A comparison and user-based evaluation of models of textual information structure in the context of cancer risk assessment

Background  

Many practical tasks in biomedicine require accessing specific types of information in scientific literature; e.g. information about the results or conclusions of the study in question. Several schemes have been developed to characterize such information in scientific journal articles. For example, a simple section-based scheme assigns individual sentences in abstracts under sections such as Objective, Methods, Results and Conclusions. Some schemes of textual information structure have proved useful for biomedical text mining (BIO-TM) tasks (e.g. automatic summarization). However, user-centered evaluation in the context of real-life tasks has been lacking.     

Information extraction from full text scientific articles: Where are the keywords?

Background  

To date, many of the methods for information extraction of biological information from scientific articles are restricted to the abstract of the article. However, full text articles in electronic version, which offer larger sources of data, are currently available. Several questions arise as to whether the effort of scanning full text articles is worthy, or whether the information that can be extracted from the different sections of an article can be relevant.     

Building a protein name dictionary from full text: a machine learning term extraction approach

Background  

The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature.     

Automating document classification for the Immune Epitope Database

Background  

The Immune Epitope Database contains information on immune epitopes curated manually from the scientific literature. Like similar projects in other knowledge domains, significant effort is spent on identifying which articles are relevant for this purpose.     

Word add-in for ontology recognition: semantic enrichment of scientific literature

Background  

In the current era of scientific research, efficient communication of information is paramount. As such, the nature of scholarly and scientific communication is changing; cyberinfrastructure is now absolutely necessary and new media are allowing information and knowledge to be more interactive and immediate. One approach to making knowledge more accessible is the addition of machine-readable semantic data to scholarly articles.     

Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery

Background  

Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication.     

Exploring supervised and unsupervised methods to detect topics in biomedical text

Background  

Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature.     

Challenges for automatically extracting molecular interactions from full-text articles

Background  

The increasing availability of full-text biomedical articles will allow more biomedical knowledge to be extracted automatically with greater reliability. However, most Information Retrieval (IR) and Extraction (IE) tools currently process only abstracts. The lack of corpora has limited the development of tools that are capable of exploiting the knowledge in full-text articles. As a result, there has been little investigation into the advantages of full-text document structure, and the challenges developers will face in processing full-text articles.     

CONDOR: a database resource of developmentally associated conserved non-coding elements

Background  

Comparative genomics is currently one of the most popular approaches to study the regulatory architecture of vertebrate genomes. Fish-mammal genomic comparisons have proved powerful in identifying conserved non-coding elements likely to be distal cis-regulatory modules such as enhancers, silencers or insulators that control the expression of genes involved in the regulation of early development. The scientific community is showing increasing interest in characterizing the function, evolution and language of these sequences. Despite this, there remains little in the way of user-friendly access to a large dataset of such elements in conjunction with the analysis and the visualization tools needed to study them.     

MSDmotif: exploring protein sites and motifs

Background  

Protein structures have conserved features – motifs, which have a sufficient influence on the protein function. These motifs can be found in sequence as well as in 3D space. Understanding of these fragments is essential for 3D structure prediction, modelling and drug-design. The Protein Data Bank (PDB) is the source of this information however present search tools have limited 3D options to integrate protein sequence with its 3D structure.     

A case study in pathway knowledgebase verification

Background  

Biological databases and pathway knowledgebases are proliferating rapidly. We are developing software tools for computer-aided hypothesis design and evaluation, and we would like our tools to take advantage of the information stored in these repositories. But before we can reliably use a pathway knowledgebase as a data source, we need to proofread it to ensure that it can fully support computer-aided information integration and inference.     

An ontology for <Emphasis Type="Italic">Xenopus</Emphasis> anatomy and development

Background  

The frogs Xenopus laevis and Xenopus (Silurana) tropicalis are model systems that have produced a wealth of genetic, genomic, and developmental information. Xenbase is a model organism database that provides centralized access to this information, including gene function data from high-throughput screens and the scientific literature. A controlled, structured vocabulary for Xenopus anatomy and development is essential for organizing these data.     

Moara: a Java library for extracting and normalizing gene and protein mentions

Background  

Gene/protein recognition and normalization are important preliminary steps for many biological text mining tasks, such as information retrieval, protein-protein interactions, and extraction of semantic information, among others. Despite dedication to these problems and effective solutions being reported, easily integrated tools to perform these tasks are not readily available.     

The Unbearable Lightness of Health Science Reporting: A Week Examining Italian Print Media

Background

Although being an important source of science news information to the public, print news media have often been criticized in their credibility. Health-related content of press media articles has been examined by many studies underlining that information about benefits, risks and costs are often incomplete or inadequate and financial conflicts of interest are rarely reported. However, these studies have focused their analysis on very selected science articles. The present research aimed at adopting a wider explorative approach, by analysing all types of health science information appearing on the Italian national press in one-week period. Moreover, we attempted to score the balance of the articles.

Methodology/Principal Findings

We collected 146 health science communication articles defined as articles aiming at improving the reader''s knowledge on health from a scientific perspective. Articles were evaluated by 3 independent physicians with respect to different divulgation parameters: benefits, costs, risks, sources of information, disclosure of financial conflicts of interest and balance. Balance was evaluated with regard to exaggerated or non correct claims. The selected articles appeared on 41 Italian national daily newspapers and 41 weekly magazines, representing 89% of national circulation copies: 97 articles (66%) covered common medical treatments or basic scientific research and 49 (34%) were about new medical treatments, procedures, tests or products. We found that only 6/49 (12%) articles on new treatments, procedures, tests or products mentioned costs or risks to patients. Moreover, benefits were always maximized and in 16/49 cases (33%) they were presented in relative rather than absolute terms. The majority of stories (133/146, 91%) did not report any financial conflict of interest. Among these, 15 were shown to underreport them (15/146, 9.5%), as we demonstrated that conflicts of interest did actually exist. Unbalanced articles were 27/146 (18%). Specifically, the probability of unbalanced reporting was significantly increased in stories about a new treatment, procedure, test or product (22/49, 45%), compared to stories covering common treatments or basic scientific research (5/97, 5%) (risk ratio, 8.72).

Conclusions/Significance

Consistent with prior research on health science communication in other countries, we report undisclosed costs and risks, emphasized benefits, unrevealed financial conflicts of interest and exaggerated claims in Italian print media. In addition, we show that the risk for a story about a new medical approach to be unbalanced is almost 9 times higher with respect to stories about any other kind of health science-related topics. These findings raise again the fundamental issue whether popular media is detrimental rather than useful to public health.     

Critical evaluation of the JDO API for the persistence and portability requirements of complex biological databases

Background  

Complex biological database systems have become key computational tools used daily by scientists and researchers. Many of these systems must be capable of executing on multiple different hardware and software configurations and are also often made available to users via the Internet. We have used the Java Data Object (JDO) persistence technology to develop the database layer of such a system known as the SigPath information management system. SigPath is an example of a complex biological database that needs to store various types of information connected by many relationships.     

Knowledge diffusion and collaboration networks on life cycle assessment

Purpose  

The interest in life cycle assessment (LCA) studies has increased over the years, and one of the main ways of disseminating these studies is through the publication of articles in scientific journals. Coauthorship relations form a social network where it is possible to identify how research is organized and structured in a specific field of knowledge. This paper aims to show the spread of these studies and the configuration of a collaboration network based on coauthorship relations between researchers of LCA considering some properties of social networks.     

Discovering semantic features in the literature: a foundation for building functional associations

Background  

Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research.     

Genomic taxonomy of vibrios

Background  

Vibrio taxonomy has been based on a polyphasic approach. In this study, we retrieve useful taxonomic information (i. e. data that can be used to distinguish different taxonomic levels, such as species and genera) from 32 genome sequences of different vibrio species. We use a variety of tools to explore the taxonomic relationship between the sequenced genomes, including Multilocus Sequence Analysis (MLSA), supertrees, Average Amino Acid Identity (AAI), genomic signatures, and Genome BLAST atlases. Our aim is to analyse the usefulness of these tools for species identification in vibrios.     

Three options for citation tracking: Google Scholar,Scopus and Web of Science

Background

Researchers turn to citation tracking to find the most influential articles for a particular topic and to see how often their own published papers are cited. For years researchers looking for this type of information had only one resource to consult: the Web of Science from Thomson Scientific. In 2004 two competitors emerged – Scopus from Elsevier and Google Scholar from Google. The research reported here uses citation analysis in an observational study examining these three databases; comparing citation counts for articles from two disciplines (oncology and condensed matter physics) and two years (1993 and 2003) to test the hypothesis that the different scholarly publication coverage provided by the three search tools will lead to different citation counts from each.

Methods

Eleven journal titles with varying impact factors were selected from each discipline (oncology and condensed matter physics) using the Journal Citation Reports (JCR). All articles published in the selected titles were retrieved for the years 1993 and 2003, and a stratified random sample of articles was chosen, resulting in four sets of articles. During the week of November 7–12, 2005, the citation counts for each research article were extracted from the three sources. The actual citing references for a subset of the articles published in 2003 were also gathered from each of the three sources.

Results

For oncology 1993 Web of Science returned the highest average number of citations, 45.3. Scopus returned the highest average number of citations (8.9) for oncology 2003. Web of Science returned the highest number of citations for condensed matter physics 1993 and 2003 (22.5 and 3.9 respectively). The data showed a significant difference in the mean citation rates between all pairs of resources except between Google Scholar and Scopus for condensed matter physics 2003. For articles published in 2003 Google Scholar returned the largest amount of unique citing material for oncology and Web of Science returned the most for condensed matter physics.

Conclusion

This study did not identify any one of these three resources as the answer to all citation tracking needs. Scopus showed strength in providing citing literature for current (2003) oncology articles, while Web of Science produced more citing material for 2003 and 1993 condensed matter physics, and 1993 oncology articles. All three tools returned some unique material. Our data indicate that the question of which tool provides the most complete set of citing literature may depend on the subject and publication year of a given article.