Maha A. Thafar

发表

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

Xin Gao, Vladimir B. Bajic, Haitham Ashoor, 2020, Journal of Cheminformatics.

Computational Drug-target Interaction Prediction based on Graph Embedding and Graph Mining

Vladimir B. Bajic, Haitham Ashoor, Magbubah Essack, 2020 .

Application and evaluation of knowledge graph embeddings in biomedical data

Magbubah Essack, Maha A. Thafar, Mona Alshahrani, 2021, PeerJ Comput. Sci..

Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA

Maha A. Thafar, V. Bajic, T. Gojobori, 2020, Gene: X.

MetastaSite: Predicting metastasis to different sites using deep learning with gene expression data

Maha A. Thafar, T. Gojobori, Mahmut Uludag, 2022, Frontiers in Molecular Biosciences.

Identification of the ubiquitin–proteasome pathway domain by hyperparameter optimization based on a 2D convolutional neural network

Maha A. Thafar, A. Worachartcheewan, Muhammad Arif, 2022, Frontiers in Genetics.

Metastatic State of Colorectal Cancer can be Accurately Predicted with Methylome

Maha A. Thafar, V. Bajic, M. Essack, 2019, ICBRA.

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

Maha A. Thafar, Chen Li, Dong-Jun Yu, 2022, Briefings Bioinform..

Automated Counting of Colony Forming Units Using Deep Transfer Learning From a Model for Congested Scenes Analysis

Xin Gao, Vladimir B. Bajic, Magbubah Essack, 2020, IEEE Access.

Machine learning and deep learning methods that use omics data for metastasis prediction

Maha A. Thafar, T. Gojobori, Christophe Van Neste, 2021, Computational and structural biotechnology journal.

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities

Vladimir B. Bajic, Magbubah Essack, Somayah Albaradei, 2019, Front. Chem..

MetaCancer: A deep learning-based pan-cancer metastasis prediction model developed using multi-omics data

Maha A. Thafar, T. Gojobori, F. Napolitano, 2021, Computational and structural biotechnology journal.

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

Maha A. Thafar, T. Gojobori, M. Essack, 2022, Scientific Reports.

DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning

Maha A. Thafar, Rawan S. Olayan, V. Bajic, 2021, Journal of Cheminformatics.

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

Maha A. Thafar, T. Gojobori, Mahmut Uludag, 2023, Frontiers in Genetics.

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

Maha A. Thafar, R. Hoehndorf, Mahmut Uludag, 2022, PeerJ.

Drug Repositioning through the Development of Diverse Computational Methods using Machine Learning, Deep Learning, and Graph Mining

Maha A. Thafar, 2022 .

Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models

Maha A. Thafar, T. Gojobori, Mahmut Uludag, 2021, Frontiers in Genetics.

StackACPred: Prediction of anticancer peptides by integrating optimized multiple feature descriptors with stacked ensemble approach

Maha A. Thafar, Dong-Jun Yu, Muhammad Kabir, 2021, Chemometrics and Intelligent Laboratory Systems.

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

Maha A. Thafar, T. Gojobori, M. Essack, 2023, Scientific Reports.

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

Maha A. Thafar, M. Essack, Olaa Motwalli, 2023, Scientific reports.