Searching the literature and selecting the right references.
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The ability to locate published data on a topic is a fundamental skill in the research process, and it aids in formulating and refining a research question and planning the study. Searching the literature for published studies on a topic relevant to one's question requires knowledge of databases such as MEDLINE, Cumulative Index to Nursing and Allied Health, or Hospital Literature Index. PubMed provides access to MEDLINE and over 12 million citations in the medical literature. When searching in PubMed you can apply various "limits," such as what fields the search term is in (eg, author, title, text word, journal), type of report (eg, clinical trial, review, editorial), language, patient age, gender, and human or animal study. The "Boolean operators" (AND, OR, and NOT) can further focus and refine your search. However, to be sure that you retrieve all the files of interest and don't miss any files that might be critical to your understanding of the topic, you must search all fields and be careful not to exclude potentially important files with the NOT operator. (+info)
Content-rich biological network constructed by mining PubMed abstracts.
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BACKGROUND: The integration of the rapidly expanding corpus of information about the genome, transcriptome, and proteome, engendered by powerful technological advances, such as microarrays, and the availability of genomic sequence from multiple species, challenges the grasp and comprehension of the scientific community. Despite the existence of text-mining methods that identify biological relationships based on the textual co-occurrence of gene/protein terms or similarities in abstract texts, knowledge of the underlying molecular connections on a large scale, which is prerequisite to understanding novel biological processes, lags far behind the accumulation of data. While computationally efficient, the co-occurrence-based approaches fail to characterize (e.g., inhibition or stimulation, directionality) biological interactions. Programs with natural language processing (NLP) capability have been created to address these limitations, however, they are in general not readily accessible to the public. RESULTS: We present a NLP-based text-mining approach, Chilibot, which constructs content-rich relationship networks among biological concepts, genes, proteins, or drugs. Amongst its features, suggestions for new hypotheses can be generated. Lastly, we provide evidence that the connectivity of molecular networks extracted from the biological literature follows the power-law distribution, indicating scale-free topologies consistent with the results of previous experimental analyses. CONCLUSIONS: Chilibot distills scientific relationships from knowledge available throughout a wide range of biological domains and presents these in a content-rich graphical format, thus integrating general biomedical knowledge with the specialized knowledge and interests of the user. Chilibot http://www.chilibot.net can be accessed free of charge to academic users. (+info)
MedKit: a helper toolkit for automatic mining of MEDLINE/PubMed citations.
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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: [email protected]. (+info)
The lack of public health research output from India.
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BACKGROUND: Systematic assessment of recent health research output from India, and its relation with the estimated disease burden, is not available. This information would help understand the areas in health research that need improvement in India to enhance the health of India's population. METHODS: The health research output from India during 2002, which was accessible in the public domain, was assessed by searching PubMed and other internet health literature databases, and was related to the disease burden suggested by the Global Burden of Disease Study. The main outcome measures were number of health papers with abstracts in basic, clinical and public health sciences; quality-adjusted research output based on the impact factors of journals in which the papers were published; classification of papers in disease/condition categories and comparison of research output with the estimated disease burden in each category. Comparison of the health papers from India during 2002 included in PubMed was done with those from Australia during one quarter of 2002. RESULTS: Of the 4876 health papers from India in 2002 in PubMed, 48.4%, 47.1% and 4.4% were in basic, clinical and public health sciences, respectively. Of the 4495 papers based on original research, only 3.3% were in public health. Quality-adjusted original research output was highest for non-communicable diseases (62% of total). Of the total quality-adjusted original research output, the proportions in injuries (0.7%), cardiovascular diseases (3.6%), respiratory infections (0.2%), diarrhoeal diseases (1.9%), perinatal conditions (0.4%), childhood cluster diseases (0.5%), unipolar major depression (0%), and HIV/AIDS (1.5%) were substantially lower than their proportional contribution to the disease burden in India. Human resources, health policy, health economics, and impact assessment of interventions were particularly poorly represented in public health research. The Australia-India ratio for quality-adjusted health research output per unit gross domestic product was 20 and for public health research output was 31. CONCLUSIONS: Good-quality public health research output from India is grossly inadequate, and strategic planning to improve it is necessary if substantial enhancement of population health were to be made possible. There is inordinately low relative research output in several diseases/conditions that cause major disease burden in India. (+info)
Finding the evidence: resources and skills for locating information on clinical effectiveness.
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Limited time and lack of knowledge about where and how to search for information often present barriers to practitioners who want to locate current best evidence for treating their patients. There is as yet no single place they can go to get an answer to all their questions. High quality clinical studies are difficult to filter out from the mass of information on large databases, and secondary resources of evaluated information are dispersed over hundreds of Internet sites worldwide. This overview presents a practical guide for the busy practitioner who searches only occasionally and needs to maximise the time spent. Major collections of secondary resources are identified and their individual features described briefly. Following this, several services using PubMed are outlined that automatically apply filters for studies with high quality research design. Further sources of information and assistance are listed for those who wish to learn more. (+info)
HCAD, closing the gap between breakpoints and genes.
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Recurrent chromosome aberrations are an important resource when associating human pathologies to specific genes. However, for technical reasons a large number of chromosome breakpoints are defined only at the level of cytobands and many of the genes involved remain unidentified. We developed a web-based information system that mines the scientific literature and generates textual and comprehensive information on all human breakpoints. We show that the statistical analysis of this textual information and its combination with genomic data can identify genes directly involved in DNA rearrangements. The Human Chromosome Aberration Database (HCAD) is publicly accessible at http://www.pdg.cnb.uam.es/UniPub/HCAD/. (+info)
Systematic review of early recurrent stenosis after carotid angioplasty and stenting.
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BACKGROUND AND PURPOSE: Carotid angioplasty and stenting (CAS) has emerged as a potential alternative to endarterectomy (CEA) for the treatment of carotid artery disease. Aside from the periprocedural complication rates, the benefits of CAS will be affected by the incidence of recurrent carotid stenosis. METHODS: We conducted a systematic analysis of all peer-reviewed studies reporting on the rate of restenosis (> or =50%) after CAS based on duplex ultrasound or angiography that were published between January 1990 and July 2004. We identified 34 studies that reported on a total of 4185 patients with a follow-up of 3814 arteries over a median of 13 months (range, 6 to 31 months). The ultrasound criteria and the lower thresholds for defining a recurrent stenosis were very heterogeneous. RESULTS: The cumulative restenosis rates after 1 and 2 years were approximately 6% and 7.5% in those studies, which used a lower restenosis threshold > or =50% to 70% and approximately 4% in the first 2 years after CAS in those studies, which used a lower restenosis threshold >70% to 80%. CONCLUSIONS: In reviewing the current literature, the early restenosis rates after CAS compare well with those reported for CEA. However, this analysis of the peer-reviewed literature also indicates that the early restenosis rates after CAS might be higher than previously suggested in observational surveys. Therefore, an active follow-up of all stented arteries seems to be warranted. Moreover, the bulk of endovascular data are derived from small studies with short follow-up periods so that the long-term durability of CAS still needs to be established in large trials. Ideally, these studies should use a clear and uniform definition of restenosis and identical follow-up schedules. (+info)
Co-occurrence based meta-analysis of scientific texts: retrieving biological relationships between genes.
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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. (+info)