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Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications

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Cover of 'Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Bioinformatics
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    Chapter 2 Review About Bioinformatics, Databases, Sequence Alignment, Docking, and Drug Discovery
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    Chapter 3 Machine Learning for Bioinformatics
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    Chapter 4 Impact of Machine Learning in Bioinformatics Research
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    Chapter 5 Text Mining in Bioinformatics
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    Chapter 6 Open-Source Software Tools for Bioinformatics
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    Chapter 7 A Study on Protein Structure Prediction
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    Chapter 8 Computational Methods Used in Prediction of Protein Structure
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    Chapter 9 Computational Methods for Inference of Gene Regulatory Networks from Gene Expression Data
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    Chapter 10 Machine-Learning Algorithms for Feature Selection from Gene Expression Data
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    Chapter 11 Unsupervised Techniques in Genomics
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    Chapter 12 Supervised Techniques in Proteomics
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    Chapter 13 Visualizing Codon Usage Within and Across Genomes: Concepts and Tools
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    Chapter 14 Single-Cell Multiomics: Dissecting Cancer
Attention for Chapter 3: Machine Learning for Bioinformatics
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