Abedalrhman alkhateeb (22 resultados)
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 235,39
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 integratingthese 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.
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 243,28
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 pl…atforms, 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 integratingthese 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.
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 344,82
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Hardcover. Condición: Brand New. 174 pages. 9.25x6.10x9.21 inches. In Stock.
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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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EUR 182,29
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Condición: new. Questo è un articolo print on demand.
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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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EUR 182,29
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Idioma: Inglés
Editorial: Springer International Publishing, Springer Nature Switzerland Nov 2024, 2024
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 213,99
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 m…ultiple 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 integratingthese 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. 176 pp. Englisch.
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Librería: moluna, Greven, Alemaniamoluna
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EUR 197,62
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
Idioma: Inglés
Editorial: Springer, Berlin|Springer International Publishing|Springer, 2023
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Librería: moluna, Greven, Alemaniamoluna
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EUR 197,62
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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. La…rge-scale data sets for multiple omics platform.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 235,39
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 integratingthese 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. 176 pp. Englisch.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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EUR 235,39
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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, Fatm…a 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 176 pp. Englisch.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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EUR 235,39
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 medicin…e, 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 176 pp. Englisch.
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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