Lista de obras -

A Multi-Label Classifier for Predicting the Subcellular Localization of Gram-Negative Bacterial Proteins with Both Single and Multiple Sites ⬇️

artículo científico publicado el 17 de junio de 2011

An application of gene comparative image for predicting the effect on replication ratio by HBV virus gene missense mutation

artículo científico publicado en 2005

Benchmark data for identifying DNA methylation sites via pseudo trinucleotide composition

artículo científico publicado en 2015

Cellular Automata and Its Applications in Protein Bioinformatics ⬇️

artículo científico publicado el 1 de septiembre de 2011

Digital coding of amino acids based on hydrophobic index

artículo científico publicado en 2007

Erratum to "pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC" [Gene 628 (2017) 315-321].

artículo científico publicado en 2017

Erratum to "pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC" [Gene 628 (2017) 315-321].

artículo científico publicado en 2017

GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions ⬇️

artículo científico publicado el 23 de diciembre de 2010

GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes

artículo científico publicado en 2009

GPCR-GIA: a web-server for identifying G-protein coupled receptors and their families with grey incidence analysis

artículo científico publicado en 2009

Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition

artículo científico publicado en 2015

NR-2L: a two-level predictor for identifying nuclear receptor subfamilies based on sequence-derived features

artículo científico publicado en 2011

Predict Drug-Protein Interaction in Cellular Networking ⬇️

artículo científico publicado el 1 de enero de 2013

Predicting Secretory Proteins of Malaria Parasite by Incorporating Sequence Evolution Information into Pseudo Amino Acid Composition via Grey System Model ⬇️

artículo científico publicado el 26 de noviembre de 2012

Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image

artículo científico publicado en 2008

Quat-2L: a web-server for predicting protein quaternary structural attributes

artículo científico publicado en 2010

Recent Progresses in Identifying Nuclear Receptors and Their Families ⬇️

artículo científico publicado el 1 de enero de 2013

Recent advances in predicting protein classification and their applications to drug development

artículo científico publicado en 2013

Using pseudo amino acid composition to predict protein structural classes: approached with complexity measure factor

artículo científico publicado en 2006

iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types ⬇️

artículo científico publicado el 6 de febrero de 2013

iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals

artículo científico publicado en 2017

iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals

artículo científico publicado en 2017

iCDI-PseFpt: Identify the channel–drug interaction in cellular networking with PseAAC and molecular fingerprints ⬇️

artículo científico publicado el 26 de agosto de 2013

iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC.

scientific article published on 02 May 2016

iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition

artículo científico publicado en 2015

iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model ⬇️

artículo científico publicado el 15 de septiembre de 2011

iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach

artículo científico publicado en 2014

iEzy-drug: a web server for identifying the interaction between enzymes and drugs in cellular networking

artículo científico publicado en 2013

iGPCR-Drug: A Web Server for Predicting Interaction between GPCRs and Drugs in Cellular Networking ⬇️

artículo científico publicado el 27 de agosto de 2013

iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC.

artículo científico publicado en 2016

iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier

artículo científico publicado en 2017

iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins ⬇️

artículo científico publicado el 31 de enero de 2013

iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins

artículo científico publicado en 2011

iLoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex Gram-positive bacterial proteins

artículo científico

iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites ⬇️

artículo científico publicado el 1 de diciembre de 2011

iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites ⬇️

artículo científico publicado el 10 de octubre de 2011

iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites

artículo científico

iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approach

artículo científico publicado en 2014

iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking

artículo científico publicado en 2014

iNR-PhysChem: a sequence-based predictor for identifying nuclear receptors and their subfamilies via physical-chemical property matrix

artículo científico publicado en 2012

iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets.

artículo científico publicado en 2016

iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC.

artículo científico publicado en 2015

iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC

artículo científico publicado en 2018

iPTM-mLys: identifying multiple lysine PTM sites and their different types

artículo científico publicado en 2016

iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier

artículo científico publicado en 2016

iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory

artículo científico publicado en 2016

iRNA-2methyl: Identify RNA 2'-O-methylation Sites by Incorporating Sequence-Coupled Effects into General PseKNC and Ensemble Classifier

artículo científico publicado en 2017

iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition

artículo científico publicado en 2017

iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition

artículo científico publicado en 2016

iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components

artículo científico publicado en 2014

iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset

artículo científico publicado en 2015

iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model

artículo científico publicado en 2014

pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites

artículo científico publicado en 2017

pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC.

artículo científico

pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC.

artículo científico publicado en 2017

pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information

artículo científico publicado en 2017

pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC.

artículo científico

pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC.

artículo científico

pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties

artículo científico publicado en 2015

pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach

artículo científico publicado en 2016

pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC.

artículo científico publicado en 2016

Lista de obras de

A Multi-Label Classifier for Predicting the Subcellular Localization of Gram-Negative Bacterial Proteins with Both Single and Multiple Sites ⬇️

An application of gene comparative image for predicting the effect on replication ratio by HBV virus gene missense mutation

Benchmark data for identifying DNA methylation sites via pseudo trinucleotide composition

Cellular Automata and Its Applications in Protein Bioinformatics ⬇️

Digital coding of amino acids based on hydrophobic index

Erratum to "pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC" [Gene 628 (2017) 315-321].

Erratum to "pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC" [Gene 628 (2017) 315-321].

GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions ⬇️

GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes

GPCR-GIA: a web-server for identifying G-protein coupled receptors and their families with grey incidence analysis

Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition

NR-2L: a two-level predictor for identifying nuclear receptor subfamilies based on sequence-derived features

Predict Drug-Protein Interaction in Cellular Networking ⬇️

Predicting Secretory Proteins of Malaria Parasite by Incorporating Sequence Evolution Information into Pseudo Amino Acid Composition via Grey System Model ⬇️

Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image

Quat-2L: a web-server for predicting protein quaternary structural attributes

Recent Progresses in Identifying Nuclear Receptors and Their Families ⬇️

Recent advances in predicting protein classification and their applications to drug development

Using pseudo amino acid composition to predict protein structural classes: approached with complexity measure factor

iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types ⬇️

iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals

iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals

iCDI-PseFpt: Identify the channel–drug interaction in cellular networking with PseAAC and molecular fingerprints ⬇️

iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC.

iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition

iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model ⬇️

iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach

iEzy-drug: a web server for identifying the interaction between enzymes and drugs in cellular networking

iGPCR-Drug: A Web Server for Predicting Interaction between GPCRs and Drugs in Cellular Networking ⬇️

iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC.

iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier

iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins ⬇️

iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins

iLoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex Gram-positive bacterial proteins

iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites ⬇️

iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites ⬇️

iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites

iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approach

iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking

iNR-PhysChem: a sequence-based predictor for identifying nuclear receptors and their subfamilies via physical-chemical property matrix

iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets.

iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC.

iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC

iPTM-mLys: identifying multiple lysine PTM sites and their different types

iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier

iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory

iRNA-2methyl: Identify RNA 2'-O-methylation Sites by Incorporating Sequence-Coupled Effects into General PseKNC and Ensemble Classifier

iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition

iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition

iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components

iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset

iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model

pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites

pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC.

pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC.

pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information

pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC.

pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC.

pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties

pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach

pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC.