Machine Learning Methods for Multi-Omics Data Integration -

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Résumé :
Chapter 1: Introduction to Multiomics Technology, Ahmed Hajyasien.- Chapter 2: Multi-omics Data Integration Applications and Structures, Ammar El-Hassa.- Chapter 3: Machine learning approaches for multi-omics data integration in medicine, Fatma Hilal Yagin.- Chapter 4: Multimodal methods for knowledge discovery from bulk and single-cell multi-omics data, Yue Li, Gregory Fonseca, and Jun Ding.- Chapter 5: Negative sample selection for miRNA-disease association prediction models, Yulian Ding, Fei Wang, Yuchen Zhang, Fang-Xiang Wu.- Chapter 6: Prediction and Analysis of Key Genes in Prostate Cancer via MRMR Enhanced Similarity Preserving Criteria and Pathway Enrichment Methods, Robert Benjamin Eshun, Hugette Naa Ayele Aryee, Marwan U. Bikdash, and A.K.M Kamrul Islam.- Chapter 7: Graph-Based Machine Learning Approaches for Pangenomics, Indika Kahanda, Joann Mudge, Buwani Manuweera, Thiruvarangan Ramaraj, Alan Cleary, and Brendan Mumey.- Chapter 8: Multiomics-based tensor decomposition for characterizing breast cancer heterogeneity,.- Qian Liu, Shujun Huang, Zhongyuan Zhang, Ted M. Lakowski, Wei Xu and Pingzhao Hu.- Chapter 9: Multi-Omics Databases, Hania AlOmari, Abedalrhman Alkhateeb, and Bassam Hammo....

Biographie:

Abedalrhman Alkhateeb earned his Bachelor's degree in Computer Science from the University of Jordan, Amman, Jordan, in 2004, and his MSc and Ph.D. in Computer Science from the University of Windsor, Canada, in 2011 and 2018, respectively. He is currently an Assistant Professor at Princess Sumaya University for Technology in Amman, Jordan. Previously, he served as an Assistant Professor and Mitacs Accelerate Postdoctoral Fellow at the University of Windsor, Canada. His research interests include machine learning, deep learning, bioinformatics, and health informatics.