Data Analytics in Bioinformatics A Machine Learning Perspective.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Satpathy, Rabinarayan
Otros Autores: Choudhury, Tanupriya, Satpathy, Suneeta, Mohanty, Sachi Nandan, Zhang, Xiaobo
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2021.
Temas:
Bioinformatics.
Bio-informatique.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Half-Title Page
  • Series Page
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Acknowledgement
  • Part 1: THE COMMENCEMENT OF MACHINE LEARNING SOLICITATION TO BIOINFORMATICS
  • 1 Introduction to Supervised Learning
  • 1.1 Introduction
  • 1.2 Learning Process & its Methodologies
  • 1.2.1 Supervised Learning
  • 1.2.2 Unsupervised Learning
  • 1.2.3 Reinforcement Learning
  • 1.3 Classification and its Types
  • 1.4 Regression
  • 1.4.1 Logistic Regression
  • 1.4.2 Difference between Linear & Logistic Regression
  • 1.5 Random Forest
  • 1.6 K-Nearest Neighbor
  • 1.7 Decision Trees
  • 1.8 Support Vector Machines
  • 1.9 Neural Networks
  • 1.10 Comparison of Numerical Interpretation
  • 1.11 Conclusion & Future Scope
  • References
  • 2 Introduction to Unsupervised Learning in Bioinformatics
  • 2.1 Introduction
  • 2.2 Clustering in Unsupervised Learning
  • 2.3 Clustering in Bioinformatics-Genetic Data
  • 2.3.1 Microarray Analysis
  • 2.3.2 Clustering Algorithms
  • 2.3.3 Partition Algorithms
  • 2.3.4 Hierarchical Clustering Algorithms
  • 2.3.5 Density-Based Approach
  • 2.3.6 Model-Based Approach
  • 2.3.7 Grid-Based Clustering
  • 2.3.8 Soft Clustering
  • 2.4 Conclusion
  • References
  • 3 A Critical Review on the Application of Artificial Neural Network in Bioinformatics
  • 3.1 Introduction
  • 3.1.1 Different Areas of Application of Bioinformatics
  • 3.1.2 Bioinformatics in Real World
  • 3.1.3 Issues with Bioinformatics
  • 3.2 Biological Datasets
  • 3.3 Building Computational Model
  • 3.3.1 Data Pre-Processing and its Necessity
  • 3.3.2 Biological Data Classification
  • 3.3.3 ML in Bioinformatics
  • 3.3.4 Introduction to ANN
  • 3.3.5 Application of ANN in Bioinformatics
  • 3.3.6 Broadly Used Supervised Machine Learning Techniques
  • 3.4 Literature Review
  • 3.4.1 Comparative Analysis of ANN With Broadly Used Traditional ML Algorithms
  • 3.5 Critical Analysis
  • 3.6 Conclusion
  • References
  • Part 2: MACHINE LEARNING AND GENOMIC TECHNOLOGY, FEATURE SELECTION AND DIMENSIONALITY REDUCTION
  • 4 Dimensionality Reduction Techniques: Principles, Benefits, and Limitations
  • 4.1 Introduction
  • 4.2 The Benefits and Limitations of Dimension Reduction Methods
  • 4.3 Components of Dimension Reduction
  • 4.3.1 Feature Selection
  • 4.3.2 Feature Reduction
  • 4.4 Methods of Dimensionality Reduction
  • 4.4.1 Principal Component Analysis (PCA)
  • 4.4.2 Missing Values Ratio (MVR)
  • 4.4.3 Linear Discriminant Analysis (LDA)
  • 4.4.4 Backward Feature Elimination (BFE)
  • 4.4.5 Forward Feature Construction (FFC)
  • 4.4.6 Independent Component Analysis (ICA)
  • 4.4.7 Low Variance Filter (LVF)
  • 4.4.8 High Correlation Filter
  • 4.4.9 Random Forests (RF)/Ensemble Trees
  • 4.4.10 t-Distributed Stochastic Neighbor Embedding (t-SNE)
  • 4.4.11 Autoencoder
  • 4.4.12 Factor Analysis (FA)
  • 4.4.13 Uniform Manifold Approximation and Projection (UMAP)
  • 4.4.14 Information Gain (IG)
  • 4.4.15 Vector Quantization (VQ)
  • 4.5 Conclusion
  • References

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