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Название: Data Science for Genomics
Автор: Amit Kumar Tyagi, Ajith Abraham
Издательство: Academic Press/Elsevier
Год: 2023
Страниц: 314
Язык: английский
Формат: pdf (true)
Размер: 13.6 MB
Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making. Sections cover Data Science, Machine Learning, Deep Learning, data analysis, and visualization techniques. The authors then present the fundamentals of Genomics, Genetics, Transcriptomes and Proteomes as basic concepts of molecular biology, along with DNA and key features of the human genome, as well as the genomes of eukaryotes and prokaryotes. Automated ML, abbreviated and popularly known as AutoML, is a process of applying automation to the ML life cycle with the aim to automate the repetitive tasks of it. This will provide an edge to the technology by not only democratizing it and making it accessible to all but will have various other advantages like reduction of the model run time, a well-tuned model, various evaluation metrics to judge the model performance, etc.
Автор: Amit Kumar Tyagi, Ajith Abraham
Издательство: Academic Press/Elsevier
Год: 2023
Страниц: 314
Язык: английский
Формат: pdf (true)
Размер: 13.6 MB
Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making. Sections cover Data Science, Machine Learning, Deep Learning, data analysis, and visualization techniques. The authors then present the fundamentals of Genomics, Genetics, Transcriptomes and Proteomes as basic concepts of molecular biology, along with DNA and key features of the human genome, as well as the genomes of eukaryotes and prokaryotes. Automated ML, abbreviated and popularly known as AutoML, is a process of applying automation to the ML life cycle with the aim to automate the repetitive tasks of it. This will provide an edge to the technology by not only democratizing it and making it accessible to all but will have various other advantages like reduction of the model run time, a well-tuned model, various evaluation metrics to judge the model performance, etc.