| Description |
1 online resource (381 p.) |
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgements -- Abstract -- Authors -- Preface -- 1. Introduction -- 1.1. Introduction -- 1.2. Central Dogma -- 1.3. Measuring Gene Expression -- 1.4. Representation of Gene Expression Data -- 1.5. Gene Expression Data Analysis: Applications -- 1.6. Machine Learning -- 1.7. Statistical and Biological Evaluation -- 1.8. Gene Expression Analysis Approaches -- 1.8.1. Preprocessing in Microarray and RNAseq Data -- 1.8.2. Co-Expressed Pattern-Finding Using Machine Learning |
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1.8.3. Co-Expressed Pattern-Finding Using Network-Based Approaches -- 1.9. Differential Co-Expression Analysis -- 1.10. Differential Expression Analysis -- 1.11. Tools and Systems for Gene Expression Data Analysis -- 1.11.1. (Diff) Co-Expression Analysis Tools and Systems -- 1.11.2. Differential Expression Analysis Tools and Systems -- 1.12. Contribution of This Book -- 1.13. Organization of This Book -- 2. Information Flow in Biological Systems -- 2.1. Concept of Systems Theory -- 2.1.1. A Brief History of Systems Thinking -- 2.1.2. Areas of Application of Systems Theory in Biology |
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2.2. Complexity in Biological Systems -- 2.2.1. Hierarchical Organization of Biological Systems from Macroscopic Levels to Microscopic Levels -- 2.2.2. Information Flow in Biological Systems -- 2.2.3. Top-Down and Bottom-Up Flow -- 2.3. Central Dogma of Molecular Biology -- 2.3.1. DNA Replication -- 2.3.2. Transcription -- 2.3.3. Translation -- 2.4. Ambiguity in Central Dogma -- 2.4.1. Reverse Transcription -- 2.4.2. RNA Replication -- 2.5. Discussion -- 2.5.1. Biological Information Flow from a Computer Science Perspective -- 2.5.2. Future Perspective -- 3. Gene Expression Data Generation |
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3.1. History of Gene Expression Data Generation -- 3.2. Low-Throughput Methods -- 3.2.1. Northern Blotting -- 3.2.2. Ribonuclease Protection Assay -- 3.2.3. qRT-PCR -- 3.2.4. SAGE -- 3.3. High-Throughput Methods -- 3.3.1. Microarray -- 3.3.2. RNA-Seq -- 3.3.3. Types of RNA-Seq -- 3.3.4. Gene Expression Data Repositories -- 3.3.5. Standards in Gene Expression Data -- 3.4. Chapter Summary -- 4. Statistical Foundations and Machine Learning -- 4.1. Introduction -- 4.2. Statistical Background -- 4.2.1. Statistical Modeling -- 4.2.2. Probability Distributions -- 4.2.3. Hypothesis Testing |
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4.2.4. Exact Tests -- 4.2.5. Common Data Distributions -- 4.2.6. Multiple Testing -- 4.2.7. False Discovery Rate -- 4.2.8. Maximum Likelihood Estimation -- 4.3. Machine Learning Background -- 4.3.1. Significance of Machine Learning -- 4.3.2. Machine Learning and Its Types -- 4.3.3. Supervised Learning Methods -- 4.3.4. Unsupervised Learning Methods -- 4.3.5. Outlier Mining -- 4.3.6. Association Rule Mining -- 4.4. Chapter Summary -- 4.4.1. Statistical Modeling -- 4.4.2. Supervised Learning: Classification and Regression Analysis -- 4.4.3. Proximity Measures |
| Notes |
Description based upon print version of record |
|
4.4.4. Unsupervised Learning: Clustering |
| Form |
Electronic book
|
| Author |
Bhattacharyya, Dhruba Kumar
|
|
Kalita, Jugal Kumar
|
| ISBN |
9781000425734 |
|
1000425738 |
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