Author

Mahdi Malekpour

Shiraz University of Medical SciencesORCID 0000-0002-7337-3479Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:protein sequence· 2 Research Focus:cell type annotation· 1 Approach:graph neural network· 1 Research Focus:protein design· 1 Approach:statistical model· 1 Approach:variational autoencoder· 1

Frequent Co-Authors

Farzad MidjaniiD· 2 shared Fateme Zahra KeshtkariD· 2 shared Bahar Saberzadeh-ArdestaniiD· 1 shared Bardia KhosraviiD· 1 shared Heewook LeeiD· 1 shared Reza GhelichiD· 1 shared Samaneh HashemiiD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

7 top collaborators · 15 mutual co-author links

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2 shared papersBioinformatics

Farzad MidjaniORCID

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2 papers

Recent·Most cited

Pep-PU-GAN: Positive-Unlabeled Adversarial Learning for Peptide Function Prediction

Farzad Midjani, Samaneh Hashemi, Fateme Zahra Keshtkar, Mahdi Malekpour, Bahar Saberzadeh-Ardestani, Bardia Khosravi

Pep-PU-GAN improves peptide function prediction using PU learning and GANs with graph neural networks, achieving high F1 and AUROC scores on neuropeptide classification.

bioRxiv · Sep 18, 2026 · PreprintBioinformatics

RulePep: Interpretable ESM-Guided Neural-Symbolic Peptide Classification

Farzad Midjani, Reza Ghelich, Fateme Zahra Keshtkar, Mahdi Malekpour, Heewook Lee

RulePep is a neural-symbolic classifier for peptide-function prediction that combines ESM-2 representations with interpretable rules to achieve high performance on three peptide classification tasks.

bioRxiv · Jul 6, 2026 · PreprintBioinformatics