國立虎尾科技大學 |

Computational Reconstruction of Missing Data in Biological Research

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Computational Reconstruction of Missing Data in Biological Research/ by Feng Bao.
Author:	Bao, Feng.
Description:	XVII, 105 p. 43 illus., 41 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-981-16-3064-4
ISBN:	9789811630644

Computational Reconstruction of Missing Data in Biological Research
Bao, Feng.

Computational Reconstruction of Missing Data in Biological Research[electronic resource] /by Feng Bao. - 1st ed. 2021. - XVII, 105 p. 43 illus., 41 illus. in color.online resource. - Springer Theses, Recognizing Outstanding Ph.D. Research,2190-5061. - Springer Theses, Recognizing Outstanding Ph.D. Research,.

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.

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.

ISBN: 9789811630644

Standard No.: 10.1007/978-981-16-3064-4doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5-.7

Dewey Class. No.: 006.31

Computational Reconstruction of Missing Data in Biological Research
LDR:03372nam a22004095i 4500 001 1053705
003 DE-He213
005 20210812134134.0
007 cr nn 008mamaa
008 220103s2021 si | s |||| 0|eng d
020 $a 9789811630644 $9 978-981-16-3064-4
024 7 $a 10.1007/978-981-16-3064-4 $2 doi
035 $a 978-981-16-3064-4
050 4 $a Q325.5-.7
050 4 $a TK7882.P3
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
100 1 $a Bao, Feng. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 674918
245 1 0 $a Computational Reconstruction of Missing Data in Biological Research $h [electronic resource] / $c by Feng Bao.
250 $a 1st ed. 2021.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a XVII, 105 p. 43 illus., 41 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Springer Theses, Recognizing Outstanding Ph.D. Research, $x 2190-5061
505 0 $a 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.
520 $a 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.
650 0 $a Machine learning. $3 561253
650 0 $a Data structures (Computer science). $3 680370
650 0 $a Artificial intelligence. $3 559380
650 0 $a Computers. $3 565115
650 0 $a Mathematical statistics. $3 527941
650 0 $a Biometrics (Biology). $3 1249740
650 1 4 $a Machine Learning. $3 1137723
650 2 4 $a Data Structures and Information Theory. $3 1211601
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Theory of Computation. $3 669322
650 2 4 $a Probability and Statistics in Computer Science. $3 669886
650 2 4 $a Biometrics. $3 677095
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811630637
776 0 8 $i Printed edition: $z 9789811630651
830 0 $a Springer Theses, Recognizing Outstanding Ph.D. Research, $x 2190-5053 $3 1253569
856 4 0 $u https://doi.org/10.1007/978-981-16-3064-4
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)