LEADER 03867nam 2200613 450 001 996466406003316 005 20230427135622.0 010 $a981-16-3064-X 024 7 $a10.1007/978-981-16-3064-4 035 $a(CKB)4100000011997776 035 $a(DE-He213)978-981-16-3064-4 035 $a(MiAaPQ)EBC6692492 035 $a(Au-PeEL)EBL6692492 035 $a(PPN)257352090 035 $a(EXLCZ)994100000011997776 100 $a20220423d2021 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational reconstruction of missing data in biological research /$fFeng Bao 205 $a1st ed. 2021. 210 1$aGateway East, Singapore :$cTsinghua University Press :$cSpringer,$d[2021] 210 4$dŠ2021 215 $a1 online resource (XVII, 105 p. 43 illus., 41 illus. in color.) 225 1 $aSpringer theses 311 $a981-16-3063-1 320 $aIncludes bibliographical references. 327 $aChapter 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. 330 $aThe emerging biotechnologies have significantly advanced the study of biological mechanisms. However, biological data usually contain a great amount of missing information, e.g. missing features, missing labels or missing samples, which greatly limits the extensive usage of the data. In this book, we introduce different types of biological data missing scenarios and propose machine learning models to improve the data analysis, including deep recurrent neural network recovery for feature missings, robust information theoretic learning for label missings and structure-aware rebalancing for minor sample missings. Models in the book cover the fields of imbalance learning, deep learning, recurrent neural network and statistical inference, providing a wide range of references of the integration between artificial intelligence and biology. With simulated and biological datasets, we apply approaches to a variety of biological tasks, including single-cell characterization, genome-wide association studies, medical image segmentations, and quantify the performances in a number of successful metrics. The outline of this book is as follows. In Chapter 2, we introduce the statistical recovery of missing data features; in Chapter 3, we introduce the statistical recovery of missing labels; in Chapter 4, we introduce the statistical recovery of missing data sample information; finally, in Chapter 5, we summarize the full text and outlook future directions. This book can be used as references for researchers in computational biology, bioinformatics and biostatistics. Readers are expected to have basic knowledge of statistics and machine learning. 410 0$aSpringer theses. 606 $aBiology$xData processing 606 $aBiologia$2thub 606 $aProcessament de dades$2thub 606 $aAprenentatge automātic$2thub 606 $aEstructures de dades (Informātica)$2thub 606 $aEstadística matemātica$2thub 608 $aLlibres electrōnics$2thub 615 0$aBiology$xData processing. 615 7$aBiologia 615 7$aProcessament de dades 615 7$aAprenentatge automātic 615 7$aEstructures de dades (Informātica) 615 7$aEstadística matemātica 676 $a570.285 700 $aBao$b Feng$0851645 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466406003316 996 $aComputational Reconstruction of Missing Data in Biological Research$91901566 997 $aUNISA