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

By Duane Szafron, Paul Lu, R. Greiner, David S. Wishart, Brett Poulin, Roman Eisner, Zhiyong Lu, John Anvik, Cam Macdonell, Alona Fyshe and 1 more. 2004

113 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy

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Citations in the corpus

What do we need to build explainable AI systems for the medical domain? (2017, methodology): “…of any classiﬁer that is formulated as an additive model and showed how to implement this framework in the context of three models: na¨ıve Bayes, linear support vector machines and logistic regression, which they implemented successfully into a bioinformatics application (Szafron et al., 2004).”
Causability and explainability of artificial intelligence in medicine (2019, methodology): “…of any classifier that is formulated as an additive model and showed how to implement this framework in the context of three models: naïve Bayes, linear support vector machines and logistic regression, which they implemented successfully into a bioinformatics application (Szafron et al., 2004).”
Comprehensible classification models: a position paper (2014, background): “In bioinformatics, understanding a model’s predictions often helps biologists to trust the predictions [16], [26], [67], increasing the chances that users will invest a lot of time and money in the execution of sophisticated biological experiments to try to confirm the model’s predictions.”
Visual Explanation of Evidence with Additive Classifiers (2006, methodology): “We also use examples from Proteome Analyst ( Szafron et al. 2004 ), a web-based proteome prediction tool used by molecular biologists, which contains a full implementation of ExplainD.”
NetB, a New Toxin That Is Associated with Avian Necrotic Enteritis Caused by Clostridium perfringens (2008, methodology): “These unique ORFs were further analysed by PSORTB [19] and ProteomeAnalyst [20] to identify predicted extracellular proteins.”