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SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data

PeerJ. Computer science, 2019-02, Vol.5, p.e177-e177, Article e177

2019

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A New Scheme to Characterize and Identify Protein Ubiquitination Sites

IEEE/ACM transactions on computational biology and bioinformatics, 2017-03, Vol.14 (2), p.393-403

2017

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SuccSite: Incorporating Amino Acid Composition and Informative k-spaced Amino Acid Pairs to Identify Protein Succinylation Sites

Genomics, proteomics & bioinformatics, 2020-04, Vol.18 (2), p.208-219

2020

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UbiNet: an online resource for exploring the functional associations and regulatory networks of protein ubiquitylation

Database : the journal of biological databases and curation, 2016, Vol.2016, p.baw054

2016

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Using a hybrid neural network architecture for DNA sequence representation: A study on N 4 -methylcytosine sites

Computers in biology and medicine, 2024-08, Vol.178, p.108664, Article 108664

2024

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The Prediction of Succinylation Site in Protein by Analyzing Amino Acid Composition

Advances in Information and Communication Technology, p.633-642

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A New Schema to Identify S-farnesyl Cysteine Prenylation Sites with Substrate Motifs

Advances in Information and Communication Technology, p.73-83

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Characterization and identification of ubiquitin conjugation sites with E3 ligase recognition specificities

BMC bioinformatics, 2015-01, Vol.16 Suppl 1 (Suppl 1), p.S1-S1, Article S1

2015

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BERT-Promoter: An improved sequence-based predictor of DNA promoter using BERT pre-trained model and SHAP feature selection

Computational biology and chemistry, 2022-08, Vol.99, p.107732-107732, Article 107732

2022

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An interpretable deep learning model for classifying adaptor protein complexes from sequence information

Methods (San Diego, Calif.), 2022-11, Vol.207, p.90-96

2022

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Recent progress in machine learning approaches for predicting carcinogenicity in drug development

Expert opinion on drug metabolism & toxicology, 2024-07, Vol.20 (7), p.1-628

2024

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Using a hybrid neural network architecture for DNA sequence representation: A study on N4-methylcytosine sites

Computers in biology and medicine, 2024-08, Vol.178, p.108664, Article 108664

2024

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Incorporating Natural Language-Based and Sequence-Based Features to Predict Protein Sumoylation Sites

The 12th Conference on Information Technology and Its Applications, p.74-88

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Exploiting Two-Layer Support Vector Machine to Predict Protein SUMOylation Sites

Advances in Engineering Research and Application, p.324-332

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Enhanced Prediction of mRNA Subcellular Localization Using a Novel Ensemble Learning and Hybrid Approach

Advances in Information and Communication Technology, p.60-68

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SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data

A New Scheme to Characterize and Identify Protein Ubiquitination Sites

SuccSite: Incorporating Amino Acid Composition and Informative k-spaced Amino Acid Pairs to Identify Protein Succinylation Sites

UbiNet: an online resource for exploring the functional associations and regulatory networks of protein ubiquitylation

Using a hybrid neural network architecture for DNA sequence representation: A study on N 4 -methylcytosine sites

The Prediction of Succinylation Site in Protein by Analyzing Amino Acid Composition

A New Schema to Identify S-farnesyl Cysteine Prenylation Sites with Substrate Motifs

Characterization and identification of ubiquitin conjugation sites with E3 ligase recognition specificities

BERT-Promoter: An improved sequence-based predictor of DNA promoter using BERT pre-trained model and SHAP feature selection

An interpretable deep learning model for classifying adaptor protein complexes from sequence information

Recent progress in machine learning approaches for predicting carcinogenicity in drug development

Using a hybrid neural network architecture for DNA sequence representation: A study on N4-methylcytosine sites

Incorporating Natural Language-Based and Sequence-Based Features to Predict Protein Sumoylation Sites

Exploiting Two-Layer Support Vector Machine to Predict Protein SUMOylation Sites

Enhanced Prediction of mRNA Subcellular Localization Using a Novel Ensemble Learning and Hybrid Approach

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