Computational Reconstruction of Missing Data in Biological Research - Couverture souple

Bao, Feng

9789811630637: Computational Reconstruction of Missing Data in Biological Research

Couverture souple

ISBN 10 : 9811630631 ISBN 13 : 9789811630637

Editeur : Springer, 2021

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Chapter 1 Introduction.- Chapter 2 Fast computational recovery of missing features for large-scale biological data.- Chapter 3 Computational recovery of information from low-quality and missing labels.- Chapter 4 Computational recovery of sample missings.- Chapter 5 Summary and outlook.

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� propos de l?auteur

Feng Bao currently is a Postdoctoral Scholar at University of California, San Francisco. He received the Ph.D. degree in control science and engineering from Department of Automation, Tsinghua University, Beijing, China, in 2019. His research interest covers machine learning, computational biology, cell profiling technologies.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteurSpringer
Date d'�dition2021
ISBN 10 9811630631
ISBN 13 9789811630637
ReliureBroch�
Langueanglais
Num�ro d'�dition1
Nombre de pages124
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Computational Reconstruction of Missing Data in Biological Research

Feng Bao

Edit� par Springer Nature Singapore, 2021

ISBN 10 : 9811630631 ISBN 13 : 9789811630637

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Computational Reconstruction of Missing Data in Biological Research

Bao, Feng

Edit� par Springer, Berlin|Springer Nature Singapore|Tsinghua University Press|Springer, 2021

ISBN 10 : 9811630631 ISBN 13 : 9789811630637

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In this book, we introduce different types of biological data missing scenarios and propose machine learning models to improve the data analysis, including deep recurrent neural network recovery for feature missings, robust information theoretic learning fo. N� de r�f. du vendeur 467597321

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Computational Reconstruction of Missing Data in Biological Research

Feng Bao

Edit� par Springer Nature Singapore Aug 2021, 2021

ISBN 10 : 9811630631 ISBN 13 : 9789811630637

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The emerging biotechnologies have significantly advanced the study of biological mechanisms. However, biological data usually contain a great amount of missing information, e.g. missing features, missing labels or missing samples, which greatly limits the extensive usage of the data. In this book, we introduce different types of biological data missing scenarios and propose machine learning models to improve the data analysis, including deep recurrent neural network recovery for feature missings, robust information theoretic learning for label missings and structure-aware rebalancing for minor sample missings. Models in the book cover the fields of imbalance learning, deep learning, recurrent neural network and statistical inference, providing a wide range of references of the integration between artificial intelligence and biology. With simulated and biological datasets, we apply approaches to a variety of biological tasks, including single-cell characterization, genome-wide association studies, medical image segmentations, and quantify the performances in a number of successful metrics.The outline of this book is as follows. In Chapter 2, we introduce the statistical recovery of missing data features; in Chapter 3, we introduce the statistical recovery of missing labels; in Chapter 4, we introduce the statistical recovery of missing data sample information; finally, in Chapter 5, we summarize the full text and outlook future directions. This book can be used as references for researchers in computational biology, bioinformatics and biostatistics. Readers are expected to have basic knowledge of statistics and machine learning. 124 pp. Englisch. N� de r�f. du vendeur 9789811630637

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Computational Reconstruction of Missing Data in Biological Research (Springer Theses)

Bao, Feng

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Computational Reconstruction of Missing Data in Biological Research

Feng Bao

Edit� par Springer Nature Singapore Aug 2021, 2021

ISBN 10 : 9811630631 ISBN 13 : 9789811630637

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Taschenbuch. Etat : Neu. Neuware -The emerging biotechnologies have significantly advanced the study of biological mechanisms. However, biological data usually contain a great amount of missing information, e.g. missing features, missing labels or missing samples, which greatly limits the extensive usage of the data. In this book, we introduce different types of biological data missing scenarios and propose machine learning models to improve the data analysis, including deep recurrent neural network recovery for feature missings, robust information theoretic learning for label missings and structure-aware rebalancing for minor sample missings. Models in the book cover the fields of imbalance learning, deep learning, recurrent neural network and statistical inference, providing a wide range of references of the integration between artificial intelligence and biology. With simulated and biological datasets, we apply approaches to a variety of biological tasks, including single-cell characterization, genome-wide association studies, medical image segmentations, and quantify the performances in a number of successful metrics.The outline of this book is as follows. In Chapter 2, we introduce the statistical recovery of missing data features; in Chapter 3, we introduce the statistical recovery of missing labels; in Chapter 4, we introduce the statistical recovery of missing data sample information; finally, in Chapter 5, we summarize the full text and outlook future directions. This book can be used as references for researchers in computational biology, bioinformatics and biostatistics. Readers are expected to have basic knowledge of statistics and machine learning.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 124 pp. Englisch. N� de r�f. du vendeur 9789811630637

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Computational Reconstruction of Missing Data in Biological Research

Feng Bao

Edit� par Springer Nature Singapore, Springer Nature Singapore, 2021

ISBN 10 : 9811630631 ISBN 13 : 9789811630637

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - The emerging biotechnologies have significantly advanced the study of biological mechanisms. However, biological data usually contain a great amount of missing information, e.g. missing features, missing labels or missing samples, which greatly limits the extensive usage of the data. In this book, we introduce different types of biological data missing scenarios and propose machine learning models to improve the data analysis, including deep recurrent neural network recovery for feature missings, robust information theoretic learning for label missings and structure-aware rebalancing for minor sample missings. Models in the book cover the fields of imbalance learning, deep learning, recurrent neural network and statistical inference, providing a wide range of references of the integration between artificial intelligence and biology. With simulated and biological datasets, we apply approaches to a variety of biological tasks, including single-cell characterization, genome-wide association studies, medical image segmentations, and quantify the performances in a number of successful metrics.The outline of this book is as follows. In Chapter 2, we introduce the statistical recovery of missing data features; in Chapter 3, we introduce the statistical recovery of missing labels; in Chapter 4, we introduce the statistical recovery of missing data sample information; finally, in Chapter 5, we summarize the full text and outlook future directions. This book can be used as references for researchers in computational biology, bioinformatics and biostatistics. Readers are expected to have basic knowledge of statistics and machine learning. N� de r�f. du vendeur 9789811630637

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