LEADER 05375nam 22006255 450 001 9910763596403321 005 20231113204836.0 010 $a3-031-36502-X 024 7 $a10.1007/978-3-031-36502-7 035 $a(MiAaPQ)EBC30941359 035 $a(Au-PeEL)EBL30941359 035 $a(DE-He213)978-3-031-36502-7 035 $a(CKB)28846147000041 035 $a(EXLCZ)9928846147000041 100 $a20231113d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning Methods for Multi-Omics Data Integration /$fedited by Abedalrhman Alkhateeb, Luis Rueda 205 $a1st ed. 2024. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024. 215 $a1 online resource (171 pages) 311 08$aPrint version: Alkhateeb, Abedalrhman Machine Learning Methods for Multi-Omics Data Integration Cham : Springer International Publishing AG,c2024 9783031365010 327 $aChapter 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. 330 $aThe advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data. Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques on various multi-omics data sets. 606 $aBioinformatics 606 $aMachine learning 606 $aData mining 606 $aMedical informatics 606 $aBioinformatics 606 $aMachine Learning 606 $aData Mining and Knowledge Discovery 606 $aHealth Informatics 606 $aComputational and Systems Biology 615 0$aBioinformatics. 615 0$aMachine learning. 615 0$aData mining. 615 0$aMedical informatics. 615 14$aBioinformatics. 615 24$aMachine Learning. 615 24$aData Mining and Knowledge Discovery. 615 24$aHealth Informatics. 615 24$aComputational and Systems Biology. 676 $a570.285 700 $aAlkhateeb$b Abedalrhman$01439082 701 $aRueda$b Luis$01439083 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910763596403321 996 $aMachine Learning Methods for Multi-Omics Data Integration$93601304 997 $aUNINA