(Q39941383)

15 September 2017

title

Multi label learning for prediction of human protein subcellular localizations (English)

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15 September 2017

Lin Zhu

1

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15 September 2017

Jie Yang

series ordinal

2

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15 September 2017

Hong-Bin Shen

series ordinal

3

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15 September 2017

publication date

1 December 2009

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15 September 2017

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15 September 2017

volume

28

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15 September 2017

issue

9-10

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15 September 2017

page(s)

384-390

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Structural bioinformatics and its impact to biomedical science

15 September 2017

cites work

1 reference

Crossref

https://api.crossref.org/works/10.1007%2FS10930-009-9205-0

Predicting subcellular localization of proteins using machine-learned classifiers

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Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search

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Predicting subcellular localization of proteins based on their N-terminal amino acid sequence

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Genetic programming for creating Chou's pseudo amino acid based features for submitochondria localization

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Identifications of conserved 7-mers in 3'-UTRs and microRNAs in Drosophila

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Prediction of subcellular protein localization based on functional domain composition.

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Prediction of protein subcellular locations using Markov chain models

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Spatio-temporal regulation of Rac1 localization and lamellipodia dynamics during epithelial cell-cell adhesion

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Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence

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12 December 2020

Recent progress in protein subcellular location prediction

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12 December 2020

Romancing the "hidden proteome", Anno Domini two zero zero seven

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Predicting protein subcellular location by fusing multiple classifiers

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Bounds on error expectation for support vector machines.

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A further step toward an optimal ensemble of classifiers for peptide classification, a case study: HIV protease.

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Automated subcellular location determination and high-throughput microscopy

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Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains

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Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions

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12 December 2020

Predicting subcellular localization of proteins using machine-learned classifiers

1 reference

12 December 2020

10.1007/S10930-009-9205-0

Identifiers

DOI

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15 September 2017

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