EAPSI: Investigating a Novel Approach for Biological Named Entity Recognition in Text Mining

Shvets Dahlia E, Charlotte NC

Investigators

Abstract

Currently, using computational resources to extract information from biological literature faces many challenges due to the domain specific terminology of biological texts. In order to read and obtain information from the vast amount of available biological literature, a large amount of funds, time and manpower are required. The amount of research literature and data is always increasing, and a scalable solution to gleaning information from biological text is necessary. This project aims to develop a new technique for extracting specific annotations from biological literature. For this project, the PI will travel to Academia Sinica in Taipei, Taiwan to work with Dr. WenLian Hsu, whose expertise in the areas of natural language processing and text mining of biological literature is necessary for the implementation of this approach. DNA methylation is regarded as a potential biomarker in the diagnosis and treatment of cancer. Recent studies have identified relationships between aberrant gene methylation and cancer development. In previous approaches of mining biological literature, a combination of manual curation and machine learning-based approaches have been used to extract gene methylation cancer relations. This project will utilize a novel statistical pattern based approach which focuses on identifying deep and important linguistic patterns described for disease terms and physical interactions. This approach has a unique flexible matching mechanism that captures existing disease terms or protein names within the text, regardless of their previous presence in the training data. This project provides a solution to the resource limitation of reading and analyzing large amounts of biological literature in a short amount of time. This award under the East Asia and Pacific Summer Institutes program supports summer research by a U.S. graduate student and is jointly funded by NSF and the Ministry of Science and Technology of Taiwan.