title: Text as data: using text-based features for proteins representation and for computational prediction of their characteristics.
abstract: The current era of large-scale biology is characterized by a fast-paced growth in the number of sequenced genomes and, consequently, by a multitude of identified proteins whose function has yet to be determined. Simultaneously, any known or postulated information concerning genes and proteins is part of the ever-growing published scientific literature, which is expanding at a rate of over a million new publications per year. Computational tools that attempt to automatically predict and annotate protein characteristics, such as function and localization patterns, are being developed along with systems that aim to support the process via text mining. Most work on protein characterization focuses on features derived directly from protein sequence data. Protein-related work that does aim to utilize the literature typically concentrates on extracting specific facts (e.g., protein interactions) from text. In the past few years we have taken a different route, treating the literature as a source of text-based features, which can be employed just as sequence-based protein-features were used in earlier work, for predicting protein subcellular location and possibly also function. We discuss here in detail the overall approach, along with results from work we have done in this area demonstrating the value of this method and its potential use.
authors.affiliation: Dept. of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA; Delaware Biotechnology Institute, University of Delaware, Newark, DE 19711, USA; Computational Biology and Machine Learning Lab, School of Computing, Queen's University, Kingston, ON K7L 3N6, Canada. Electronic address: shatkay@udel.edu.
authors.forename: Hagit
authors.initials: H
authors.lastname: Shatkay
authors.affiliation: School of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada; Computational Biology and Machine Learning Lab, School of Computing, Queen's University, Kingston, ON K7L 3N6, Canada.
authors.forename: Scott
authors.initials: S
authors.lastname: Brady
authors.affiliation: Office of Personalized Genomics & Innovative Medicine, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada; Computational Biology and Machine Learning Lab, School of Computing, Queen's University, Kingston, ON K7L 3N6, Canada.
authors.forename: Andrew
authors.initials: A
authors.lastname: Wong
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods* / Q000639:trends; D057225:Data Mining / Q000379:methods* / Q000639:trends; D030562:Databases, Protein / Q000639:trends; D006801:Humans; D011506:Proteins / Q000235:genetics
chemical_list: D011506:Proteins
keywords: Biomedical text mining; Machine learning; Protein annotation; Protein function prediction; Protein location prediction; Protein representation; Protein subcellular location; Text classification; Text mining