Search Results - "Maha A. Thafar"

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

Authors: Irfan Khan Muhammad Arif Ali Ghulam et al.

Source: IET Syst Biol

Subject Terms: Deep Learning, Protein Interaction Mapping, Computational Biology, Humans, Animals, Computer Simulation, Original Research

Access URL: https://pubmed.ncbi.nlm.nih.gov/40275540

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

Authors: Ali Ghulam Muhammad Arif Ahsanullah Unar et al.

Source: IET Syst Biol

Subject Terms: Machine Learning, Computational Biology, Humans, Peptides, Antihypertensive Agents, Algorithms, Original Research

Access URL: https://pubmed.ncbi.nlm.nih.gov/39905861

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

Authors: Thafar, Maha A. Alsulami, Mashael M. Albaradei, Somayah

Source: Mathematical & Computational Applications; Aug2024, Vol. 29 Issue 4, p59, 16p

Subject Terms: TEXT mining, DATA mining, FEATURE extraction, CITATION networks, RESEARCH personnel

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

Authors: Zhang, Hong-Qi Arif, Muhammad Thafar, Maha A. et al.

Source: Frontiers in Medicine; 2025, p1-11, 11p

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

Authors: Alghoraibi, Huda Alqurashi, Nuha Alotaibi, Sarah et al.

Source: Diagnostics (2075-4418); Oct2025, Vol. 15 Issue 19, p2505, 26p

Subject Terms: MONKEYPOX, SKIN disease diagnosis, HEALTH care industry, MOBILE apps, HEALTH information systems, PUBLIC health surveillance, DEEP learning, REAL-time computing

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

Authors: Qari, Shomukh Thafar, Maha A.

Source: Diagnostics (2075-4418); Oct2025, Vol. 15 Issue 19, p2486, 23p

Subject Terms: STROKE, COMPUTED tomography, ARTIFICIAL intelligence, COMPUTER-assisted image analysis (Medicine), DEEP learning, TRANSFORMER models

Can deep learning technology really recognize Mpox? A positive response from the EfficientNet model.

Authors: Zhao, Xiaoqian Lyu, Long Zhang, Li

Source: Frontiers in Microbiology; 2025, p1-13, 13p

Subject Terms: MONKEYPOX, DEEP learning, DIAGNOSIS, MEDICAL technology, CONVOLUTIONAL neural networks, TRANSFER of training, PUBLIC health, COMPUTER performance

Company/Entity: WORLD Health Organization

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

Authors: Albaradei, Somayah Alganmi, Nofe Albaradie, Abdulrahman et al.

Source: Scientific Reports; 11/30/2023, Vol. 13 Issue 1, p1-14, 14p

Subject Terms: DEEP learning, MACHINE learning, GENE expression, FEATURE selection, TUMOR markers, CANCER cells, MESSENGER RNA

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

Authors: Alamro, Hind Thafar, Maha A. Albaradei, Somayah et al.

Source: Scientific Reports; 3/27/2023, Vol. 13 Issue 1, p1-13, 13p

Subject Terms: DEEP learning, ALZHEIMER'S disease, MACHINE learning, FEATURE selection, BIOMARKERS

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

Authors: Ge, Fang Li, Chen Iqbal, Shahid et al.

Source: Briefings in Bioinformatics; Jan2023, Vol. 24 Issue 1, p1-16, 16p

Subject Terms: DEEP learning, FEATURE extraction, DOWNLOADING

Combining biomedical knowledge graphs and text to improve predictions for drugtarget interactions and drug-indications.

Authors: Alshahrani, Mona Almansour, Abdullah Alkhaldi, Asma et al.

Source: PeerJ; Apr2022, p1-24, 24p

Subject Terms: KNOWLEDGE graphs, SUPERVISED learning, GRAPH algorithms, DRUG interactions, DRUG target, MACHINE learning

Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning.

Authors: Thafar, Maha A. Alshahrani, Mona Albaradei, Somayah et al.

Source: Scientific Reports; 3/19/2022, Vol. 12 Issue 1, p1-18, 18p

Subject Terms: MACHINE learning, DRUG repositioning, WEIGHTED graphs, MINES & mineral resources, DRUG use testing, NOMOGRAPHY (Mathematics), CURVES

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques.

Authors: Thafar, Maha A. Olayan, Rawan S. Ashoor, Haitham et al.

Source: Journal of Cheminformatics; 6/29/2020, Vol. 12 Issue 1, p1-17, 17p

Subject Terms: FORECASTING, SIMILARITY (Geometry), EMBEDDINGS (Mathematics), ERROR rates, MINES & mineral resources

Representation Learning for Natural Language Processing

Authors: Zhiyuan Liu Yankai Lin Maosong Sun

Resource Type: eBook.

Subjects: Natural language processing (Computer science)

Categories: COMPUTERS / Speech & Audio Processing, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Data Science / Data Analytics, LANGUAGE ARTS & DISCIPLINES / Linguistics / General, MATHEMATICS / Probability & Statistics / General

Artificial Intelligence

Authors: Hamid R. Arabnia, David de la Fuente, Roger Dziegiel

Resource Type: eBook.

Subjects: Artificial intelligence--Congresses

Categories: COMPUTERS / Artificial Intelligence / General