Identifying the Interaction between Genes and Gene Products Based on Frequently Seen Verbs in Medline Abstracts.

We have selected the most frequently seen verbs from raw texts made up of 1-million-words of Medline abstracts, and we were able to identify (or bracket) noun phrases contained in the corpus, with a precision rate of 90%. Then, based on the noun-phrase-bracketted corpus, we tried to find the subject and object terms for some frequently seen verbs in the domain. The precision rate of finding the right subject and object for each verb was about 73%. This task was only made possible because we were able to linguistically analyze (or parse) a large quantity of a raw corpus. Our approach will be useful for classifying genes and gene products and for identifying the interaction between them. It is the first step of our effort in building a genome-related thesaurus and hierarchies in a fully automatic way.

Developing NLP Tools for Genome Informatics: An Information Extraction Perspective.

Huge quantities of on-line medical texts such as Medline are available, and we would hope to extract useful information from these resources, as much as possible, hopefully in an automatic way, with the aid of computer technologies. Especially, recent advances in Natural Language Processing (NLP) techniques raise new challenges and opportunities for tackling genome-related on-line text; combining NLP techniques with genome informatics extends beyond the traditional realms of either technology to a variety of emerging applications. In this paper, we explain some of our current efforts for developing various NLP-based tools for tackling genome-related on-line documents for information extraction task.