Titlebook: Applications of Bioinformatics in Rice Research; Manoj Kumar Gupta,Lambodar Behera Book 2021 The Editor(s) (if applicable) and The Author(

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Empirische Verfahren zur Klassifikation. The QTL mapping was carried in F., RILs, NILs, backcrossing populations, and double haploids (DH) in rice to study the performance of respective QTLs and its genetic basis of expression in grain yield and other traits as well as other parameters of yield like biotic and disease resistance. Various

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https://doi.org/10.1007/978-3-322-91773-7es of unknown genes by using natural variations and mutations in target plants. These metabolomic methods could be useful in crop breeding, where important plant traits like taste, yield, and grain yield quality are strongly linked to metabolic conditions. Various data analysis techniques are used f

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Instrumente und Methoden der Wahlforschung, used to identify such modifications in rice that have an effect on growth, development as well as biotic and abiotic stress in the genome region. DNA methylation is regulated in rice by several regulators that are involved in both adaptation and agronomic efficiency. This, in turn, indicates that a

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https://doi.org/10.1007/978-3-662-49299-4, agriculture automation is a big concern for all countries to feed and provide food security to their population. This book chapter focuses on evaluating and exploring a variety of published solutions, methods, and perspectives on “artificial intelligence (AI),” “machinelearning”, “big data applica

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Nichtstationarität und Kointegrationts in the public domain could cause unacknowledged use of such research for commercial gains by other agencies both within and outside the country. Moreover, the protection of IPR generates an incentive for more knowledge and technology generation, as innovators are recognized and rewarded. However,

CLAMP

Manoj Kumar Gupta,Lambodar BeheraSummarizes bioinformatics tools for analyzing the metabolomics, polyploidy genome, and extra-chromosomal genome in rice.Highlights the use of artificial intelligence and machine learning in rice resea

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https://doi.org/10.1007/978-981-16-3997-5Bioinformatics; Rice; High throughput sequencing; Algorithms; Machine learning

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