Using over-represented tetrapeptides to predict protein submitochondria locations.
about
Identify Beta-Hairpin Motifs with Quadratic Discriminant Algorithm Based on the Chemical ShiftsComputational prediction of anti HIV-1 peptides and in vitro evaluation of anti HIV-1 activity of HIV-1 P24-derived peptides.Survey of Natural Language Processing Techniques in Bioinformatics.Identifying the subfamilies of voltage-gated potassium channels using feature selection technique.Predicting cancerlectins by the optimal g-gap dipeptides.Identification of apolipoprotein using feature selection technique.SubMito-PSPCP: predicting protein submitochondrial locations by hybridizing positional specific physicochemical properties with pseudoamino acid compositionsPAI: Predicting adenosine to inosine editing sites by using pseudo nucleotide compositions.Characterization of proteins in S. cerevisiae with subcellular localizations.High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures.Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition.iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier.Identify Secretory Protein of Malaria Parasite with Modified Quadratic Discriminant Algorithm and Amino Acid Composition.SChloro: directing Viridiplantae proteins to six chloroplastic sub-compartments.Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences.Predicting Protein Submitochondrial Locations: The 10th Anniversary.Identification of mitochondrial proteins of malaria parasite using analysis of variance.Protein binding site prediction by combining hidden Markov support vector machine and profile-based propensities.HBPred: a tool to identify growth hormone-binding proteins.Improving Prediction of Self-interacting Proteins Using Stacked Sparse Auto-Encoder with PSSM profiles.An empirical study of different approaches for protein classificationPrediction of four kinds of simple supersecondary structures in protein by using chemical shifts
P2860
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P2860
Using over-represented tetrapeptides to predict protein submitochondria locations.
description
2013 nî lūn-bûn
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name
Using over-represented tetrapeptides to predict protein submitochondria locations.
@en
Using over-represented tetrapeptides to predict protein submitochondria locations.
@nl
type
label
Using over-represented tetrapeptides to predict protein submitochondria locations.
@en
Using over-represented tetrapeptides to predict protein submitochondria locations.
@nl
prefLabel
Using over-represented tetrapeptides to predict protein submitochondria locations.
@en
Using over-represented tetrapeptides to predict protein submitochondria locations.
@nl
P2093
P2860
P1433
P1476
Using over-represented tetrapeptides to predict protein submitochondria locations.
@en
P2093
Lu-Feng Yuan
Zi-Qiang Li
P2860
P2888
P304
P356
10.1007/S10441-013-9181-9
P50
P577
2013-03-10T00:00:00Z