Biomedical Language Processing: What's Beyond PubMed?

Abstract

Exponential growth of the peer-reviewed literature and the breakdown of disciplinary boundaries heralded by genome-scale instruments have made it harder than ever for scientists to find and assimilate all the publications relevant to their research. The widespread adoption of title/abstract word search, primarily through the National Library of Medicine's PubMed system (http://www.ncbi.nlm.nih.gov/pubmed), was the first major change in the way bioscientists found relevant publications since the origin of Index Medicus in 1879. (Although it remains useful for locating pre-1966 literature (Hersh, 2003), Index Medicus ceased publication in 2004.) However, PubMed is only the beginning of a revolution in how scientists use the biomedical literature. Computational tools that classify documents, extract factual information, generate summaries, and generally process human language are providing powerful new tools for staying on top of the torrent of publications. The biomedical literature is growing at a double-exponential pace; over the last 20 years, the total size of MEDLINE (the database searched by PubMed) has grown at a ~4.2% compounded annual growth rate, and the number of new entries in MEDLINE each year has grown at a compounded annual growth rate of ~3.1% (see Figure 1). There are now more than 16,000,000 publications in MEDLINE; more than three million of those were published in the last 5 years alone. The number of MEDLINE entries with a 2005 publication date was 666,029-more than 1800 per day. Large as MEDLINE is, it captures only bibliographic information and abstracts. Electronic access to the full texts, including graphics and figures, is also on the rise, and sophisticated linkages between publications and data repositories or other supplementary materials increase the amount of information available still further. Although online full-text materials are increasingly prevalent, dramatic increases in subscription prices and decreases in library budgets have paradoxically decreased access for some researchers. Toll-free linking, where copyright owners allow free search but charge per view, is one approach to ameliorating this problem. An alternative strategy toward this goal is the recent establishment of a movement toward a new "Open Access" model of scientific publishing. On April 11, 2003, a group of individuals interested in promoting open access to the scientific literature drafted a statement of principles that is

Negotiating a Text Mining License for Faculty Researchers

“…10 With an estimated growth rate of 3.1 percent annually for PubMed Central, the US National Library of Medicine's repository, researchers struggle to master the new literature of their field using traditional methods. Yet much of the advancement of biological knowledge relies on the interplay of data created by protein, sequence, and expression studies and the communication of information and discoveries through nontextual and textual databases and published reports.…”

“…12 BNLP "encompasses the many computational tools and methods that take human--generated texts as input, generally applied to tasks such as information retrieval, document classification, information extraction, plagiarism detection, or literature--based discovery." 13 BNLP techniques accomplish many tasks usually performed manually by researchers, including enhancing access through expanded indexing of content or linkage to additional information, automating reviews of the literature, discovering new insights, and extracting meaning from text. 14 Text mining is just one tool in a larger BNLP toolbox of resources used to read, reason, and report findings in a way that connects data to information sources to speed discovery of new knowledge.…”

Temporal Expression Recognition for Cell Cycle Phase Concepts in Biomedical Literature

“…A vast collection of biomedical literature in PubMed/MEDLINE and other biomedical journal repositories is estimated to grow exponentially (Hunter and Cohen, 2006), as shown in Figure 2.…”

“…Building a specialized search engine exclusively to search biomedical literature using genes and small molecules as keywords could be very useful, for instance, for cancer researchers. (Hunter and Cohen, 2006) Our long term goal is to build a specialized search engine specific to cancer research. The system will retrieve articles from PubMed/MEDLINE and rank them according to their relevance.…”

BioExcom: Detection and Categorization of Speculative Sentences in Biomedical Literature

“…However, it is very difficult for a single researcher to keep abreast of all developments [9]. To face this challenge, many systems, based on different Natural Language Processing methods, have been built (for reviews, see [10,11]). …”