title: Identifying and classifying biomedical perturbations in text.
abstract: Molecular perturbations provide a powerful toolset for biomedical researchers to scrutinize the contributions 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 formalized. We propose a formal framework to describe and classify perturbations that allows accumulation of knowledge in order to inform the process of biomedical scientific experimentation and target analysis. We apply this framework to develop a novel algorithm for automatic detection and characterization of perturbations in text and show its relevance in the study of gene-phenotype associations and protein-protein interactions in diabetes and cancer. Analyzing perturbations introduces a novel view of the multivariate landscape of biological systems.
authors.affiliation: Pfizer Research Technology Center, 620 Memorial Dr., Cambridge, MA 02139, USA. Raul.Rodriguez-Esteban@pfizer.com
authors.forename: Raul
authors.initials: R
authors.lastname: Rodriguez-Esteban
authors.forename: Phoebe M
authors.initials: PM
authors.lastname: Roberts
authors.forename: Matthew E
authors.initials: ME
authors.lastname: Crawford
mesh_terms: D000465:Algorithms; D002965:Classification / Q000379:methods; D003920:Diabetes Mellitus / Q000235:genetics / Q000378:metabolism; D004194:Disease / Q000235:genetics*; D053263:Gene Regulatory Networks; D006801:Humans; D016239:MEDLINE; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D010641:Phenotype; D025941:Protein Interaction Mapping