The authors of this text present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students: firstly those biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function; and secondly those with a primary background in physics, mathematics, statistics or computer science who need to know more about specific applications in molecular biology.
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The authors of this text present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students: firstly those biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function; and secondly those with a primary background in physics, mathematics, statistics or computer science who need to know more about specific applications in molecular biology.
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