Computational reconstruction of missing data in biological research / Feng Bao.
2021
QH324.2 .B36 2021
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Details
Title
Computational reconstruction of missing data in biological research / Feng Bao.
Author
Bao, Feng, author.
ISBN
9789811630644 (electronic bk.)
981163064X (electronic bk.)
9789811630637
9811630631
981163064X (electronic bk.)
9789811630637
9811630631
Published
Singapore : Springer ; [Beijing] : Tsinghua University Press, [2021]
Copyright
©2021
Language
English
Description
1 online resource (118 pages) : illustrations (chiefly color)
Item Number
10.1007/978-981-16-3064-4 doi
Call Number
QH324.2 .B36 2021
Dewey Decimal Classification
570.285
Summary
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.
Note
"Doctoral thesis accepted by Tsinghua University, Beijing, China."
Bibliography, etc. Note
Includes bibliographical references.
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Description based on print version record.
Series
Springer theses.
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Computational Reconstruction of Missing Data in Biological Research.
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Table of Contents
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.
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.