TEXT AND DATA MINING FOR BIOMEDICAL DISCOVERY- SESSION INTRODUCTION

Abstract

The biggest challenge for text and data mining is to truly impact the biomedical discovery process, enabling scientists to generate novel hypothesis to address the most crucial questions. Among a number of worthy submissions, we have selected six papers that exemplify advances in text and data mining methods that have a demonstrated impact on a wide range of applications. Work presented in this session includes data mining techniques applied to the discovery of 3-way genetic interactions and to the analysis of genetic data in the context of electronic medical records (EMRs), as well as an integrative approach that combines data from genetic (SNP) and transcriptomic (microarray) sources for clinical prediction. Text mining advances include a classification method to determine whether a published article contains pharmacological experiments relevant to drug-drug interactions, a fine-grained text mining approach for detecting the catalytic sites in proteins in the biomedical literature, and a method for automatically extending a taxonomy of health-related terms to integrate consumer-friendly synonyms for medical terminologies.