03867nam 2200613 450 99646640600331620230427135622.0981-16-3064-X10.1007/978-981-16-3064-4(CKB)4100000011997776(DE-He213)978-981-16-3064-4(MiAaPQ)EBC6692492(Au-PeEL)EBL6692492(PPN)257352090(EXLCZ)99410000001199777620220423d2021 uy 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierComputational reconstruction of missing data in biological research /Feng Bao1st ed. 2021.Gateway East, Singapore :Tsinghua University Press :Springer,[2021]©20211 online resource (XVII, 105 p. 43 illus., 41 illus. in color.)Springer theses981-16-3063-1 Includes bibliographical references.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.Springer theses.BiologyData processingBiologiathubProcessament de dadesthubAprenentatge automàticthubEstructures de dades (Informàtica)thubEstadística matemàticathubLlibres electrònicsthubBiologyData processing.BiologiaProcessament de dadesAprenentatge automàticEstructures de dades (Informàtica)Estadística matemàtica570.285Bao Feng851645MiAaPQMiAaPQMiAaPQBOOK996466406003316Computational Reconstruction of Missing Data in Biological Research1901566UNISA