Author

Md Tamjidul Hoque

LSU New OrleansORCID 0000-0002-0110-2194Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Assay:protein sequence· 3 Research Focus:benchmarking· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Research Focus:cell type annotation· 1 Assay:imaging· 1 Organism:plant (other)· 1

Frequent Co-Authors

Md Wasi Ul KabiriD· 2 shared A. Keith DunkeriD· 1 shared Ágnes TantosiD· 1 shared Alexander Miguel MonzóniD· 1 shared Alok K. SharmaiD· 1 shared Anastasia ChasapiiD· 1 shared András HatosiD· 1 shared Andrey V. KajavaiD· 1 shared

Activity by Year

2021-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersBioinformatics

Md Wasi Ul KabirORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

ASD-FER: An Annotated Multi-Class Benchmark and Comparative Study for Facial Emotion Recognition in Children with Autism Spectrum Disorder

Shahana Yasmin Chowdhury, Bithi Banik, Tracey A. Knaus, Christopher David Moore, Henry Fordjour Ansah, Huda Hammad, Pranish Ghimire, Md Tamjidul Hoque, Shreya Banerjee

ASD-FER introduces a benchmark dataset for facial emotion recognition in children with ASD and evaluates multiple models, finding ViT achieves the highest accuracy on a held-out set.

bioRxiv · Oct 20, 2026 · PreprintNeuroscience

ESMDisPred: A Structure-Aware CNN-Transformer Architecture for Intrinsically Disordered Protein Prediction

Md Wasi Ul Kabir, Ayon Dey, Farzeen Nafees, Md Tamjidul Hoque

ESMDisPred predicts intrinsically disordered proteins by integrating sequence embeddings with structural data, achieving high accuracy on benchmark datasets.

bioRxiv · Jan 24, 2026 · PreprintBioinformatics

Critical Assessment of Protein Intrinsic Disorder Prediction

Marco Necci, Damiano Piovesan, CAID Predictors, Md Tamjidul Hoque, Ian Walsh, Sumaiya Iqbal, Michele Vendruscolo, Pietro Sormanni, Chen Wang, Daniele Raimondi, Ronesh Sharma, Yaoqi Zhou, Thomas Litfin, Oxana V. Galzitskaya, Michail Yu. Lobanov, , Silvio C. E. Tosatto

The paper evaluates 43 methods for predicting intrinsically disordered regions and binding regions in proteins, finding that deep learning methods outperform older physicochemical approaches but binding prediction remains challenging.

Nature Methods · Apr 19, 2021Peer-reviewedBioinformatics

AIRBP: Accurate identification of RNA-binding proteins using machine learning techniques

Avdesh Mishra, Reecha Khanal, Md Wasi Ul Kabir, Md Tamjidul Hoque

AIRBP uses machine learning to accurately identify RNA-binding proteins from sequence data, outperforming existing methods across multiple species.

Artificial Intelligence in Medicine · Feb 15, 2021Peer-reviewedBioinformatics