Practical Bioinformatics

Algorithms in Bioinformatics: A Practical Introduction Genome Rearrangement Evidences of Genome Rearrangement

Machine Learning Approaches to Bioinformatics

The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers ...

Machine Learning Approaches to Bioinformatics

The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers ...

Novel Applications of Immuno-bioinformatics in Vaccine and Bio-product Developments at Research Institutes

There are many challenges in the field of public health sciences. Rational decisions are required in order to treat different diseases, gain knowledge and wealth regarding research, and produce biological or synthetic products. Various advances in the basic laboratory science, computer science, and the engineering of biological production processes can help solve the occurring problems. Bioinfo...

Machine Learning Approaches to Bioinformatics

The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers ...

Machine Learning Approaches to Bioinformatics

The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers ...