Md. Mehedi Hasan
Orcid: 0000-0003-4952-0739Affiliations:
- Kyushu Institute of Technology, Department of Bioscience and Bioinformatics, Fukuoka, Japan
According to our database1,
Md. Mehedi Hasan
authored at least 20 papers
between 2018 and 2023.
Collaborative distances:
Collaborative distances:
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Bibliography
2023
PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning.
Comput. Biol. Medicine, 2023
2022
DeepDNAbP: A deep learning-based hybrid approach to improve the identification of deoxyribonucleic acid-binding proteins.
Comput. Biol. Medicine, 2022
BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches.
Briefings Bioinform., 2022
TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization.
Briefings Bioinform., 2022
2021
IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations.
J. Comput. Aided Mol. Des., 2021
Convolutional neural networks with image representation of amino acid sequences for protein function prediction.
Comput. Biol. Chem., 2021
BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides.
Bioinform., 2021
LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec.
Briefings Bioinform., 2021
Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework.
Briefings Bioinform., 2021
StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides.
Briefings Bioinform., 2021
Integrative machine learning framework for the identification of cell-specific enhancers from the human genome.
Briefings Bioinform., 2021
NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning.
Briefings Bioinform., 2021
2020
iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides.
J. Chem. Inf. Model., 2020
ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations.
J. Comput. Aided Mol. Des., 2020
Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation.
J. Comput. Aided Mol. Des., 2020
iLBE for Computational Identification of Linear B-cell Epitopes by Integrating Sequence and Evolutionary Features.
Genom. Proteom. Bioinform., 2020
Computational prediction of protein ubiquitination sites mapping on <i>Arabidopsis thaliana</i>.
Comput. Biol. Chem., 2020
HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation.
Bioinform., 2020
2018
SIPMA: A Systematic Identification of Protein-Protein Interactions in Zea mays Using Autocorrelation Features in a Machine-Learning Framework.
Proceedings of the 18th IEEE International Conference on Bioinformatics and Bioengineering, 2018
iLMS, Computational Identification of Lysine-Malonylation Sites by Combining Multiple Sequence Features.
Proceedings of the 18th IEEE International Conference on Bioinformatics and Bioengineering, 2018