iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets.
about
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 networkiPTM-mLys: identifying multiple lysine PTM sites and their different types.Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods.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.iACP: a sequence-based tool for identifying anticancer peptides.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.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.Review and comparative assessment of sequence-based predictors of protein-binding residues.iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory.Prediction and identification of the effectors of heterotrimeric G proteins in rice (Oryza sativa L.).iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition.pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC.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.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.Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information.iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC.Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM.A structural perspective on the interactions of TRAF6 and Basigin during the onset of melanoma: A molecular dynamics simulation study.ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based On g-Spaced Di-peptide Features and Support Vector Machine.Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams.Drug Design and Discovery: Principles and Applications.Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia.
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iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets.
description
2016 nî lūn-bûn
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2016年の論文
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2016年学术文章
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2016年学术文章
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2016年学术文章
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2016年学术文章
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2016年学术文章
@zh-my
2016年学术文章
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2016年學術文章
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2016年學術文章
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name
iPPBS-Opt: A Sequence-Based En ...... Imbalanced Training Datasets.
@en
iPPBS-Opt: A Sequence-Based En ...... Imbalanced Training Datasets.
@nl
type
label
iPPBS-Opt: A Sequence-Based En ...... Imbalanced Training Datasets.
@en
iPPBS-Opt: A Sequence-Based En ...... Imbalanced Training Datasets.
@nl
prefLabel
iPPBS-Opt: A Sequence-Based En ...... Imbalanced Training Datasets.
@en
iPPBS-Opt: A Sequence-Based En ...... Imbalanced Training Datasets.
@nl
P2093
P2860
P1433
P1476
iPPBS-Opt: A Sequence-Based En ...... g Imbalanced Training Datasets
@en
P2093
Bingxiang Liu
Jianhua Jia
Kuo-Chen Chou
P2860
P356
10.3390/MOLECULES21010095
P407
P577
2016-01-19T00:00:00Z