EDITORIAL
Published on 20 Sep 2022
Editorial: Machine learning for peptide structure, function, and design
doi 10.3389/fgene.2022.1007635
- 1,989 views
- 4 citations
- Frontiers in Genetics
- Computational Genomics
- Research Topics
- Machine Learning for Peptide Structure, Function, and Design
Machine Learning for Peptide Structure, Function, and Design
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Articles
ORIGINAL RESEARCH
Published on 22 Aug 2022
AttnTAP: A Dual-input Framework Incorporating the Attention Mechanism for Accurately Predicting TCR-peptide Binding
doi 10.3389/fgene.2022.942491
- 2,082 views
- 12 citations
ORIGINAL RESEARCH
Published on 22 Jul 2022
Profiling a Community-Specific Function Landscape for Bacterial Peptides Through Protein-Level Meta-Assembly and Machine Learning
doi 10.3389/fgene.2022.935351
- 1,156 views
- 3 citations
ORIGINAL RESEARCH
Published on 08 Jun 2022
DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method
doi 10.3389/fgene.2022.859188
- 2,677 views
- 10 citations
ORIGINAL RESEARCH
Published on 31 May 2022
ProtTrans-Glutar: Incorporating Features From Pre-trained Transformer-Based Models for Predicting Glutarylation Sites
doi 10.3389/fgene.2022.885929
- 2,971 views
- 11 citations
ORIGINAL RESEARCH
Published on 17 May 2022
BBPpredict: A Web Service for Identifying Blood-Brain Barrier Penetrating Peptides
doi 10.3389/fgene.2022.845747
- 5,444 views
- 15 citations
ORIGINAL RESEARCH
Published on 16 May 2022
Inter-Residue Distance Prediction From Duet Deep Learning Models
doi 10.3389/fgene.2022.887491
- 10,697 views
- 7 citations
ORIGINAL RESEARCH
Published on 28 Apr 2022
Ensemble-AHTPpred: A Robust Ensemble Machine Learning Model Integrated With a New Composite Feature for Identifying Antihypertensive Peptides
doi 10.3389/fgene.2022.883766
- 1,933 views
- 10 citations
ORIGINAL RESEARCH
Published on 27 Apr 2022
Refined Contact Map Prediction of Peptides Based on GCN and ResNet
doi 10.3389/fgene.2022.859626
- 5,277 views
- 4 citations
METHODS
Published on 27 Apr 2022
i2APP: A Two-Step Machine Learning Framework For Antiparasitic Peptides Identification
doi 10.3389/fgene.2022.884589
- 1,216 views
- 2 citations
ORIGINAL RESEARCH
Published on 15 Mar 2022
SSH2.0: A Better Tool for Predicting the Hydrophobic Interaction Risk of Monoclonal Antibody
doi 10.3389/fgene.2022.842127
- 5,325 views
- 13 citations
ORIGINAL RESEARCH
Published on 14 Jan 2022
Systematic Modeling, Prediction, and Comparison of Domain–Peptide Affinities: Does it Work Effectively With the Peptide QSAR Methodology?
doi 10.3389/fgene.2021.800857
- 2,832 views
- 42 citations
