Author

Muhammed Talo

University of North TexasORCID 0000-0002-1595-5681Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:deep learning· 3 Research Focus:drug response / drug discovery· 2 Organism:human· 1 Approach:large language model· 1 Assay:protein sequence· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Serdar BozdagiD· 3 shared David Peter SiderovskiiD· 1 shared Nathalie SumieniD· 1 shared S. C. PandeyiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

4 top collaborators · 6 mutual co-author links

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3 shared papersBioinformatics

Serdar BozdagORCID

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintMar 10, 2026

From General-Purpose to Disease-Specific Features: Aligning LLM Embeddings on a Disease-Specific Biomedical Knowledge Graph for Drug Repurposing

S. C. Pandey, Muhammed Talo, David Peter Siderovski…·Serdar Bozdag

The paper introduces CLEAR, a framework that aligns LLM embeddings with disease-specific knowledge graphs to improve drug repurposing, achieving state-of-the-art results on benchmark datasets.

DOI 10.64898/2026.03.07.707871

humandeep learning

𝕏 PostBluesky

BioinformaticsBioinformaticsPeer-reviewedJul 1, 2025

Top-DTI: Integrating Topological Deep Learning and Large Language Models for Drug Target Interaction Prediction

Muhammed Talo, Serdar Bozdag

Top-DTI predicts drug-target interactions by combining topological features and large language model embeddings, outperforming existing methods on benchmark datasets.

15 citationstop 2%

deep learninglarge language model

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BioinformaticsBioinformatics AdvancesPeer-reviewedDec 26, 2024

ProtFun: A Protein Function Prediction Model Using Graph Attention Networks with a Protein Large Language Model

Muhammed Talo, Serdar Bozdag

ProtFun predicts protein functions using a graph attention network with protein LLM embeddings, outperforming existing methods on benchmark datasets.

2 citations

protein sequencedeep learning

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