Machine Learning Methods for Multi-Omics Data Integration
portes grátis
Machine Learning Methods for Multi-Omics Data Integration
Alkhateeb, Abedalrhman; Rueda, Luis
Springer International Publishing AG
11/2023
168
Dura
Inglês
9783031365010
15 a 20 dias
Descrição não disponível.
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.
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Multi-omics data integration;machine learning;bioinformatics;bioinformatics;proteomics;cancer biomarkers;precision medicine;next-generation sequencing
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
