Prediction of human protein function from post-translational modifications and localization features.

By Lars Juhl Jensen, Ramneek Gupta, Nikolaj S. Blom, Damien P. Devos, Javier Tamames, Can Kesmir, Henrik Nielsen, H. Stærfeldt, Krzysztof Rapacki, C. Workman and 5 more. Journal of Molecular Biology, 2002

332 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/169ab06b45072b5523c5279ccbf96f133f929ff3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Protein secondary structure prediction for a single-sequence using hidden semi-Markov models (2006, background): “Also, out of the 25,000 genes believed to be present in the human genome, no more than 40–60% can be assigned a functional role based on similarity to known proteins [31,32].”
Prediction, conservation analysis, and structural characterization of mammalian mucin-type O-glycosylation sites. (2005, methodology): “� Sparse encoding ( Hertz et al., 1991; Qian and Sejnowski, 1988) is the conventional way to convert the amino acid sequence into numerical form.”
Feature-based prediction of non-classical and leaderless protein secretion. (2004, methodology): “dictive performance were combined in a neural network approach (Jensen et al., 2002a).”
Distinguishing enzyme structures from non-enzymes without alignments. (2003, methodology): “We also make a comparison to the ProtFun enzyme/ non-enzyme methods described by Brunak et al.(22)”
Functional Knowledge Transfer for High-accuracy Prediction of Under-studied Biological Processes (2013, background): “These approaches rely on identifying characteristic behavioral patterns, in functional genomic datasets, phylogenetic profiles, or genomic feature studies of genes that are known to participate in a pathway, then use these patterns to predict additional pathway members [12–14].”
FFPred 3: feature-based function prediction for all Gene Ontology domains (2016, background): “…in ProtFun, which is based on neural networks trained for the functional classification of protein sequences from similarities in amino acid composition, and content of signal peptides, trans-membrane helices, post-translationally modified residues as well as other biological features 14,15 .”
Predicting subcellular localization of proteins for Gram‐negative bacteria by support vector machines based on n‐peptide compositions (2004, background): “Keywords: subcellular localization; support vector machine; Gram-negative bacteria; machine-learning method; proteome; genome; n-peptide compositions The subcellular location of a protein is closely correlated to its biological function (Jensen et al. 2002).”