全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Literature Mining Solutions for Life Science Research

DOI: 10.1155/2013/320436

Full-Text   Cite this paper   Add to My Lib

Abstract:

Research and development in the area of biomedical literature analysis aims at providing life science researchers with effective means to access and exploit knowledge contained in scientific publications. Virtually all journal publications and many conference proceedings are nowadays readily available in an electronic form—for instance, as abstracts through the MEDLINE citation index or as full-text articles through PubMed Central. Nevertheless, keeping up to date with and searching for recent findings in a research domain remains a tedious task hampered by inefficient and ineffective means for access and exploitation. Biomedical text analysis aims to improve access to unstructured knowledge by alleviating searches, providing auto generated summaries of documents and topics, linking and integrating publications with structured resources, visualizing content for better understanding, and guiding researchers to novel hypotheses and into knowledge discovery. Focused research over recent years has improved fundamental solutions for biomedical text mining, such as document retrieval, named entity recognition, normalization and grounding, and extraction of relationships, with levels of accuracy that reach human annotators when considering inter annotator agreement. Consequently, more and more integrative analysis tools were put forward by the text mining community targeting a broad audience of end users: generic and task-specific search engines for life science researchers, interfaces for networks synthesis based on textual evidences, or more specialized tools searching for transcription factors, or primer sequences. This special issue of Advances in Bioinformatics presents overviews and examples of end-user-oriented biomedical text mining tools for bioinformaticians, molecular biologists, biochemists, clinicians, pharmacologists, and other researchers in life sciences. We start with A. Manconi et al. survey on “Literature retrieval and mining in bioinformatics: state of the art and challenges.” The authors introduce the major concepts that life science researchers should be familiar with getting the best out of existing text mining solutions, and survey key tools and research. In a dedicated second part of their survey, the authors address the major challenges both life science researchers and solution developers are facing at this point. The reader will find plenty of references to existing search tools, resources, and research papers. A. E. Thessen et al. focus on a particular domain, presenting an overview of “Applications of natural language processing

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133