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. 2004 Jul 1;32(Web Server issue):W365-71.
doi: 10.1093/nar/gkh485.

Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations

Duane Szafron  1 , Paul Lu, Russell Greiner, David S Wishart, Brett Poulin, Roman Eisner, Zhiyong Lu, John Anvik, Cam Macdonell, Alona Fyshe, David Meeuwis
Affiliations

Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations

Duane Szafron et al. Nucleic Acids Res. .

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.

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Figures

Figure 1
Figure 1
The top part of a sample PACard.
Figure 2
Figure 2
Proteins by ontological class (partial screenshot).
Figure 3
Figure 3
Full classifier output for ACEA_ECOLI (partial screenshot).
Figure 4
Figure 4
The feature extraction algorithm for a protein sequence in PA.
Figure 5
Figure 5
The training and prediction phases of classification.
Figure 6
Figure 6
The FASTA-based format of a classifier training file.
Figure 7
Figure 7
Information for a trained classifier (partial screenshot).
Figure 8
Figure 8
Part of the general function prediction (GeneQuiz ontology) Explain page for ACEA_ECOLI.
See this image and copyright information in PMC

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