Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

Azim, Sayed Mehedi; Sabab, Noor Hossain Nuri; Noshadi, Iman; Alinejad-Rokny, Hamid; Sharma, Alok; Shatabda, Swakkhar; Dehzangi, Iman

doi:10.1016/j.imu.2023.101348

Informatics in Medicine Unlocked

2023

DOI: 10.1016/j.imu.2023.101348

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

Sayed Mehedi Azim,

Noor Hossain Nuri Sabab,

Iman Noshadi

et al.

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2024

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Cited by 2 publications

References 57 publications

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CAPTURE: Comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder

Ghafoor,

Asim,

Ibrahim

et al. 2024

Computers in Biology and Medicine

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CAPTURE: Comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder

Ghafoor,

Asim,

Ibrahim

et al. 2024

Computers in Biology and Medicine

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Explainable Machine Learning Model to Accurately Predict Protein-Binding Peptides

Azim,

Balasubramanyam,

Islam

et al. 2024

Algorithms

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Enzymes play key roles in the biological functions of living organisms, which serve as catalysts to and regulate biochemical reaction pathways. Recent studies suggest that peptides are promising molecules for modulating enzyme function due to their advantages in large chemical diversity and well-established methods for library synthesis. Experimental approaches to identify protein-binding peptides are time-consuming and costly. Hence, there is a demand to develop a fast and accurate computational approach to tackle this problem. Another challenge in developing a computational approach is the lack of a large and reliable dataset. In this study, we develop a new machine learning approach called PepBind-SVM to predict protein-binding peptides. To build this model, we extract different sequential and physicochemical features from peptides and use a Support Vector Machine (SVM) as the classification technique. We train this model on the dataset that we also introduce in this study. PepBind-SVM achieves 92.1% prediction accuracy, outperforming other classifiers at predicting protein-binding peptides.

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

Cited by 2 publications

References 57 publications

CAPTURE: Comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder

CAPTURE: Comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder

Explainable Machine Learning Model to Accurately Predict Protein-Binding Peptides

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