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If you have any query regarding our method,
please contact 2PMLab's researchers 
Bioinformatic Tools (Webserver) for Bioactive Peptide Prediction:
- TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides
- TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus
- PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning
- iAMAP-SCM: a novel computational tool for large-scale identification of antimalarial peptides using estimated propensity scores of dipeptides
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SCMB3PP: Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides
- MetaNaBP: Leveraging a meta-learning approach to advance the accuracy of Nav blocking peptides prediction
- StackPAP: Accelerating the discovery of plant allergenic proteins using a stacked ensemble-learning framework
References:
- Charoenkwan, P., Kongsompong, S., Schaduangrat, N., Chumnanpuen, P., & Shoombuatong, W. (2023). TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides. BMC bioinformatics, 24(1), 356.
- Charoenkwan, P., Waramit, S., Chumnanpuen, P., Schaduangrat, N., & Shoombuatong, W. (2023). TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus. Plos one, 18(8), e0290538.
- Charoenkwan, P., Chumnanpuen, P., Schaduangrat, N., Oh, C., Manavalan, B., & Shoombuatong, W. (2023). PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning. Computers in Biology and Medicine, 158, 106784.
- Charoenkwan, P., Schaduangrat, N., Lio, P., Moni, M.A., Chumnanpuen, P., Shoombuatong, W. (2022). iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides. ACS Omega, DOI: 10.1021/acsomega.2c04465.
- Charoenkwan, P., Chumnanpuen, P., Schaduangrat, N., Lio, P., Moni, M.A., Shoombuatong, W. (2022). Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides. Journal of Computer-Aided Molecular Design, 1-16.
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