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Related Experiment Video

Updated: Jul 5, 2026

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

New challenges for text mining: mapping between text and manually curated pathways.

Kanae Oda¹, Jin-Dong Kim, Tomoko Ohta

¹Department of Computer Science, Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan. k.oda@is.s.u-tokyo.ac.jp

BMC Bioinformatics

|May 9, 2008

Summary

This summary is machine-generated.

This study addresses challenges in linking scientific literature to biological pathways. New resources and analysis reveal nine classes of biological inferences, aiding pathway construction from text.

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Area of Science:

Computational Biology
Bioinformatics
Natural Language Processing

Background:

Text mining (TM) faces challenges in associating literature with biological pathways.
Key challenges include mapping entities/reactions, recognizing causal relationships, and implementing biological domain knowledge for inferences.

Purpose of the Study:

To develop resources and methods for linking scientific text to biological pathways.
To analyze the differences between text and pathway representations and identify 'bio-inference' schemes.

Main Methods:

Construction of the GENIA pathway corpus with event annotation.
Development of the NF-kB pathway model.
Detailed analysis of text and pathway representations.

Main Results:

Introduction of new resources for text-pathway linkage.
Identification and categorization of nine classes of 'bio-inference' schemes.
Precise comparisons of text and pathway representations.

Conclusions:

The developed resources and analysis represent a significant advancement for automatic pathway construction from text.
This work facilitates future research in text mining for biological pathway generation.