventions (particularly for Fly), and complex gene names (for Mouse). For Fly, the top F-measure was 0.82 out of 11 systems and for Mouse, it was 0.79 out of 16 systems. CONCLUSION: This assessment demonstrates that multiple groups were able to perform a real biological task across a range of organisms. The performance was dependent on the organism, and specifically on the naming conventions associated with each organism. These results hold out promise that the technology can provide partial automation of the curation process in the near future.

Overview of BioCreAtIvE: critical assessment of information extraction for biology.

L Hirschman — 2006-04-21T22:25:04-00:00

BMC Bioinformatics, Vol. 6 Suppl 1 (2005)

BACKGROUND: The goal of the first BioCreAtIvE challenge (Critical Assessment of Information Extraction in Biology) was to provide a set of common evaluation tasks to assess the state of the art for text mining applied to biological problems. The results were presented in a workshop held in Granada, Spain March 28-31, 2004. The articles collected in this BMC Bioinformatics supplement entitled "A critical assessment of text mining methods in molecular biology" describe the BioCreAtIvE tasks, systems, results and their independent evaluation. RESULTS: BioCreAtIvE focused on two tasks. The first dealt with extraction of gene or protein names from text, and their mapping into standardized gene identifiers for three model organism databases (fly, mouse, yeast). The second task addressed issues of functional annotation, requiring systems to identify specific text passages that supported Gene Ontology annotations for specific proteins, given full text articles. CONCLUSION: The first BioCreAtIvE assessment achieved a high level of international participation (27 groups from 10 countries). The assessment provided state-of-the-art performance results for a basic task (gene name finding and normalization), where the best systems achieved a balanced 80% precision / recall or better, which potentially makes them suitable for real applications in biology. The results for the advanced task (functional annotation from free text) were significantly lower, demonstrating the current limitations of text-mining approaches where knowledge extrapolation and interpretation are required. In addition, an important contribution of BioCreAtIvE has been the creation and release of training and test data sets for both tasks. There are 22 articles in this special issue, including six that provide analyses of results or data quality for the data sets, including a novel inter-annotator consistency assessment for the test set used in task 2.

Personalized ranking: a contextual ranking approach

Gae-Won You — 2007-07-12T02:53:00-00:00

(2007), pp. 506-510.

uBioRSS: Tracking Taxonomic Literature Using RSS.

Patrick R Leary — 2007-03-31T13:55:24-00:00

Bioinformatics (28 March 2007)

SUMMARY: Web content syndication through standard formats such as RSS and ATOM has become an increasingly popular mechanism for publishers, news sources, and blogs to disseminate regularly updated content. These standardized syndication formats deliver content directly to the subscriber, allowing them to locally aggregate content from a variety of sources instead of having to find the information on multiple websites. The uBioRSS application is a "taxonomically intelligent" service customized for the biological sciences. It aggregates syndicated content from academic publishers and science news feeds, then uses a taxonomic name entity recognition algorithm to identify and index taxonomic names within those data streams. The resulting name index is cross-referenced to current global taxonomic datasets to provide context for browsing the publications by taxonomic group. This process, called taxonomic indexing, draws upon services developed specifically for biological sciences, collectively referred to as "taxonomic intelligence." Such value-added enhancements can provide biologists with accelerated and improved access to current biological content. AVAILABILITY: http://names.ubio.org/rss/

LinkHub: a Semantic Web system that facilitates cross-database queries and information retrieval in proteomics.

AK Smith — 2007-06-07T03:04:30-00:00

BMC Bioinformatics, Vol. 8 Suppl 3 (2007)

BACKGROUND: A key abstraction in representing proteomics knowledge is the notion of unique identifiers for individual entities (e.g. proteins) and the massive graph of relationships among them. These relationships are sometimes simple (e.g. synonyms) but are often more complex (e.g. one-to-many relationships in protein family membership). RESULTS: We have built a software system called LinkHub using Semantic Web RDF that manages the graph of identifier relationships and allows exploration with a variety of interfaces. For efficiency, we also provide relational-database access and translation between the relational and RDF versions. LinkHub is practically useful in creating small, local hubs on common topics and then connecting these to major portals in a federated architecture; we have used LinkHub to establish such a relationship between UniProt and the North East Structural Genomics Consortium. LinkHub also facilitates queries and access to information and documents related to identifiers spread across multiple databases, acting as "connecting glue" between different identifier spaces. We demonstrate this with example queries discovering "interologs" of yeast protein interactions in the worm and exploring the relationship between gene essentiality and pseudogene content. We also show how "protein family based" retrieval of documents can be achieved. LinkHub is available at hub.gersteinlab.org and hub.nesg.org with supplement, database models and full-source code. CONCLUSION: LinkHub leverages Semantic Web standards-based integrated data to provide novel information retrieval to identifier-related documents through relational graph queries, simplifies and manages connections to major hubs such as UniProt, and provides useful interactive and query interfaces for exploring the integrated data.

Unavailability of online supplementary scientific information from articles published in major journals.

E Evangelou — 2006-06-29T19:34:37-00:00

FASEB J, Vol. 19, No. 14. (December 2005), pp. 1943-1944.

Printed articles increasingly rely on online supplements to store critical scientific information, but such data may eventually become unavailable. We checked the current availability of online supplementary scientific information published in six top-cited scientific journals (Science, Nature, Cell, New England Journal of Medicine, Lancet, Proceedings of the National Academy of Sciences USA). Here we show that in 4.7% and 9.6% of articles with online supplementary material, some of the supplements became unavailable within 2 and 5 years of their publication, respectively.

Discovering functional linkages and uncharacterized cellular pathways using phylogenetic profile comparisons: a comprehensive assessment

Raja Jothi — 2007-05-24T04:38:16-00:00

BMC Bioinformatics, Vol. 8 (23 May 2007), 173.

Publishing perishing? Towards tomorrow's information architecture

Michael Seringhaus — 2007-01-20T17:59:35-00:00

BMC Bioinformatics, Vol. 8 (19 January 2007), 17.

The database revolution

2007-01-18T12:38:46-00:00

Nature, Vol. 445, No. 7125. (18 January 2007), pp. 229-230.

Structured digital abstract makes text mining easy

Mark Gerstein — 2007-05-10T01:40:08-00:00

Nature, Vol. 447, No. 7141. (09 May 2007), pp. 142-142.

Text mining and protein annotations: the construction and use of protein description sentences.

M Krallinger — 2007-05-24T15:53:01-00:00

Genome Inform, Vol. 17, No. 2. (2006), pp. 121-130.

Existing biological knowledge stored as structured database records has been extracted manually by database curators analyzing the scientific literature. Most of this information was derived from sentences which describe biologically relevant aspects of genes and gene products. We introduce the Protein description sentence (Prodisen) corpus, a useful resource for the automatic identification and construction of text-based protein and gene description records using information extraction and text classification techniques. Basic guidelines and criteria relevant for the construction of a text corpus of functional descriptions of genes and proteins are proposed. The steps used for the corpus construction and its features are presented. Moreover, some of the potential applications of the Prodisen corpus for biomedical text mining purposes are explored and the obtained results are presented.

Estimating the annotation error rate of curated GO database sequence annotations

Craig Jones — 2007-05-23T12:04:27-00:00

BMC Bioinformatics, Vol. 8, No. 1. (2007)

BACKGROUND:Annotations that describe the function of sequences are enormously important to researchers during laboratory investigations and when making computational inferences. However, there has been little investigation into the data quality of sequence function annotations. Here we have developed a new method of estimating the error rate of curated sequence annotations, and applied this to the Gene Ontology (GO) sequence database (GOSeqLite). This method involved artificially adding errors to sequence annotations at known rates, and used regression to model the impact on the precision of annotations based on BLAST matched sequences.RESULTS:We estimated the error rate of curated GO sequence annotations in the GOSeqLite database (March 2006) at between 28% and 30%. Annotations made without use of sequence similarity based methods (non-ISS) had an estimated error rate of between 13% and 18%. Annotations made with the use of sequence similarity methodology (ISS) had an estimated error rate of 49%.CONCLUSIONS:While the overall error rate is reasonably low, it would be prudent to treat all ISS annotations with caution. Electronic annotators that use ISS annotations as the basis of predictions are likely to have higher false prediction rates, and for this reason designers of these systems should consider avoiding ISS annotations where possible. Electronic annotators that use ISS annotations to make predictions should be viewed sceptically. We recommend that curators thoroughly review ISS annotations before accepting them as valid. Overall, users of curated sequence annotations from the GO database should feel assured that they are using a comparatively high quality source of information.

BioPP: A Tool for Web-publication of Biological Networks

Ganesh Viswanathan — 2007-05-23T12:03:18-00:00

BMC Bioinformatics, Vol. 8, No. 1. (2007)

BACKGROUND:Cellular processes depend on the function of intracellular molecular networks. The curation of the literature relevant to specific biological pathways is important for many theoretical and experimental research teams and communities. No current tool supports web publication or hosting of user-developed large scale annotated pathway diagrams. Sharing via web publication is needed to allow real-time access to the current literature pathway knowledgebase, both privately within a research team or publicly among the outside research community. Web publication also facilitates team and/or community input into the curation process while allowing centralized control of the curation and validation process. We have developed new tool to address these needs. Biological Pathway Publisher (BioPP) is a software suite for converting CellDesigner Systems Biology Markup Language (CD-SBML) formatted pathways into a web viewable format. The BioPP suite is available for private use and for depositing knowledgebases into a newly created public repository.RESULTS:BioPP suite is a web-based application that allows pathway knowledgebases stored in CD-SBML to be web published with an easily navigated user interface. The BioPP suite consists of four interrelated elements: a pathway publisher, an upload web-interface, a pathway repository for user-deposited knowledgebases and a pathway navigator. Users have the option to convert their CD-SBML files to HTML for restricted use or to allow their knowledgebase to be web-accessible to the scientific community. All entities in all knowledgebases in the repository are linked to public database entries as well as to a newly created public wiki which provides a discussion forum. CONCLUSION:BioPP tools and the public repository facilitate sharing of pathway knowledgebases and interactive curation for research teams and scientific communities. BioPP suite is accessible at http://tsb.mssm.edu/pathwayPublisher/broadcast/

Modeling cellular machinery through biological network comparison

Roded Sharan — 2006-04-07T06:49:29-00:00

Nature Biotechnology, Vol. 24, No. 4. (06 April 2006), pp. 427-433.

Answering Clinical Questions with Knowledge-Based and Statistical Techniques

Dina Demner-Fushman — 2007-05-21T09:04:31-00:00

Computational Linguistics, Vol. 33, No. 1. (March 2007), pp. 63-103.

The combination of recent developments in question-answering research and the availability of unparalleled resources developed specifically for automatic semantic processing of text in the medical domain provides a unique opportunity to explore complex question answering in the domain of clinical medicine. This article presents a system designed to satisfy the information needs of physicians practicing evidence-based medicine. We have developed a series of knowledge extractors, which employ a combination of knowledge-based and statistical techniques, for automatically identifying clinically relevant aspects of MEDLINE abstracts. These extracted elements serve as the input to an algorithm that scores the relevance of citations with respect to structured representations of information needs, in accordance with the principles of evidence-based medicine. Starting with an initial list of citations retrieved by PubMed, our system can bring relevant abstracts into higher ranking positions, and from these abstracts generate responses that directly answer physicians' questions. We describe three separate evaluations: one focused on the accuracy of the knowledge extractors, one conceptualized as a document reranking task, and finally, an evaluation of answers by two physicians. Experiments on a collection of real-world clinical questions show that our approach significantly outperforms the already competitive PubMed baseline.

Question Answering in Restricted Domains: An Overview

Diego Mollá — 2007-05-21T07:56:12-00:00

Computational Linguistics, Vol. 33, No. 1. (March 2007), pp. 41-61.

Automated question answering has been a topic of research and development since the earliest AI applications. Computing power has increased since the first such systems were developed, and the general methodology has changed from the use of hand-encoded knowledge bases about simple domains to the use of text collections as the main knowledge source over more complex domains. Still, many research issues remain. The focus of this article is on the use of restricted domains for automated question answering. The article contains a historical perspective on question answering over restricted domains and an overview of the current methods and applications used in restricted domains. A main characteristic of question answering in restricted domains is the integration of domain-specific information that is either developed for question answering or that has been developed for other purposes. We explore the main methods developed to leverage this domain-specific information.

Reactome: a knowledgebase of biological pathways.

G Joshi-Tope — 2006-02-28T11:50:48-00:00

Nucleic Acids Res, Vol. 33, No. Database issue. (1 January 2005)

Reactome, located at http://www.reactome.org is a curated, peer-reviewed resource of human biological processes. Given the genetic makeup of an organism, the complete set of possible reactions constitutes its reactome. The basic unit of the Reactome database is a reaction; reactions are then grouped into causal chains to form pathways. The Reactome data model allows us to represent many diverse processes in the human system, including the pathways of intermediary metabolism, regulatory pathways, and signal transduction, and high-level processes, such as the cell cycle. Reactome provides a qualitative framework, on which quantitative data can be superimposed. Tools have been developed to facilitate custom data entry and annotation by expert biologists, and to allow visualization and exploration of the finished dataset as an interactive process map. Although our primary curational domain is pathways from Homo sapiens, we regularly create electronic projections of human pathways onto other organisms via putative orthologs, thus making Reactome relevant to model organism research communities. The database is publicly available under open source terms, which allows both its content and its software infrastructure to be freely used and redistributed.

The PANTHER database of protein families, subfamilies, functions and pathways.

H Mi — 2005-12-23T12:28:46-00:00

Nucleic Acids Res, Vol. 33, No. Database issue. (1 January 2005)

PANTHER is a large collection of protein families that have been subdivided into functionally related subfamilies, using human expertise. These subfamilies model the divergence of specific functions within protein families, allowing more accurate association with function (ontology terms and pathways), as well as inference of amino acids important for functional specificity. Hidden Markov models (HMMs) are built for each family and subfamily for classifying additional protein sequences. The latest version, 5.0, contains 6683 protein families, divided into 31,705 subfamilies, covering approximately 90% of mammalian protein-coding genes. PANTHER 5.0 includes a number of significant improvements over previous versions, most notably (i) representation of pathways (primarily signaling pathways) and association with subfamilies and individual protein sequences; (ii) an improved methodology for defining the PANTHER families and subfamilies, and for building the HMMs; (iii) resources for scoring sequences against PANTHER HMMs both over the web and locally; and (iv) a number of new web resources to facilitate analysis of large gene lists, including data generated from high-throughput expression experiments. Efforts are underway to add PANTHER to the InterPro suite of databases, and to make PANTHER consistent with the PIRSF database. PANTHER is now publicly available without restriction at http://panther.appliedbiosystems.com.

Event ontology: a pathway-centric ontology for biological processes.

T Kushida — 2007-05-02T05:22:42-00:00

Pac Symp Biocomput (2006), pp. 152-163.

Event ontology is a new biomedical ontology developed to annotate pathway components in a pathway database. It organizes the concepts and terms of sub-pathways, pathways, biological phenomena, experimental conditions, medications, and external stimuli appearing in biological pathways (e.g. signal transduction, disease-, metabolic-, molecular interaction-, genetic interaction pathways, etc.). Concepts in the Event ontology are extracted manually from scientific literature. Each term has links to external databases such as Gene Ontology, Reactome, KEGG, BioCyc, and PubMed.

The connectivity map.

SW Michnick — 2007-05-01T11:48:13-00:00

Nat Chem Biol, Vol. 2, No. 12. (December 2006), pp. 663-664.

The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease.

J Lamb — 2006-10-02T08:35:35-00:00

Science, Vol. 313, No. 5795. (29 September 2006), pp. 1929-1935.

To pursue a systematic approach to the discovery of functional connections among diseases, genetic perturbation, and drug action, we have created the first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules, together with pattern-matching software to mine these data. We demonstrate that this "Connectivity Map" resource can be used to find connections among small molecules sharing a mechanism of action, chemicals and physiological processes, and diseases and drugs. These results indicate the feasibility of the approach and suggest the value of a large-scale community Connectivity Map project.

The PROSITE database.

N Hulo — 2006-03-24T20:29:41-00:00

Nucleic Acids Res, Vol. 34, No. Database issue. (1 January 2006)

The PROSITE database consists of a large collection of biologically meaningful signatures that are described as patterns or profiles. Each signature is linked to a documentation that provides useful biological information on the protein family, domain or functional site identified by the signature. The PROSITE database is now complemented by a series of rules that can give more precise information about specific residues. During the last 2 years, the documentation and the ScanProsite web pages were redesigned to add more functionalities. The latest version of PROSITE (release 19.11 of September 27, 2005) contains 1329 patterns and 552 profile entries. Over the past 2 years more than 200 domains have been added, and now 52% of UniProtKB/Swiss-Prot entries (release 48.1 of September 27, 2005) have a cross-reference to a PROSITE entry. The database is accessible at http://www.expasy.org/prosite/.

ORegAnno: an open access database and curation system for literature-derived promoters, transcription factor binding sites and regulatory variation

SB Montgomery — 2006-02-27T16:11:44-00:00

Bioinformatics, Vol. 22, No. 5. (1 March 2006), pp. 637-640.

Drosophila DNase I footprint database: a systematic genome annotation of transcription factor binding sites in the fruitfly, Drosophila melanogaster.

CM Bergman — 2007-04-30T09:18:37-00:00

Bioinformatics, Vol. 21, No. 8. (15 April 2005), pp. 1747-1749.

SUMMARY: Despite increasing numbers of computational tools developed to predict cis-regulatory sequences, the availability of high-quality datasets of transcription factor binding sites limits advances in the bioinformatics of gene regulation. Here we present such a dataset based on a systematic literature curation and genome annotation of DNase I footprints for the fruitfly, Drosophila melanogaster. Using the experimental results of 201 primary references, we annotated 1367 binding sites from 87 transcription factors and 101 target genes in the D.melanogaster genome sequence. These data will provide a rich resource for future bioinformatics analyses of transcriptional regulation in Drosophila such as constructing motif models, training cis-regulatory module detectors, benchmarking alignment tools and continued text mining of the extensive literature on transcriptional regulation in this important model organism. AVAILABILITY: http://www.flyreg.org/ CONTACT: cbergman@gen.cam.ac.uk.

A text-mining analysis of the human phenome

Marc van Driel — 2006-02-22T15:53:51-00:00

European Journal of Human Genetics, Vol. aop, No. current.

The tYNA platform for comparative interactomics: a web tool for managing, comparing and mining multiple networks.

KY Yip — 2007-04-21T06:10:22-00:00

Bioinformatics, Vol. 22, No. 23. (1 December 2006), pp. 2968-2970.

Biological processes involve complex networks of interactions between molecules. Various large-scale experiments and curation efforts have led to preliminary versions of complete cellular networks for a number of organisms. To grapple with these networks, we developed TopNet-like Yale Network Analyzer (tYNA), a Web system for managing, comparing and mining multiple networks, both directed and undirected. tYNA efficiently implements methods that have proven useful in network analysis, including identifying defective cliques, finding small network motifs (such as feed-forward loops), calculating global statistics (such as the clustering coefficient and eccentricity), and identifying hubs and bottlenecks. It also allows one to manage a large number of private and public networks using a flexible tagging system, to filter them based on a variety of criteria, and to visualize them through an interactive graphical interface. A number of commonly used biological datasets have been pre-loaded into tYNA, standardized and grouped into different categories. AVAILABILITY: The tYNA system can be accessed at http://networks.gersteinlab.org/tyna. The source code, JavaDoc API and WSDL can also be downloaded from the website. tYNA can also be accessed from the Cytoscape software using a plugin.

The Biomolecular Interaction Network Database and related tools 2005 update.

C Alfarano — 2006-06-25T23:29:34-00:00

Nucleic Acids Res, Vol. 33, No. Database issue. (1 January 2005)

The Biomolecular Interaction Network Database (BIND) (http://bind.ca) archives biomolecular interaction, reaction, complex and pathway information. Our aim is to curate the details about molecular interactions that arise from published experimental research and to provide this information, as well as tools to enable data analysis, freely to researchers worldwide. BIND data are curated into a comprehensive machine-readable archive of computable information and provides users with methods to discover interactions and molecular mechanisms. BIND has worked to develop new methods for visualization that amplify the underlying annotation of genes and proteins to facilitate the study of molecular interaction networks. BIND has maintained an open database policy since its inception in 1999. Data growth has proceeded at a tremendous rate, approaching over 100 000 records. New services provided include a new BIND Query and Submission interface, a Standard Object Access Protocol service and the Small Molecule Interaction Database (http://smid.blueprint.org) that allows users to determine probable small molecule binding sites of new sequences and examine conserved binding residues.

BIND--a data specification for storing and describing biomolecular interactions, molecular complexes and pathways.

GD Bader — 2006-07-24T18:43:33-00:00

Bioinformatics, Vol. 16, No. 5. (May 2000), pp. 465-477.

MOTIVATION: Proteomics is gearing up towards high-throughput methods for identifying and characterizing all of the proteins, protein domains and protein interactions in a cell and will eventually create more recorded biological information than the Human Genome Project. Each protein expressed in a cell can interact with various other proteins and molecules in the course of its function. A standard data specification is required that can describe and store this information in all its detail and allow efficient cross-platform transfer of data. A complete specification must be the basis for any database or tool for managing and analysing this information. RESULTS: We have defined a complete data specification in ASN.1 that can describe information about biomolecular interactions, complexes and pathways. Our group is using this data specification in our database, the Biomolecular Interaction Network Database (BIND). An interaction record is based on the interaction between two objects. An object can be a protein, DNA, RNA, ligand, molecular complex or an interaction. Interaction description encompasses cellular location, experimental conditions used to observe the interaction, conserved sequence, molecular location, chemical action, kinetics, thermodynamics, and chemical state. Molecular complexes are defined as collections of more than two interactions that form a complex, with extra descriptive information such as complex topology. Pathways are defined as collections of more than two interactions that form a pathway, with additional descriptive information such as cell cycle stage. A request for proposal of a human readable flat-file format that mirrors the BIND data specification is also tendered for interested parties. AVAILABILITY: The ASN.1 data specification for biomolecular interaction, molecular complex and pathway data is available at ftp://bioinfo.mshri.on.ca/pub/BIND/Spec/bind.asn. An interactive browser for this document is available through our homepage at http://bioinfo.mshri.on.ca/BIND/asn-browser/.

PubFocus: Semantic MEDLINE/PubMed citations analytics through integration of controlled biomedical dictionaries and ranking algorithm

Maksim Plikus — 2006-10-03T03:18:50-00:00

BMC Bioinformatics, Vol. 7 (02 October 2006), 424.

botXminer: mining biomedical literature with a new web-based application.

U Mudunuri — 2006-07-26T08:30:03-00:00

Nucleic Acids Res, Vol. 34, No. Web Server issue. (1 July 2006)

This paper outlines botXminer, a publicly available application to search XML-formatted MEDLINE data in a complete, object-relational schema implemented in Oracle XML DB. An advantage offered by botXminer is that it can generate quantitative results with certain queries that are not feasible through the Entrez-PubMed interface. After retrieving citations associated with user-supplied search terms, MEDLINE fields (title, abstract, journal, MeSH and chemical) and terms (MeSH qualifiers and descriptors, keywords, author, gene symbol and chemical), these citations are grouped and displayed as tabulated or graphic results. This work represents an extension of previous research for integrating these citations with relational systems. botXminer has a user-friendly, intuitive interface that can be freely accessed at http://botdb.abcc.ncifcrf.gov.

PubNet: a flexible system for visualizing literature derived networks.

SM Douglas — 2006-01-23T23:38:00-00:00

Genome Biol, Vol. 6, No. 9. (2005)

We have developed PubNet, a web-based tool that extracts several types of relationships returned by PubMed queries and maps them into networks, allowing for graphical visualization, textual navigation, and topological analysis. PubNet supports the creation of complex networks derived from the contents of individual citations, such as genes, proteins, Protein Data Bank (PDB) IDs, Medical Subject Headings (MeSH) terms, and authors. This feature allows one to, for example, examine a literature derived network of genes based on functional similarity.

Functional centering: grounding referential coherence in information structure

Michael Strube — 2007-03-26T08:15:52-00:00

Comput. Linguist., Vol. 25, No. 3. (1999), pp. 309-344.

WYSIWYM: building user interfaces with natural language feedback

Roger Evans — 2007-03-26T08:12:47-00:00

(2003), pp. 203-206.

Filling preposition-based templates to capture information from medical abstracts.

G Leroy — 2006-12-06T21:45:44-00:00

Pac Symp Biocomput (2002), pp. 350-361.

Due to the recent explosion of information in the biomedical field, it is hard for a single researcher to review the complex network involving genes, proteins, and interactions. We are currently building GeneScene, a toolkit that will assist researchers in reviewing existing literature, and report on the first phase in our development effort: extracting the relevant information from medical abstracts. We are developing a medical parser that extracts information, fills basic prepositional-based templates, and combines the templates to capture the underlying sentence logic. We tested our parser on 50 unseen abstracts and found that it extracted 246 templates with a precision of 70%. In comparison with many other techniques, more information was extracted without sacrificing precision. Future improvement in precision will be achieved by correcting three categories of errors.

Creating knowledge repositories from biomedical reports: the MEDSYNDIKATE text mining system.

U Hahn — 2007-03-26T08:02:31-00:00

Pac Symp Biocomput (2002), pp. 338-349.

MEDSYNDIKATE is a natural language processor for automatically acquiring knowledge from medical finding reports. The content of these documents is transferred to formal representation structures which constitute a corresponding text knowledge base. The system architecture integrates requirements from the analysis of single sentences, as well as those of referentially linked sentences forming cohesive texts. The strong demands MEDSYNDIKATE poses to the availability of expressive knowledge sources are accounted for by two alternative approaches to (semi)automatic ontology engineering. We also present data for the knowledge extraction performance of MEDSYNDIKATE for three major syntactic patterns in medical documents.