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Maha Thafar

Assistant Professor

taif university

Saudi Arabia

Research Interests

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Professors in Biochemistry

Articles (16)

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2026

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2023

Collaborators (3)

Ahsanullah Unar

Università degli Studi della Campania Luigi Vanvitelli

ITALY

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Azza Althagafi

Assistant Professor

taif university

SAUDI ARABIA

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Eaman Alharbi

Assistant Professor

king abdulaziz university

SAUDI ARABIA

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Maha Thafar

Research Interests

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Articles (16)

Comparative Analysis of CNN and Transformer Models for Multi-Class Diabetic Retinopathy Grading Using Fundus Images

Smart Waste Management System for Makkah City Using Artificial Intelligence and Internet of Things

Deep Learning-Based Mpox Skin Lesion Detection and Real-Time Monitoring in a Smart Healthcare System

StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach

Brain Stroke Classification Using CT Scans with Transformer-Based Models and Explainable AI

Improved in Silico Identification of Protein-Protein Interactions Using Deep Learning Approach

Machine learning-based classification of viral membrane proteins

PMPred-AE: a computational model for the detection and interpretation of pathological myopia based on artificial intelligence

FutureCite: Predicting Research Articles’ Impact Using Machine Learning and Text and Graph Mining Techniques

Improving protein-protein interaction prediction using protein language model and protein network features

Protein-peptide binding residue prediction based on protein language models and cross-attention mechanism

A deep learning model predicts the presence of diverse cancer types using circulating tumor cells

OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features

Exploiting machine learning models to identify novel Alzheimer’s disease biomarkers and potential targets

VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants

VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants (vol 24, bbac535, 2022)

Collaborators (3)

Ahsanullah Unar

Azza Althagafi

Eaman Alharbi

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