Mimicking cellular sorting improves prediction of subcellular localization.

By Rajesh Nair, Burkhard Rost. 2005

306 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, background): “…aspects of protein function improved by combining evolutionary information and machine learning, including predictions of subcellular localization (aka cellular compartment or CC in GO [21, 22]), protein interaction sites [23 – 25], and the effects of sequence variation upon function [26, 27].”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “Later, methods predicting aspects of protein function improved through 24 the combination of evolutionary information and machine learning, including predictions of sub25 cellular localization (aka cellular location [21, 22]), protein interaction sites [23-25], and the 26 effects of sequence variati”
Improving protein secondary structure prediction based on short subsequences with local structure similarity (2010, background): “Many researchers employ PSS as a feature to predict the tertiary structure [1-4], function [5-8], or subcellular localization [9-11] of proteins.”
LocTree2 predicts localization for all domains of life (2012, methodology): “Filtering by HVAL and EVAL ensured that homology-based inference would be less accurate than our previous LocTree method (Nair and Rost, 2005).”
Natively Unstructured Loops Differ from Other Loops (2007, background): “In the context of a different problem, we showed how beneficial the use of prediction-based sets with errors might be [80].”
Molecular characterization of EgMYB1, a putative transcriptional repressor of the lignin biosynthetic pathway (2007, methodology): “However, no NLS was found in EgMYB1 using the NLS prediction program LOC tree [36].”
YLoc—an interpretable web server for predicting subcellular localization (2010, methodology): “We compared the YLoc predictors with five other state-of-the-art subcellular localization predictors: MultiLoc2 (20), BaCelLo (6), LOCTree (4), WoLF PSORT (9) and Euk-mPloc (19).”
BaCelLo: a balanced subcellular localization predictor (2006, methodology): “Indeed most of the available methods, except LocTree (Nair and Rost, 2005), take into consideration proteins sharing high level of identity, up to 95%, and do not adopt rigorous validation procedures for excluding homology between the training and the testing sets.”
Global Analysis of DELLA Direct Targets in Early Gibberellin Signaling in Arabidopsis[W] (2007, background): “At2g45900 and At2g34340, which will be referred to as Exp-PT1 and Exp-PT2, respectively, were annotated as expressed proteins and both are predicted to be localized to the nucleus (Nakai and Kanehisa, 1992; Heazlewood et al., 2005, 2007; Nair and Rost, 2005).”
SNAP: predict effect of non-synonymous polymorphisms on function (2007, methodology): “This method allows for maximum resolution in representation and has proven useful in a number of studies done in our group (6,36,39,40).”
MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction (2009, methodology): “The latter methods [2,13-21] use different kinds of composition information like the overall, paired, gapped-paired, surface or pseudo amino acid composition from the protein sequence or sequence profiles.”