Toward Routine Automatic Pathway Discovery from On-line Scientific Text Abstracts

See-Kiong Ng[1] (skng@krdl.org.sg)
Marie Wong[2] (marie@bic.nus.edu.sg)

[1] Kent Ridge Digital Labs
21 Heng Mui Keng Terrace, Singapore 119613
[2] NUS Bioinformatics Centre
National University of Singapore, Singapore 119260


Abstract

We are entering a new era of research where the latest scientific discoveries are often first reported online and are readily accessible by scientists worldwide. This rapid electronic dissemination of research breakthroughs has greatly accelerated the current pace in genomics and proteomics research. The race to the discovery of a gene or a drug has now become increasingly dependent on how quickly a scientist can scan through voluminous amount of information available online to construct the relevant picture (such as protein-protein interaction pathways) as it takes shape amongst the rapidly expanding pool of globally accessible biological data (e.g. GENBANK) and scientific literature (e.g. MEDLINE).

We describe a prototype system for automatic pathway discovery from on-line text abstracts, combining technologies that (1) retrieve research abstracts from online sources, (2) extract relevant information from the free texts, and (3) present the extracted information graphically and intuitively. Our work demonstrates that this framework allows us to routinely scan online scientific literature for automatic discovery of knowledge, giving modern scientists the necessary competitive edge in managing the information explosion in this electronic age.

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Japanese Society for Bioinformatics