國立高雄大學圖資館 |

登入

回首頁

Machine learning methods for multi-omics data integration

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning methods for multi-omics data integrationedited by Abedalrhman Alkhateeb, Luis Rueda.
其他作者:	Alkhateeb, Abedalrhman.
出版者:	Cham :Springer International Publishing :2024.
面頁冊數:	vi, 168 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	BioinformaticsData processing.
電子資源:	https://doi.org/10.1007/978-3-031-36502-7
ISBN:	9783031365027$q(electronic bk.)

Machine learning methods for multi-omics data integration
Machine learning methods for multi-omics data integration[electronic resource] /edited by Abedalrhman Alkhateeb, Luis Rueda. - Cham :Springer International Publishing :2024. - vi, 168 p. :ill. (chiefly col.), digital ;24 cm.

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.

The 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.

ISBN: 9783031365027$q(electronic bk.)

Standard No.: 10.1007/978-3-031-36502-7doiSubjects--Topical Terms:

487349
Bioinformatics
--Data processing.

LC Class. No.: QH324.2

Dewey Class. No.: 570.285

Machine learning methods for multi-omics data integration
LDR:04370nmm a2200337 a 4500 001 660436
003 DE-He213
005 20231113204836.0
006 m d
007 cr nn 008maaau
008 241106s2024 sz s 0 eng d
020 $a 9783031365027$q(electronic bk.)
020 $a 9783031365010$q(paper)
024 7 $a 10.1007/978-3-031-36502-7 $2 doi
035 $a 978-3-031-36502-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QH324.2
072 7 $a UY $2 bicssc
072 7 $a PS $2 bicssc
072 7 $a SCI056000 $2 bisacsh
072 7 $a PSAX $2 thema
082 0 4 $a 570.285 $2 23
090 $a QH324.2 $b .M149 2024
245 0 0 $a Machine learning methods for multi-omics data integration $h [electronic resource] / $c edited by Abedalrhman Alkhateeb, Luis Rueda.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a vi, 168 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
505 0 $a 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.
520 $a The 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.
650 0 $a Bioinformatics $x Data processing. $3 487349
650 0 $a Genomics $x Data processing. $3 196486
650 0 $a Systems biology $x Data processing. $3 971771
650 0 $a Machine learning. $3 188639
650 1 4 $a Bioinformatics. $3 194415
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Health Informatics. $3 274212
650 2 4 $a Computational and Systems Biology. $3 911810
700 1 $a Alkhateeb, Abedalrhman. $3 971769
700 1 $a Rueda, Luis. $3 971770
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-36502-7
950 $a Computer Science (SpringerNature-11645)