LEADER 03185nam 22005895 450 001 9910886083403321 005 20250807143453.0 010 $a9789819767038$b(eBook) 024 7 $a10.1007/978-981-97-6703-8 035 $a(MiAaPQ)EBC31642016 035 $a(Au-PeEL)EBL31642016 035 $a(CKB)34774646900041 035 $a(DE-He213)978-981-97-6703-8 035 $a(OCoLC)1455131479 035 $a(EXLCZ)9934774646900041 100 $a20240902d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning in Single-Cell RNA-seq Data Analysis /$fby Khalid Raza 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (xviii, 88 pages) $cillustrations 225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3712 311 08$a9789819767021 320 $aIncludes bibliographical references. 327 $aChapter 1. Introduction to Single-Cell RNA-seq Data Analysis -- Chapter 2. Preprocessing and Quality Control -- Chapter 3. Dimensionality Reduction and Clustering -- Chapter 4. Differential Expression Analysis -- Chapter 5. Trajectory Inference and Cell Fate Prediction -- Chapter 6. Emerging Topics and Future Directions. 330 $aThis book provides a concise guide tailored for researchers, bioinformaticians, and enthusiasts eager to unravel the mysteries hidden within single-cell RNA sequencing (scRNA-seq) data using cutting-edge machine learning techniques. The advent of scRNA-seq technology has revolutionized our understanding of cellular diversity and function, offering unprecedented insights into the intricate tapestry of gene expression at the single-cell level. However, the deluge of data generated by these experiments presents a formidable challenge, demanding advanced analytical tools, methodologies, and skills for meaningful interpretation. This book bridges the gap between traditional bioinformatics and the evolving landscape of machine learning. Authored by seasoned experts at the intersection of genomics and artificial intelligence, this book serves as a roadmap for leveraging machine learning algorithms to extract meaningful patterns and uncover hidden biological insights within scRNA-seq datasets. . 410 0$aSpringerBriefs in Computational Intelligence,$x2625-3712 606 $aArtificial intelligence 606 $aMachine learning 606 $aQuantitative research 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aData Analysis and Big Data 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aQuantitative research. 615 14$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aData Analysis and Big Data. 676 $a006.31 700 $aRaza$b Khalid$01266124 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910886083403321 996 $aMachine Learning in Single-Cell RNA-seq Data Analysis$94430687 997 $aUNINA