Predicting subcellular localization via protein motif co-occurrence. - Wikidata - wikimedia - TriplyDB

Predicting subcellular localization via protein motif co-occurrence.

Predicting subcellular localization via protein motif co-occurrence.

http://www.wikidata.org/entity/Q37593051

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Refining protein subcellular localization MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition ngLOC: an n-gram-based Bayesian method for estimating the subcellular proteomes of eukaryotes.Global protein function annotation through mining genome-scale data in yeast Saccharomyces cerevisiae mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence.Probabilistic prediction and ranking of human protein-protein interactions.'Unite and conquer': enhanced prediction of protein subcellular localization by integrating multiple specialized tools MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction.FGsub: Fusarium graminearum protein subcellular localizations predicted from primary structures TESTLoc: protein subcellular localization prediction from EST data.Proteome-wide remodeling of protein location and function by stress.Going from where to why--interpretable prediction of protein subcellular localization.Imbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites.Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study Classification of protein motifs based on subcellular localization uncovers evolutionary relationships at both sequence and functional levels.PSI: a comprehensive and integrative approach for accurate plant subcellular localization prediction.Improvement in Protein Domain Identification Is Reached by Breaking Consensus, with the Agreement of Many Profiles and Domain Co-occurrence Predicting protein subcellular localization: past, present, and future.Protein networks markedly improve prediction of subcellular localization in multiple eukaryotic species Proteome-wide discovery of mislocated proteins in cancer Complementary methods to assist subcellular fractionation in organellar proteomics.Subcellular fractionation methods and strategies for proteomics.Recent progress in predicting protein sub-subcellular locations.Exoproteomics: exploring the world around biological systems.Predicting multisite protein subcellular locations: progress and challenges.Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features.Subcellular functions of proteins under fluorescence single-cell microscopy.Detection of new protein domains using co-occurrence: application to Plasmodium falciparum.Protein subcellular localization prediction using multiple kernel learning based support vector machine.Anchorage-independent cell growth signature identifies tumors with metastatic potential.Prediction of endoplasmic reticulum resident proteins using fragmented amino acid composition and support vector machine Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition.YLoc--an interpretable web server for predicting subcellular localization.Fast subcellular localization by cascaded fusion of signal-based and homology-based methods.Predicting the subcellular localization of viral proteins within a mammalian host cell.Combining machine learning and homology-based approaches to accurately predict subcellular localization in Arabidopsis.Network-based prediction of metabolic enzymes' subcellular localization.Accurate prediction of subcellular location of apoptosis proteins combining Chou's PseAAC and PsePSSM based on wavelet denoising.PredAlgo: a new subcellular localization prediction tool dedicated to green algae.

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Predicting subcellular localization via protein motif co-occurrence.

http://www.wikidata.org/entity/Q37593051

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article científic

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article scientifique

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articolo scientifico

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artigo científico

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bilimsel makale

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scientific article published on October 2004

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vedecký článok

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vetenskaplig artikel

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videnskabelig artikel

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vědecký článek

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name

Predicting subcellular localization via protein motif co-occurrence.

@en

Predicting subcellular localization via protein motif co-occurrence.

@nl

type

label

Predicting subcellular localization via protein motif co-occurrence.

@en

Predicting subcellular localization via protein motif co-occurrence.

@nl

prefLabel

Predicting subcellular localization via protein motif co-occurrence.

@en

Predicting subcellular localization via protein motif co-occurrence.

@nl

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Genome Research

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Predicting subcellular localization via protein motif co-occurrence.

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David Y Thomas

http://www.w3.org/2001/XMLSchema#string

Michael T Hallett

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Michelle S Scott

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P2860

Systematic subcellular localization of novel proteins identified by large-scale cDNA sequencing.

Using neural networks for prediction of the subcellular location of proteins

Saccharomyces Genome Database (SGD) provides secondary gene annotation using the Gene Ontology (GO)

Human protein reference database as a discovery resource for proteomics

Global analysis of protein localization in budding yeast.

Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites

The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003

Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes

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

Proteomics characterization of abundant Golgi membrane proteins

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1957-1966

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scholarly article

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10.1101/GR.2650004

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10A

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14

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2004-10-01T00:00:00Z

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15466294

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