S
Somayah Albaradei
Researcher at King Abdullah University of Science and Technology
Publications - 18
Citations - 342
Somayah Albaradei is an academic researcher from King Abdullah University of Science and Technology. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 6, co-authored 14 publications receiving 128 citations. Previous affiliations of Somayah Albaradei include University of Manitoba & King Abdulaziz University.
Papers
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Journal ArticleDOI
Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities.
Maha A. Thafar,Arwa Bin Raies,Somayah Albaradei,Somayah Albaradei,Magbubah Essack,Vladimir B. Bajic +5 more
TL;DR: This study provides a comprehensive overview of the existing methods that predict drug-target binding affinities (DTBA) and focuses on the methods developed using artificial intelligence, machine learning, and deep learning approaches, as well as related benchmark datasets and databases.
Journal ArticleDOI
DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques
Maha A. Thafar,Maha A. Thafar,Rawan S. Olayan,Haitham Ashoor,Somayah Albaradei,Somayah Albaradei,Vladimir B. Bajic,Xin Gao,Takashi Gojobori,Magbubah Essack +9 more
TL;DR: DTiGEMS+stantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state- of- the-art methods comparison.
Journal ArticleDOI
Machine learning and deep learning methods that use omics data for metastasis prediction
Somayah Albaradei,Somayah Albaradei,Maha A. Thafar,Maha A. Thafar,Asim Alsaedi,Asim Alsaedi,Christophe Van Neste,Takashi Gojobori,Magbubah Essack,Xin Gao +9 more
TL;DR: A review of machine learning and deep learning-based metastasis prediction methods can be found in this paper, where different types of molecular data are used to build the models and the critical signatures derived from the different methods.
Journal ArticleDOI
Splice2Deep: An Ensemble of Deep Convolutional Neural Networks for Improved Splice Site Prediction in Genomic DNA
Somayah Albaradei,Somayah Albaradei,Arturo Magana-Mora,Arturo Magana-Mora,Maha A. Thafar,Maha A. Thafar,Mahmut Uludag,Vladimir B. Bajic,Takashi Gojobori,Magbubah Essack,Boris R. Jankovic +10 more
TL;DR: The results of this study demonstrated that Splice2Deep both achieved a considerably reduced error rate compared to other state-of-the-art models and the ability to accurately recognize SS in other organisms for which the model was not trained, enabling annotation of poorly studied or newly sequenced genomes.
Posted Content
SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic.
Qiang Yang,Hind Alamro,Somayah Albaradei,Adil Salhi,Xiaoting Lv,Changsheng Ma,Manal Alshehri,Inji Ibrahim Jaber,Faroug Tifratene,Wei Wang,Takashi Gojobori,Carlos M. Duarte,Xin Gao,Xiangliang Zhang +13 more
TL;DR: Overall, SenWave shows that optimistic and positive sentiments increased over time, foretelling a desire to seek, together, a reset for an improved COVID-19 world.