Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations
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Date
2004
Authors
Szafron, Duane
Lu, Paul
Greiner, Russell
Wishart, David S.
Poulin, Brett
Eisner, Roman
Lu, Zhiyong
Anvik, John
Macdonell, Cam
Fyshe, Alona
Journal Title
Journal ISSN
Volume Title
Publisher
Oxford University Press
Abstract
Proteome Analyst (PA) (http://www.cs.ualberta.ca/~bioinfo/PA/) is a publicly available, high-throughput, web-based system for predicting various properties of each protein in an entire proteome. Using machine-learned classifiers, PA can predict, for example, the GeneQuiz general function and Gene Ontology (GO) molecular function of a protein. In addition, PA is currently the most accurate and most comprehensive system for predicting subcellular localization, the location within a cell where a protein performs its main function. Two other capabilities of PA are notable. First, PA can create a custom classifier to predict a new property, without requiring any programming, based on labeled training data (i.e. a set of examples, each with the correct classification label) provided by a user. PA has been used to create custom classifiers for potassium-ion channel proteins and other general function ontologies. Second, PA provides a sophisticated explanation feature that shows why one prediction is chosen over another. The PA system produces a Naïve Bayes classifier, which is amenable to a graphical and interactive approach to explanations for its predictions; transparent predictions increase the user's confidence in, and understanding of, PA.
Description
Sherpa Romeo green journal. Permission to archive final published version.
Keywords
Proteome Analyst , Custom predictions , Custom classifiers , Proteome annotations
Citation
Szafron, D., Lu, P., Greiner, R., Wishart, D. S., Poulin, B. Eisner, R., ... Meeuwis, D. (2004). Proteome Analyst: Custom predictions with explanations in a web-based tool for high-throughput proteome annotations. Nucleic Acids Research, 32(2), Pages W365–W371, https://doi.org/10.1093/nar/gkh485