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iMethyl-PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach

BioMed research international, 2014-01, Vol.2014, p.947416-12

2014

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

International journal of molecular sciences, 2014-01, Vol.15 (2), p.1746-1766

2014

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iRNAD: a computational tool for identifying D modification sites in RNA sequence

Bioinformatics, 2019-12, Vol.35 (23), p.4922-4929

2019

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

Molecular informatics, 2017-05, Vol.36 (5-6), p.n/a

2017

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

Journal of biomolecular structure & dynamics, 2015-08, Vol.33 (8), p.1731-1742

2015

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iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder

Scientific reports, 2017-08, Vol.7 (1), p.8222-12, Article 8222

2017

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DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method

Frontiers in genetics, 2022-06, Vol.13, p.859188-859188

2022

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

Medicinal chemistry (Shp-sariqah, United Arab Emirates), 2017-12, Vol.13 (8), p.734

2017

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iCDI-W2vCom: Identifying the Ion Channel–Drug Interaction in Cellular Networking Based on word2vec and node2vec

Frontiers in genetics, 2021-09, Vol.12, p.738274-738274

2021

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Integrating unsupervised language model with multi-view multiple sequence alignments for high-accuracy inter-chain contact prediction

Computers in biology and medicine, 2023-11, Vol.166, p.107529-107529, Article 107529

2023

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

Analytical biochemistry, 2015-04, Vol.474, p.69-77

2015

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Intelligent assistant diagnosis for pediatric inguinal hernia based on a multilayer and unbalanced classification model

Frontiers in physiology, 2023-03, Vol.14, p.1105891-1105891

2023

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Stacking-ac4C: an ensemble model using mixed features for identifying n4-acetylcytidine in mRNA

Frontiers in immunology, 2023-11, Vol.14, p.1267755-1267755

2023

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

Bioinformatics (Oxford, England), 2016-10, Vol.32 (20), p.3116-3123

2016

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Using Cellular Automata to Simulate Domain Evolution in Proteins

Frontiers in genetics, 2020-06, Vol.11, p.515-515

2020

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Integrative approach for classifying male tumors based on DNA methylation 450K data

Mathematical biosciences and engineering : MBE, 2023-01, Vol.20 (11), p.19133-19151

2023

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Predicting S-nitrosylation proteins and sites by fusing multiple features

Mathematical biosciences and engineering : MBE, 2021-11, Vol.18 (6), p.9132-9147

2021

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

Analytical biochemistry, 2016-03, Vol.497, p.60-67

2016

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Predicting the Lung Adenocarcinoma and Its Biomarkers by Integrating Gene Expression and DNA Methylation Data

Frontiers in genetics, 2022-06, Vol.13, p.926927-926927

2022

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

Oncotarget, 2016-08, Vol.7 (32), p.51270-51283

2016

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

Oncotarget, 2017-06, Vol.8 (25), p.41178-41188

2017

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MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label Classifier

IEEE access, 2020, Vol.8, p.50276-50284

2020

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iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC

International journal of biological sciences, 2018-01, Vol.14 (8), p.883-891

2018

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

Genomics (San Diego, Calif.), 2018-09, Vol.110 (5), p.239-246

2018

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

Oncotarget, 2016-07, Vol.7 (28), p.44310-44321

2016

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iMethyl-PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach

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

iRNAD: a computational tool for identifying D modification sites in RNA sequence

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

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

iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder

DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method

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

iCDI-W2vCom: Identifying the Ion Channel–Drug Interaction in Cellular Networking Based on word2vec and node2vec

Integrating unsupervised language model with multi-view multiple sequence alignments for high-accuracy inter-chain contact prediction

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

Intelligent assistant diagnosis for pediatric inguinal hernia based on a multilayer and unbalanced classification model

Stacking-ac4C: an ensemble model using mixed features for identifying n4-acetylcytidine in mRNA

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

Using Cellular Automata to Simulate Domain Evolution in Proteins

Integrative approach for classifying male tumors based on DNA methylation 450K data

Predicting S-nitrosylation proteins and sites by fusing multiple features

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

Predicting the Lung Adenocarcinoma and Its Biomarkers by Integrating Gene Expression and DNA Methylation Data

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

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

MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label Classifier

iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC

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

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

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