Computational Reconstruction of Missing Data in Biological Research

Feng Bao

ISBN 10: 9811630631 / ISBN 13: 9789811630637

Verlag: Springer Nature Singapore Aug 2021, 2021

Beschreibung

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.

Bestandsnummer des Verkäufers 9789811630637

Bibliografische Details

• Titel: Computational Reconstruction of Missing Data in Biological Research
• Verlag: Springer Nature Singapore Aug 2021
• Erscheinungsjahr: 2021
• Sprache: Englisch
• ISBN-10: 9811630631
• ISBN-13: 9789811630637
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 235B x 155 H x 8 T Millimeter Breite: 235 mm, Höhe: 155 mm, Tiefe: 8 mm
• Gewicht: 201 Gramm

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

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.