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Indigo Introduction to Bioinformatics and Machine Learning | Erin Mills Town Centre
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Introduction to Bioinformatics and Machine Learning 1st Edition bridges the gap between biological data analysis and machine learning techniques, offering a foundational understanding of bioinformatics concepts and practical applications of machine learning in biological research. It covers key topics such as sequence analysis, omics data integration, predictive modelling, RNA and DNA sequencing, and algorithm development, with real-world examples and case studies. The need for this book arises from the rapid growth of biological data and the increasing demand for tools to analyze and interpret it effectively. Unlike existing resources, this textbook provides a balanced approach to both theoretical concepts and hands-on problem-solving, making it suitable for readers with diverse backgrounds in biology, computer science, and data science. The scope includes introductory material, advanced applications, and emerging trends, ensuring depth and relevance for learners and practitioners alike, and is a comprehensive resource for undergraduate and graduate students, as well as professionals transitioning into these fields.
Features integration of bioinformatics and machine learning concepts
Provides hands-on examples and case studies with real data
Includes stepwise approach to building machine learning skills
Offers ancillary material, including lecture slides, practise datasets and quizzes, to support the learning experience
Introduction to Bioinformatics and Machine Learning 1st Edition bridges the gap between biological data analysis and machine learning techniques, offering a foundational understanding of bioinformatics concepts and practical applications of machine learning in biological research. It covers key topics such as sequence analysis, omics data integration, predictive modelling, RNA and DNA sequencing, and algorithm development, with real-world examples and case studies. The need for this book arises from the rapid growth of biological data and the increasing demand for tools to analyze and interpret it effectively. Unlike existing resources, this textbook provides a balanced approach to both theoretical concepts and hands-on problem-solving, making it suitable for readers with diverse backgrounds in biology, computer science, and data science. The scope includes introductory material, advanced applications, and emerging trends, ensuring depth and relevance for learners and practitioners alike, and is a comprehensive resource for undergraduate and graduate students, as well as professionals transitioning into these fields.
Features integration of bioinformatics and machine learning concepts
Provides hands-on examples and case studies with real data
Includes stepwise approach to building machine learning skills
Offers ancillary material, including lecture slides, practise datasets and quizzes, to support the learning experience
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