Data analytics in bioinformatics : a machine learning perspective / edited by Rabinarayan Satpathy, Tanupriya Choudhury, Suneeta Satpathy, Sachi Nandan Mohanty, and Xiaobo Zhang.
Contributor(s): Satpathy, Rabinarayan [editor.]
| Choudhury, Tanupriya [editor.] | Satpathy, Suneeta [editor.] | Mohanty, Sachi Nandan [editor.].
Material type: 
BookPublisher: Hoboken, NJ : Wiley-Scrivener, 2021Edition: First edition.Description: 1 online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781119785620; 1119785626; 9781119785606; 111978560X; 9781119785613; 1119785618.Subject(s): Bioinformatics | Artificial intelligence -- Biological applications | Artificial intelligence -- Biological applications | BioinformaticsGenre/Form: Electronic books.Additional physical formats: Print version:: Data analytics in bioinformaticsDDC classification: 570.285 Online resources: Wiley Online Library Summary: "Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more"-- Provided by publisher.
Includes bibliographical references and index.
"Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more"-- Provided by publisher.
Description based on print version record and CIP data provided by publisher; resource not viewed.
Wiley Frontlist Obook All English 2021
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