iACP: a sequence-based tool for identifying anticancer peptides.
about
Identifying anticancer peptides by using improved hybrid compositionsResistance gene identification from Larimichthys crocea with machine learning techniques.Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function.Molecular classification of prostate adenocarcinoma by the integrated somatic mutation profiles and molecular networkPrediction of presynaptic and postsynaptic neurotoxins by combining various Chou's pseudo components.Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods.LoLoPicker: detecting low allelic-fraction variants from low-quality cancer samples.Combined sequence and sequence-structure based methods for analyzing FGF23, CYP24A1 and VDR genes.Predicting Subcellular Localization of Apoptosis Proteins Combining GO Features of Homologous Proteins and Distance Weighted KNN Classifier.ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble ClassifieriROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition.iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC.Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids.Identification of DEP domain-containing proteins by a machine learning method and experimental analysis of their expression in human HCC tissues.iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC.iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier.Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in HumanEstimating the effects of transcription factors binding and histone modifications on gene expression levels in human cells.Identification of potential CCR5 inhibitors through pharmacophore-based virtual screening, molecular dynamics simulation and binding free energy analysis.iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition.Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types.iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide compositionPrediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression.iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals.iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC.iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences.iRNA-PseU: Identifying RNA pseudouridine sites.EnhancerPred: a predictor for discovering enhancers based on the combination and selection of multiple features.RIFS: a randomly restarted incremental feature selection algorithm.2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.Rama: a machine learning approach for ribosomal protein prediction in plants.MLACP: machine-learning-based prediction of anticancer peptides.iDNA4mC: identifying DNA N4-methylcytosine sites based on nucleotide chemical properties.LPI-NRLMF: lncRNA-protein interaction prediction by neighborhood regularized logistic matrix factorization.pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information.iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC.EnhancerPred2.0: predicting enhancers and their strength based on position-specific trinucleotide propensity and electron-ion interaction potential feature selection.ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based On g-Spaced Di-peptide Features and Support Vector Machine.A Two-Step Feature Selection Method to Predict Cancerlectins by Multiview Features and Synthetic Minority Oversampling Technique.
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iACP: a sequence-based tool for identifying anticancer peptides.
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on March 2016
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
iACP: a sequence-based tool for identifying anticancer peptides.
@en
iACP: a sequence-based tool for identifying anticancer peptides.
@nl
type
label
iACP: a sequence-based tool for identifying anticancer peptides.
@en
iACP: a sequence-based tool for identifying anticancer peptides.
@nl
prefLabel
iACP: a sequence-based tool for identifying anticancer peptides.
@en
iACP: a sequence-based tool for identifying anticancer peptides.
@nl
P2093
P2860
P356
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P1476
iACP: a sequence-based tool for identifying anticancer peptides.
@en
P2093
P2860
P304
16895-16909
P356
10.18632/ONCOTARGET.7815
P407
P577
2016-03-01T00:00:00Z