Published online 12 December 2008 Nucleic Acids Research, 2009, Vol. 37, No. 3 771–777 doi:10.1093/nar/gkn986 Identifying and classifying biomedical perturbations in text Raul Rodriguez-Esteban*, Phoebe M. Roberts and Matthew E. Crawford Pfizer Research Technology Center, 620 Memorial Dr., Cambridge, MA 02139, USA Received September 11, 2008; Revised November 11, 2008; Accepted November 21, 2008 ABSTRACT Molecular perturbations provide a powerful toolset for biomedical researchers to scrutinize the contrib- utions of individual molecules in biological systems. Perturbations qualify the context of experimental results and, despite their diversity, share properties in different dimensions in ways that can be forma- lized. We propose a formal framework to describe and classify perturbations that allows accumulation of knowledge in order to inform the process of bio- medical scientific experimentation and target analysis. We apply this framework to develop a novel algorithm for automatic detection and charac- terization of perturbations in text and show its relevance in the study of gene–phenotype associa- tions and protein–protein interactions in diabetes and cancer. Analyzing perturbations introduces a novel view of the multivariate landscape of biologi- cal systems. INTRODUCTION In the early days of biological research, mutations that caused discernable phenotypes were the primary tool for understanding how a biological system worked—in the absence of a mutation a gene was invisible. Today, biolo- gists are armed with a whole arsenal of tools to regulate gene, mRNA, and protein abundance and activity, thereby promoting the discovery of mechanisms and how a system gone awry can lead to disease (1). Among these are tools for suppressing the activity of a gene or gene product (e.g. site-directed mutagenesis, RNA inter- ference, small molecule inhibitors) or enhancing activity (e.g. activating mutations or receptor agonist). Markedly diﬀerent approaches can be used to perturb biological systems with similar eﬀects. For instance, interfering with protein activity using small-molecule inhibitors should have a phenotype similar to reducing the abun- dance of the corresponding mRNA with anti-sense oligonucleotides (2). Likewise, similar responses are expected whether increases in intracellular protein concen- tration are achieved via an inducible promoter or by addi- tion of recombinant protein (3). As such, perturbations form the core of understanding how biological systems work, how diseases arise, and how they can be treated. Any serious attempt to analyze a biological process starts by identiﬁcation and characterization of perturba- tions that have been used in prior work. This task requires a framework that can be systematically applied and that is amenable to both manual and automatic means. Currently, there is no established categorization that suﬃciently represents the range of described experimental manipulations beyond high-level semantic and grammati- cal classiﬁcations (4,5) or description of techniques (6). For example, the closest concept we have found is ‘altered expression,’ deﬁned as ‘altered expression level of a gene/ protein’ (7). We believe that this concept is overly speciﬁc and fails to cover important phenomena, among others, changes in protein activity or gene mutations. We pro- pose, instead, taking the existing concept of ‘perturbation’ and broadening it to comprise the range of terms used in text to indicate changes in the abundance or activity of DNA, RNA and proteins. Perturbations, in this new for- mulation, would refer to a collection of phenomena in a manner analogous to the way protein–protein interactions refer to biological phenomena of diﬀerent type (e.g. bind, activate, inhibit). Since this proposition, like any other, needs to be tested for validity and utility, we have applied it to a case study involving gene–phenotype associations in disease and have developed a mining algorithm that detects the diverse forms in which perturbations appear in text. Therefore, we are introducing in this work both a new way to understand a crucial part of biology and a new text-mining method tailored to its extraction. MATERIALS AND METHODS We created three corpora that we named ‘design’, ‘test’ and ‘analysis’. As initial step, we created the design corpus to develop an analytical framework *To whom correspondence should be addressed. Email: Raul.Rodriguez-Esteban@pfizer.com 2008 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.