Bioinformatics in Agriculture
-10%
portes grátis
Bioinformatics in Agriculture
Next Generation Sequencing Era
Yadav, Dinesh; Sharma, Pradeep; Gaur, R.K.
Elsevier Science & Technology
04/2022
706
Mole
Inglês
9780323897785
15 a 20 dias
1770
Descrição não disponível.
1. Advances in Agricultural Bioinformatics: Outlook of Multi-Omics Approaches 2. Promises and Benefits ?of Omic approaches to Data driven science industries 3. Bioinformatics intervention in functional genomics: current status and future perspective - An overview 4. Genome informatics: Present status and Future Prospects in agriculture 5. Genomics and its applications for crop improvement why not here crop specific like cotton, musrard, wheat 6. Genome-wide Predictions, Structural and Functional Annotations of Plant Transcription Factor Gene Families: A Bioinformatics Approach 7. Proteomics and their applications to understand the biology of agricultural crops 8. Metabolomics and Sustainable Agriculture: Concepts, Applications and Perspectives 9. Plant Metabolomics: A New Era in the Advancement of Agricultural Research 10. Exploring NGS-based RNA-Seq Transcriptomes of Crops Responding to Abiotic Stress 11. Identification of novel RNAs in plants with the help of Next Generation Sequencing Technologies 12. Molecular Evolution, Three Dimensional Structural Characteristics, Mechanism of Action and Functions of Plant Beta-galactosidases 13. Next generation genomics: toward decoding domestication history of crops 14. In-silico identification of Small RNAs, a tiny silent tool against agriculture pest 15. Bioinformatics-assisted multi-omics approaches to improve the agronomic traits in cotton 16. Omics-assisted Understanding of BPH Resistance in Rice: Current Updates and Future Prospective 17. Contemporary Genomic Approaches in Modern Agriculture for Improving Tomato Varieties 18. Characterization of drought tolerance in maize Omics approaches 19. Deciphering the genomic hotspots in wheat for key breeding traits using comparative and structural genomics 20. Prospects of molecular markers for wheat improvement in post genomic era 21. Omics Approaches for Biotic, Abiotic and Quality Traits Improvement in Potato (Solanum tuberosum L.) 22. Tea plant genome sequencing: prospect for crop improvement through genomics tools 23. Next Generation Sequencing and Viroid Research 24. Computational analysis for plants Virus identification?Using Next Generation Sequencing 25. Microbial degradation of herbicides in contaminated soils by following computational approaches 26. Chloroplast genome and Plant-Virus Interaction 27. Deciphering soil microbiota using metagenomic approach for sustainable agriculture: An overview 28. Concepts and Applications of Bioinformatics for Sustainable Agriculture 29. Application of high throughput structural and functional genomic technologies in crop nutrition research 30. Bioinformatics approach for whole transcriptomics-based marker prediction in agriculture crops 31. Computational approaches towards SNP discovery and its applications in plant breeding 32. Bioinformatics intervention in identification and development of molecular markers: An Overview 33. Deciphering comparative and structural variation that regulates abiotic stress response 34. Deep Learning Applied to Computational Biology and Agricultural Sciences 35. Image processing based artificial intelligence system for rapid detection of plant diseases 36. Uses and Applications of Artificial intelligence and Big Data in agriculture: Smart Farming 37. Artificial Intelligence: The future of Agricultural Sciences
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?AFLP; Abiotic and quality traits; Abiotic stress; Active site; Agricultural sciences; Agriculture; Agriculture robot; Alternative splicing; Artificial intelligence; Artificial neural network; BLAST; Big data; Biodegradation; Bioinformatic; Bioinformatics; Biomarkers; Biotechnology; Biotic; Biotic stress; Breeding; Brown planthopper (BPH); Camellia sinensis; Chlorophyll; Chloroplast; Chloroplast genome; Chlorosis; Comparative genomics; Computational approaches; Computational biology; Computational intelligence; Computational methods; Convolutional neural network; Cotton; Crop; Crop breeding; Crop improvement; Crop scouting; Crops; DNA sequence; DNA sequencing; DNA-binding domain; Data integration; Data mining; Data repositories; Databases; Deep Learning; Deep learning; Detection and diagnosis; Differentially expressed genes; Disease synergism; Domestication; Drone analytics; Drought; EQTL; Environmental stress; Epigenome; Evolution of crops; Evolutionary genomics; Fiber; Field mapping; Forecasting; Functional genomics; Gene expression; Gene family; Gene ontology; Gene prediction; Genes; Genetic mapping; Genetic markers; Genome; Genome analysis; Genome annotation; Genome assembly; Genome hotspots; Genome informatics; Genome mapping; Genome-wide; Genome-wide association studies; Genome-wide association study (GWAS); Genomic methods; Genomics; Genotyping; Herbicides; High-throughput genotyping; Homology modeling; Hyperspectral reflection; Image processing; Ionomics; MAS; MFEI; Machine Learning; Machine learning; Maize; Mass spectrometry; Metabolic engineering; Metabolic modeling; Metabolite extraction; Metabolite profiling; Metabolomic databases; Metabolomics
1. Advances in Agricultural Bioinformatics: Outlook of Multi-Omics Approaches 2. Promises and Benefits ?of Omic approaches to Data driven science industries 3. Bioinformatics intervention in functional genomics: current status and future perspective - An overview 4. Genome informatics: Present status and Future Prospects in agriculture 5. Genomics and its applications for crop improvement why not here crop specific like cotton, musrard, wheat 6. Genome-wide Predictions, Structural and Functional Annotations of Plant Transcription Factor Gene Families: A Bioinformatics Approach 7. Proteomics and their applications to understand the biology of agricultural crops 8. Metabolomics and Sustainable Agriculture: Concepts, Applications and Perspectives 9. Plant Metabolomics: A New Era in the Advancement of Agricultural Research 10. Exploring NGS-based RNA-Seq Transcriptomes of Crops Responding to Abiotic Stress 11. Identification of novel RNAs in plants with the help of Next Generation Sequencing Technologies 12. Molecular Evolution, Three Dimensional Structural Characteristics, Mechanism of Action and Functions of Plant Beta-galactosidases 13. Next generation genomics: toward decoding domestication history of crops 14. In-silico identification of Small RNAs, a tiny silent tool against agriculture pest 15. Bioinformatics-assisted multi-omics approaches to improve the agronomic traits in cotton 16. Omics-assisted Understanding of BPH Resistance in Rice: Current Updates and Future Prospective 17. Contemporary Genomic Approaches in Modern Agriculture for Improving Tomato Varieties 18. Characterization of drought tolerance in maize Omics approaches 19. Deciphering the genomic hotspots in wheat for key breeding traits using comparative and structural genomics 20. Prospects of molecular markers for wheat improvement in post genomic era 21. Omics Approaches for Biotic, Abiotic and Quality Traits Improvement in Potato (Solanum tuberosum L.) 22. Tea plant genome sequencing: prospect for crop improvement through genomics tools 23. Next Generation Sequencing and Viroid Research 24. Computational analysis for plants Virus identification?Using Next Generation Sequencing 25. Microbial degradation of herbicides in contaminated soils by following computational approaches 26. Chloroplast genome and Plant-Virus Interaction 27. Deciphering soil microbiota using metagenomic approach for sustainable agriculture: An overview 28. Concepts and Applications of Bioinformatics for Sustainable Agriculture 29. Application of high throughput structural and functional genomic technologies in crop nutrition research 30. Bioinformatics approach for whole transcriptomics-based marker prediction in agriculture crops 31. Computational approaches towards SNP discovery and its applications in plant breeding 32. Bioinformatics intervention in identification and development of molecular markers: An Overview 33. Deciphering comparative and structural variation that regulates abiotic stress response 34. Deep Learning Applied to Computational Biology and Agricultural Sciences 35. Image processing based artificial intelligence system for rapid detection of plant diseases 36. Uses and Applications of Artificial intelligence and Big Data in agriculture: Smart Farming 37. Artificial Intelligence: The future of Agricultural Sciences
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
?AFLP; Abiotic and quality traits; Abiotic stress; Active site; Agricultural sciences; Agriculture; Agriculture robot; Alternative splicing; Artificial intelligence; Artificial neural network; BLAST; Big data; Biodegradation; Bioinformatic; Bioinformatics; Biomarkers; Biotechnology; Biotic; Biotic stress; Breeding; Brown planthopper (BPH); Camellia sinensis; Chlorophyll; Chloroplast; Chloroplast genome; Chlorosis; Comparative genomics; Computational approaches; Computational biology; Computational intelligence; Computational methods; Convolutional neural network; Cotton; Crop; Crop breeding; Crop improvement; Crop scouting; Crops; DNA sequence; DNA sequencing; DNA-binding domain; Data integration; Data mining; Data repositories; Databases; Deep Learning; Deep learning; Detection and diagnosis; Differentially expressed genes; Disease synergism; Domestication; Drone analytics; Drought; EQTL; Environmental stress; Epigenome; Evolution of crops; Evolutionary genomics; Fiber; Field mapping; Forecasting; Functional genomics; Gene expression; Gene family; Gene ontology; Gene prediction; Genes; Genetic mapping; Genetic markers; Genome; Genome analysis; Genome annotation; Genome assembly; Genome hotspots; Genome informatics; Genome mapping; Genome-wide; Genome-wide association studies; Genome-wide association study (GWAS); Genomic methods; Genomics; Genotyping; Herbicides; High-throughput genotyping; Homology modeling; Hyperspectral reflection; Image processing; Ionomics; MAS; MFEI; Machine Learning; Machine learning; Maize; Mass spectrometry; Metabolic engineering; Metabolic modeling; Metabolite extraction; Metabolite profiling; Metabolomic databases; Metabolomics
