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010   $a981-16-5992-3
010   $a981-16-5993-1
010   $a981-16-5993-1
024 7 $a10.1007/978-981-16-5993-5
035   $a(MiAaPQ)EBC6882493
035   $a(Au-PeEL)EBL6882493
035   $a(CKB)21069312200041
035   $a(PPN)26083100X
035   $a(OCoLC)1295701557
035   $a(DE-He213)978-981-16-5993-5
035   $a(EXLCZ)9921069312200041
100   $a20220204d2022      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aMachine Learning and Systems Biology in Genomics and Health /$fedited by Shailza Singh
205   $a1st ed. 2022.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2022.
215   $a1 online resource
225 1 $aBiomedical and Life Sciences Series
320   $aIncludes bibliographical references.
327   $aChapter 1: Construction of feedforward multilayer perceptron model for diagnosing leishmaniasis using transcriptome datasets and cognitive computing -- Chapter2- Big data in drug discovery -- Chapter3 - An overview of databases and tools for lncRNA genomics advancing precision medicine -- Chapter 4-Machine Learning in Genomics -- Chapter 5-How Machine Learning has revolutionized the field of Cancer Informatics? -- Chapter 6- Connecting the dots: Using machine learning to Forge Gene Regulatory Networks from large biological datasets -- Chapter 7-Identification of novel Non-coding RNAs in Plants by Big data analysis -- Chapter 8-Artificial Intelligence in Biomedical Image Processing -- Chapter 9- Artificial Intelligence and its Application in Cardiovascular Disease Management.
330   $aThis book discusses the application of machine learning in genomics. Machine Learning offers ample opportunities for Big Data to be assimilated and comprehended effectively using different frameworks. Stratification, diagnosis, classification and survival predictions encompass the different health care regimes representing unique challenges for data pre-processing, model training, refinement of the systems with clinical implications. The book discusses different models for in-depth analysis of different conditions. Machine Learning techniques have revolutionized genomic analysis. Different chapters of the book describe the role of Artificial Intelligence in clinical and genomic diagnostics. It discusses how systems biology is exploited in identifying the genetic markers for drug discovery and disease identification. Myriad number of diseases whether be infectious, metabolic, cancer can be dealt in effectively which combines the different omics data for precision medicine. Major breakthroughs in the field would help reflect more new innovations which are at their pinnacle stage. This book is useful for researchers in the fields of genomics, genetics, computational biology and bioinformatics.
410  0$aBiomedical and Life Sciences Series
606   $aGenomics
606   $aMolecular genetics
606   $aBioinformatics
606   $aBiology$xTechnique
606   $aGene expression
606   $aCancer$xGenetic aspects
606   $aGenomics
606   $aMolecular Genetics
606   $aComputational and Systems Biology
606   $aGenomic Analysis
606   $aGene Expression Analysis
606   $aCancer Genetics and Genomics
615  0$aGenomics.
615  0$aMolecular genetics.
615  0$aBioinformatics.
615  0$aBiology$xTechnique.
615  0$aGene expression.
615  0$aCancer$xGenetic aspects.
615 14$aGenomics.
615 24$aMolecular Genetics.
615 24$aComputational and Systems Biology.
615 24$aGenomic Analysis.
615 24$aGene Expression Analysis.
615 24$aCancer Genetics and Genomics.
676   $a572.86028
702   $aSingh$b Shailza
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910743219803321
996   $aMachine learning and systems biology in genomics and health$93560242
997   $aUNINA