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211011 ||| eng |
| 020 |
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|a 9789811639975
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| 100 |
1 |
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|a Gupta, Manoj Kumar
|e [editor]
|
| 245 |
0 |
0 |
|a Applications of Bioinformatics in Rice Research
|h Elektronische Ressource
|c edited by Manoj Kumar Gupta, Lambodar Behera
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| 250 |
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|a 1st ed. 2021
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| 260 |
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|a Singapore
|b Springer Nature Singapore
|c 2021, 2021
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| 300 |
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|a XV, 359 p. 21 illus., 16 illus. in color
|b online resource
|
| 505 |
0 |
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|a Chapter-1 Possibility of Uncoding Structural Organization of Genome in Rice: Prospects and Approaches By 3D Genome Sequencing -- Chapter-2 Bioinformatics Approaches for High Density Linkage Mapping in Rice Research -- Chapter-3 Quantitative Trait Locus Mapping in Rice -- Chapter-4 Metabolomics in Rice Improvement -- Chapter-5 Computational Approaches Towards Decoding the Extra Chromosomal Genome of Rice -- Chapter-6 Computational Epigenetics in Rice Research -- Chapter-7 Computational Approaches Towards Understanding Stress in Rice -- Chapter-8 Identifying Complex Polyploidy Genomes Using Bioinformatics Approaches -- Chapter-9 Perspectives and Challenges of Phenotyping in Rice -- Chapter-10 The CRISPR Technology and Application in Rice -- Chapter-11 De Novo Evolution of Genes in Rice -- Chapter-12 Artificial intelligence and machine learning in rice research -- Chapter-13 Intellectual Property and Rice Research -- Chapter-14 Plant Pathogen Co-Evolution in Rice -- Chapter-15 Conservation of Rice Germplasm by Bioinformatics Strategy -- Chapter-16 Recent Advances in Multi-Omics and Breeding Approaches Towards Drought Tolerance in Crops
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| 653 |
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|a Bioinformatics
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| 653 |
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|a Computational and Systems Biology
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| 653 |
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|a Genomics
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| 653 |
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|a Botany
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| 653 |
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|a Plant Science
|
| 700 |
1 |
|
|a Behera, Lambodar
|e [editor]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b Springer
|a Springer eBooks 2005-
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-981-16-3997-5?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
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|a 570.113
|
| 082 |
0 |
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|a 570.285
|
| 520 |
|
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|a This book summarizes the advanced computational methods for mapping high-density linkages and quantitative trait loci in the rice genome. It also discusses the tools for analyzing metabolomics, identifying complex polyploidy genomes, and decoding the extrachromosomal genome in rice. Further, the book highlights the application of CRISPR-Cas technology and methods for understanding the evolutionary development and the de novo evolution of genes in rice. Lastly, it discusses the role of artificial intelligence and machine learning in rice research and computational tools to analyze plant-pathogen co-evolution in rice crops
|