Advances in computational and biological methods during the last decade have remarkably changed the scale of biomedical and genomic research. Current research on biomedicine and genomics is characterized by immense volume of data, accompanied by a tremendous increase in the number of gene and protein related publications. This wealth of information presents a major data-analysis challenge. Many of the relationships between biological entities are captured in scientific text articles where they are difficult to access and compute on. Our ultimate goal is to understand the complex biological interrelationships among all discovered genes and proteins.

In addition, biologists have an increasing need to review the results of their experiments in the light of this current knowledge. Experimental data sets are quite often very large as are the literature data sets that may hold relevant information. Therefore, the ability to rapidly and effectively survey the published literature constitutes a necessary and important step for both the design and the interpretation process of large-scale experiments. Given the millions of related publications, the field of Information Retrieval, which is concerned with the automatic identification of relevant documents from large text collections, has much to offer for biomedical and genomics researchers.

Biomedical text mining can be defined as the automatic extraction of information about genes, proteins and their functional relationships from biomedical text documents. It has emerged as a hybrid discipline on the edges of the fields of computer science, bioinformatics and computational linguistics. Most current research in text mining usually focuses on the development of specific functions or algorithms. One of major aims is to help biomedical researchers extract knowledge and semantics from the natural language biomedical literature to facilitate new discovery in a more efficient manner. Another major aim is to move beyond name-finding and document-ranking algorithms to develop text mining tools that help real users improve the quality and efficiency of their biomedical research.

The goal of this special issue is to bring together contributions from academic and industrial researchers to present the most innovative approaches to biomedical text retrieval and mining and, in particular, to discuss emerging related topics such as those mentioned above, and to determine how research in this area should be addressed.

Submissions will undergo the normal review process, and will be reviewed by three established researchers selected from a panel of reviewers formed for the special issue.

Submissions should be sent to jhuang@cs.yorku.ca. Detailed formatting instructions and templates are available from: https://www.inderscience.com/www/authorguide.pdf

Jimmy Huang, School of Information Technology, York University, Toronto, Canada. Email: jhuang@cs.yorku.ca
Charlie Clarke, School of Computer Science, Univ. of Waterloo, Waterloo, Canada. Email: claclark@plg.uwaterloo.ca

For more information please contact:

Professor Jimmy Huang
2005 TEL Building
School of Information Technology
York University, 4700 Keele Street
Toronto, Ontario
Canada M3J 1P3
Phone: +1-416-736 2100 ext 30149, Fax +1-416-736 5188, Email: jhuang@cs.yorku.ca