£109.07

Springer Statistical Modelling and Machine Learning for Bioinformatics

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Description

This comprehensive guide explores the intersection of statistical modelling, machine learning, and bioinformatics, presenting cutting-edge research methodologies for extracting knowledge from biological data. Published by Springer as part of the Algorithms for Intelligent Systems series, the book demonstrates how computational techniques and machine learning drive innovation across bioinformatics applications. Discover practical approaches to drug design, gene therapy, proteomics analysis, and agricultural biotechnology. The content bridges theory and application, equipping researchers and professionals with essential tools for leveraging data science in life sciences and biomedical research.

Key Features

Covers statistical modelling and machine learning principles specifically applied to bioinformatics research and practice.

Explores practical applications in drug design, gene therapy, proteomics, and agricultural biotechnology using computational methods.

Part of Springer's Algorithms for Intelligent Systems series, providing authoritative coverage of latest bioinformatics techniques.

Demonstrates knowledge extraction from biological data through integrated machine learning and computing approaches.

Combines theoretical foundations with real-world bioinformatics tools and methodologies for professional practitioners.

Addresses current research trends in how machine learning transforms biological data analysis and discovery.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
31 January 2020
Listed Since
22 November 2019

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No barcode data available

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