02930nam a22004575i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001300153050001400166072001600180072002300196072001500219100007500234245010000309250001800409264007200427300006600499336002600565337002600591338003600617347002400653490006100677505032700738520100701065650002902072650002202101650002702123650002902150650002202179650003202201710003402233773002602267776003602293776003602329830006102365856004602426978-981-97-6703-8DE-He21320251030112933.0cr nn 008mamaa240902s2024    si |    s    |||| 0|eng d  a97898197670387 a10.1007/978-981-97-6703-82doi 4aQ334-342 4aTA347.A78 7aUYQ2bicssc 7aCOM0040002bisacsh 7aUYQ2thema1 aRaza, Khalid.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aMachine Learning in Single-Cell RNA-seq Data Analysish[electronic resource] /cby Khalid Raza.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aXVIII, 88 p. 25 illus., 16 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSpringerBriefs in Computational Intelligence,x2625-37120 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.  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. . 0aArtificial intelligence. 0aMachine learning. 0aQuantitative research.14aArtificial Intelligence.24aMachine Learning.24aData Analysis and Big Data.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981976702108iPrinted edition:z9789819767045 0aSpringerBriefs in Computational Intelligence,x2625-371240uhttps://doi.org/10.1007/978-981-97-6703-8