A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae)

By Olga G. Troyanskaya, Kara Dolinski, Art B. Owen, Russ B. Altman, David Botstein. Proceedings of the National Academy of Sciences of the United States of America, 2003

593 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b65cea8cc1c29d4d6b806c81abe986450a88bdcb

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Citations in the corpus

A Primer on Learning in Bayesian Networks for Computational Biology (2007, methodology): “They are becoming increasingly important in the biological sciences for the tasks of inferring cellular networks [1], modelling protein signalling pathways [2], systems biology, data integration [3], classification [4], and genetic data analysis [5].”
Common markers of proliferation (2006, methodology): “Computational methods are being developed that can predict the function of gene products, based on expression patterns, analysis of the gene promoter sequence, and protein–protein interactions of gene product”
Significance analysis of functional categories in gene expression studies: a structured permutation approach (2005, background): “Recent publications have promoted more probabilistic approaches to gene function (Fraser and Marcotte, 2004; Troyanskaya et al., 2003) that are appropriate for unknown genes or those with less certain annotation.”
Functional Knowledge Transfer for High-accuracy Prediction of Under-studied Biological Processes (2013, methodology): “Previous methods have focused on incorporating functional genomic data primarily as input data [66–69].”
Prioritizing candidate disease genes by network-based boosting of genome-wide association data. (2011, background): “1999) and may have corresponding measures of confidence (Troyanskaya et al. 2003; Lee et al. 2004; Rhodes et al. 2005; Alexeyenko and Sonnhammer 2009; Jensen et al. 2009).”
Comparative Genomics in Eukaryotes (2005, background): “Another vertical comparative genomics effort was made by constructing a naïve Bayesian network to predict physical protein interactions, effectively adding up the log odds probabilities of interaction from heterogeneous data sources (Troyanskaya et al., 2003).”
GENEVESTIGATOR. Arabidopsis Microarray Database and Analysis Toolbox1[w] (2004, background): “The inference of such module networks may require the use of many data types such as gene expression, protein abundance, protein interaction, metabolite abundance, afﬁnity precipitation, synthetic lethality, etc. (Troyanskaya et al., 2003).”
Prediction‐based fingerprints of protein–protein interactions (2006, methodology): “…if (i) it is surface-exposed when considering the structure of an individual protein chain; (ii) the change in its RSA between the isolated chain and the corresponding complex structure is greater than 4% RSA, and the change in its exposed surface area in absolute terms is greater than 5 A˚ 2 .”
GoFDR: A sequence alignment based method for predicting protein functions. (2016, unknown): “…they use, which are sequence-based algorithms that require only the sequence of a protein [1–12], and omics data-based algorithms that take input various types of functional genomics data, such as gene expression, protein–protein interaction, transcription factor binding, phenotypes, etc [13–24].”