Online coverage of the literature on thermal physiology

Online searches for research topics in thermal physiology usually do not retrieve more than 40% of the existing publications. In order to determine whether this low retrieval rate is due to deficient coverage of the literature or to problems in storage and retrieval of information, a list of 25 major papers, 25 minor papers and 15 obscure papers on thermal physiology was compiled. The 65 documents were searched online in the MEDLINE database. Retrieval rate was more than 60% for obscure papers, 80% for minor papers, and 90% for major papers. It is concluded that MEDLINE has an appropriate coverage of the literature on thermal physiology and that the low retrieval rate in online searches is due mostly to problems in information processing.     

Computing fuzzy associations for the analysis of biological literature

The increase of information in biology makes it difficult for researchers in any field to keep current with the literature. The MEDLINE database of scientific abstracts can be quickly scanned using electronic mechanisms. Potentially interesting abstracts can be selected by matching words joined by Boolean operators. However this means of selecting documents is not optimal. Nonspecific queries have to be effected, resulting in large numbers of irrelevant abstracts that have to be manually scanned To facilitate this analysis, we have developed a system that compiles a summary of subjects and related documents on the results of a MEDLINE query. For this, we have applied a fuzzy binary relation formalism that deduces relations between words present in a set of abstracts preprocessed with a standard grammatical tagger. Those relations are used to derive ensembles of related words and their associated subsets of abstracts. The algorithm can be used publicly at http:// www.bork.embl-heidelberg.de/xplormed/.     

Gene clustering by latent semantic indexing of MEDLINE abstracts

MOTIVATION: A major challenge in the interpretation of high-throughput genomic data is understanding the functional associations between genes. Previously, several approaches have been described to extract gene relationships from various biological databases using term-matching methods. However, more flexible automated methods are needed to identify functional relationships (both explicit and implicit) between genes from the biomedical literature. In this study, we explored the utility of Latent Semantic Indexing (LSI), a vector space model for information retrieval, to automatically identify conceptual gene relationships from titles and abstracts in MEDLINE citations. RESULTS: We found that LSI identified gene-to-gene and keyword-to-gene relationships with high average precision. In addition, LSI identified implicit gene relationships based on word usage patterns in the gene abstract documents. Finally, we demonstrate here that pairwise distances derived from the vector angles of gene abstract documents can be effectively used to functionally group genes by hierarchical clustering. Our results provide proof-of-principle that LSI is a robust automated method to elucidate both known (explicit) and unknown (implicit) gene relationships from the biomedical literature. These features make LSI particularly useful for the analysis of novel associations discovered in genomic experiments. AVAILABILITY: The 50-gene document collection used in this study can be interactively queried at http://shad.cs.utk.edu/sgo/sgo.html.     

Clustering more than two million biomedical publications: comparing the accuracies of nine text-based similarity approaches

Background

We investigate the accuracy of different similarity approaches for clustering over two million biomedical documents. Clustering large sets of text documents is important for a variety of information needs and applications such as collection management and navigation, summary and analysis. The few comparisons of clustering results from different similarity approaches have focused on small literature sets and have given conflicting results. Our study was designed to seek a robust answer to the question of which similarity approach would generate the most coherent clusters of a biomedical literature set of over two million documents.

Methodology

We used a corpus of 2.15 million recent (2004-2008) records from MEDLINE, and generated nine different document-document similarity matrices from information extracted from their bibliographic records, including titles, abstracts and subject headings. The nine approaches were comprised of five different analytical techniques with two data sources. The five analytical techniques are cosine similarity using term frequency-inverse document frequency vectors (tf-idf cosine), latent semantic analysis (LSA), topic modeling, and two Poisson-based language models – BM25 and PMRA (PubMed Related Articles). The two data sources were a) MeSH subject headings, and b) words from titles and abstracts. Each similarity matrix was filtered to keep the top-n highest similarities per document and then clustered using a combination of graph layout and average-link clustering. Cluster results from the nine similarity approaches were compared using (1) within-cluster textual coherence based on the Jensen-Shannon divergence, and (2) two concentration measures based on grant-to-article linkages indexed in MEDLINE.

Conclusions

PubMed''s own related article approach (PMRA) generated the most coherent and most concentrated cluster solution of the nine text-based similarity approaches tested, followed closely by the BM25 approach using titles and abstracts. Approaches using only MeSH subject headings were not competitive with those based on titles and abstracts.     

GenBank.

The GenBank sequence database has undergone an expansion in data coverage, annotation content and the development of new services for the scientific community. In addition to nucleotide sequences, data from the major protein sequence and structural databases, and from U.S. and European patents is now included in an integrated system. MEDLINE abstracts from published articles describing the sequences provide an important new source of biological annotation for sequence entries. In addition to the continued support of existing services, new CD-ROM and network-based systems have been implemented for literature retrieval and sequence similarity searching. Major releases of GenBank are now more frequent and the data are distributed in several new forms for both end users and software developers.     

Suregene, a scalable system for automated term disambiguation of gene and protein names

Researchers, hindered by a lack of standard gene and protein-naming conventions, endure long, sometimes fruitless, literature searches. A system that is able to automatically assign gene names to their LocusLink ID (LLID) in previously unseen MEDLINE abstracts is described. The system is based on supervised learning and builds a model for each LLID. The training sets for all LLIDs are extracted automatically from MEDLINE references in the LocusLink and SwissProt databases. A validation was done of the performance for all 20,546 human genes with LLIDs. Of these, 7344 produced good quality models (F-measure >0.7, nearly 60% of which were >0.9) and 13,202 did not, mainly due to insufficient numbers of known document references. A hand validation of MEDLINE documents for a set of 66 genes agreed well with the system's internal accuracy assessment. It is concluded that it is possible to achieve high quality gene disambiguation using scaleable automated techniques.     

Retrieval with gene queries

Background  

Accuracy of document retrieval from MEDLINE for gene queries is crucially important for many applications in bioinformatics. We explore five information retrieval-based methods to rank documents retrieved by PubMed gene queries for the human genome. The aim is to rank relevant documents higher in the retrieved list. We address the special challenges faced due to ambiguity in gene nomenclature: gene terms that refer to multiple genes, gene terms that are also English words, and gene terms that have other biological meanings.     

Tagging gene and protein names in biomedical text

MOTIVATION: The MEDLINE database of biomedical abstracts contains scientific knowledge about thousands of interacting genes and proteins. Automated text processing can aid in the comprehension and synthesis of this valuable information. The fundamental task of identifying gene and protein names is a necessary first step towards making full use of the information encoded in biomedical text. This remains a challenging task due to the irregularities and ambiguities in gene and protein nomenclature. We propose to approach the detection of gene and protein names in scientific abstracts as part-of-speech tagging, the most basic form of linguistic corpus annotation. RESULTS: We present a method for tagging gene and protein names in biomedical text using a combination of statistical and knowledge-based strategies. This method incorporates automatically generated rules from a transformation-based part-of-speech tagger, and manually generated rules from morphological clues, low frequency trigrams, indicator terms, suffixes and part-of-speech information. Results of an experiment on a test corpus of 56K MEDLINE documents demonstrate that our method to extract gene and protein names can be applied to large sets of MEDLINE abstracts, without the need for special conditions or human experts to predetermine relevant subsets. AVAILABILITY: The programs are available on request from the authors.     

Terminology-driven mining of biomedical literature

MOTIVATION: With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective literature mining techniques that can help biologists to gather and make use of the knowledge encoded in text documents. Although the knowledge is organized around sets of domain-specific terms, few literature mining systems incorporate deep and dynamic terminology processing. RESULTS: In this paper, we present an overview of an integrated framework for terminology-driven mining from biomedical literature. The framework integrates the following components: automatic term recognition, term variation handling, acronym acquisition, automatic discovery of term similarities and term clustering. The term variant recognition is incorporated into terminology recognition process by taking into account orthographical, morphological, syntactic, lexico-semantic and pragmatic term variations. In particular, we address acronyms as a common way of introducing term variants in biomedical papers. Term clustering is based on the automatic discovery of term similarities. We use a hybrid similarity measure, where terms are compared by using both internal and external evidence. The measure combines lexical, syntactical and contextual similarity. Experiments on terminology recognition and clustering performed on a corpus of MEDLINE abstracts recorded the precision of 98 and 71% respectively. AVAILABILITY: software for the terminology management is available upon request.     

GenBank.

The GenBank (Registered Trademark symbol) sequence database incorporates DNA sequences from all available public sources, primarily through the direct submission of sequence data from individual laboratories and from large-scale sequencing projects. Most submitters use the BankIt (Web) or Sequin programs to format and send sequence data. Data exchange with the EMBL Data Library and the DNA Data Bank of Japan helps ensure comprehensive worldwide coverage. GenBank data is accessible through NCBI's integrated retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome and protein structure information. MEDLINE (Registered Trademark symbol) s from published articles describing the sequences are included as an additional source of biological annotation through the PubMed search system. Sequence similarity searching is offered through the BLAST series of database search programs. In addition to FTP, Email, and server/client versions of Entrez and BLAST, NCBI offers a wide range of World Wide Web retrieval and analysis services based on GenBank data. The GenBank database and related resources are freely accessible via the URL: http://www.ncbi.nlm.nih.gov     

Anni 2.0: a multipurpose text-mining tool for the life sciences

Anni 2.0 is an online tool () to aid the biomedical researcher with a broad range of information needs. Anni provides an ontology-based interface to MEDLINE and retrieves documents and associations for several classes of biomedical concepts, including genes, drugs and diseases, with established text-mining technology. In this article we illustrate Anni's usability by applying the tool to two use cases: interpretation of a set of differentially expressed genes, and literature-based knowledge discovery.     

Patient-Reported Outcome (PRO) Assessment in Clinical Trials: A Systematic Review of Guidance for Trial Protocol Writers

Background

Evidence suggests there are inconsistencies in patient-reported outcome (PRO) assessment and reporting in clinical trials, which may limit the use of these data to inform patient care. For trials with a PRO endpoint, routine inclusion of key PRO information in the protocol may help improve trial conduct and the reporting and appraisal of PRO results; however, it is currently unclear exactly what PRO-specific information should be included. The aim of this review was to summarize the current PRO-specific guidance for clinical trial protocol developers.

Methods and Findings

We searched the MEDLINE, EMBASE, CINHAL and Cochrane Library databases (inception to February 2013) for PRO-specific guidance regarding trial protocol development. Further guidance documents were identified via Google, Google scholar, requests to members of the UK Clinical Research Collaboration registered clinical trials units and international experts. Two independent investigators undertook title/abstract screening, full text review and data extraction, with a third involved in the event of disagreement. 21,175 citations were screened and 54 met the inclusion criteria. Guidance documents were difficult to access: electronic database searches identified just 8 documents, with the remaining 46 sourced elsewhere (5 from citation tracking, 27 from hand searching, 7 from the grey literature review and 7 from experts). 162 unique PRO-specific protocol recommendations were extracted from included documents. A further 10 PRO recommendations were identified relating to supporting trial documentation. Only 5/162 (3%) recommendations appeared in ≥50% of guidance documents reviewed, indicating a lack of consistency.

Conclusions

PRO-specific protocol guidelines were difficult to access, lacked consistency and may be challenging to implement in practice. There is a need to develop easily accessible consensus-driven PRO protocol guidance. Guidance should be aimed at ensuring key PRO information is routinely included in appropriate trial protocols, in order to facilitate rigorous collection/reporting of PRO data, to effectively inform patient care.     

GenBank.

The GenBank sequence database continues to expand its data coverage, quality control, annotation content and retrieval services. GenBank is comprised of DNA sequences submitted directly by authors as well as sequences from the other major public databases. An integrated retrieval system, known as Entrez, contains data from GenBank and from the major protein sequence and structural databases, as well as related MEDLINE abstracts. Users may access GenBank over the Internet through the World Wide Web and through special client-server programs for text and sequence similarity searching. FTP, CD-ROM and e-mail servers are alternate means of access.     

GenBank.

The GenBank sequence database incorporates DNA sequences from all available public sources, primarily through the direct submission of sequence data from authors and from large-scale sequencing projects. Data exchange with the EMBL Data Library and the DNA Data Bank of Japan helps ensure comprehensive coverage. GenBank continues to focus on quality control and annotation while expanding data coverage and retrieval services. An integrated retrieval system, known asEntrez, incorporates data from the major DNA and protein sequence databases, along with genome maps and protein structure information. MEDLINE abstracts from published articles describing the sequences are also included as an additional source of biological annotation. Sequence similarity searching is offered through the BLAST family of programs. All of NCBI's services are offered through the World Wide Web. In addition, there are specialized server/client versions as well as FTP and e-mail server access.     

GenBank

GenBank (R) is a comprehensive sequence database that contains publicly available DNA sequences for more than 119 000 different organisms, obtained primarily through the submission of sequence data from individual laboratories and batch submissions from large-scale sequencing projects. Most submissions are made using the BankIt (web) or Sequin programs and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the EMBL Data Library in the UK and the DNA Data Bank of Japan helps ensure worldwide coverage. GenBank is accessible through NCBI's retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, go to the NCBI home page at: http://www.ncbi.nlm.nih.gov.     

Teaching human genetics in biochemistry by computer literature searching.

We describe a new user-intense-learning experience that incorporates the teaching of clinical and research applications of human genetics in biochemistry while training first-year medical students to develop skills in computer access to the literature. Human genetics was incorporated into the biochemistry curriculum by providing each student with experience in on-line literature searching in MEDLINE, using Grateful Med, in order to write an abstract about a specific inherited biochemical disorder. We stressed the need for the students to obtain current information in order to understand and interpret the rapidly changing field of human genetics. We taught the students that the most efficient method of obtaining such information was by searching the medical literature via computer.     

Textmining in support of knowledge discovery for vaccine development

Complete genome data of infectious microorganisms permit systematic computational sequence-based predictions and experimental testing of candidate vaccine epitopes. Both, predictions and the interpretation of experiments rely on existing information in the literature which is mostly manually extracted and curated. The growing amount of data and literature information has created a major bottleneck for the interpretation of results and maintenance of curated databases. The lack of suitable free-text information extraction, processing, and reporting tools prompted us to develop a knowledge discovery support system that enhances the understanding of immune response and vaccine development. The current prototype system, Gene expression/epitpopes/protein interaction (GEpi), focuses on molecular functions of HIV-infected T-cells and HIV epitope information, using textmining, and interrelation of biomolecular data from domain-specific databases with MEDLINE abstract-inferred information. Results showed that extraction and processing of molecular interaction, disease associations, and gene ontology-derived functional information generate intuitive knowledge reports that aid the interpretation of host-pathogen interaction. In contrast, epitope (word and sequence) information in MEDLINE abstracts is surprisingly sparse and often lacks necessary context information, such as HLA-restriction. Since the majority of epitope information is found in tables, figures, and legends of full-text articles, its extraction may not require sophisticated natural language processing techniques. Support of vaccine development through textmining requires therefore the timely development of domain-specific extraction rules for full-text articles, and a knowledge model for epitope-related information.     

Hairpins in bookstacks: information retrieval from biomedical text

Current advances in high-throughput biology are accompanied by a tremendous increase in the number of related publications. Much biomedical information is reported in the vast amount of literature. The ability to rapidly and effectively survey the literature is necessary for both the design and the interpretation of large-scale experiments, and for curation of structured biomedical knowledge in public databases. Given the millions of published documents, the field of information retrieval, which is concerned with the automatic identification of relevant documents from large text collections, has much to offer. This paper introduces the basics of information retrieval, discusses its applications in biomedicine, and presents traditional and non-traditional ways in which it can be used.     

GenBank.

The GenBank(R) sequence database (http://www.ncbi.nlm.nih.gov/) incorporates DNA sequences from all available public sources, primarily through the direct submission of sequence data from individual laboratories and from large-scale sequencing projects. Most submitters use the BankIt (WWW) or Sequin programs to send their sequence data. Data exchange with the EMBL Data Library and the DNA Data Bank of Japan helps ensure comprehensive worldwide coverage. GenBank data is accessible through NCBI's integrated retrieval system, Entrez , which integrates data from the major DNA and protein sequence databases along with taxonomy, genome and protein structure information. MEDLINE(R) abstracts from published articles describing the sequences are also included as an additional source of biological annotation. Sequence similarity searching is offered through the BLAST series of database search programs. In addition to FTP, e-mail and server/client versions of Entrez and BLAST, NCBI offers a wide range of World Wide Web retrieval and analysis services of interest to biologists.     

XplorMed: a tool for exploring MEDLINE abstracts

The most frequent access to the MEDLINE database of scientific abstracts is by keyword search. However, this is often not sufficient because although the user might find all the useful abstracts, these are buried in hundreds that are irrelevant. The exploratory tool XplorMed has been developed to analyse the result of any MEDLINE query. It suggests main groups of related topics and documents, sparing the user the need of reading all abstracts.