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Data Analytics in Bioinformatics
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Страница 1
Table of Contents
List of Illustrations
List of Tables
Guide
Pages
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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
Страница 32
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
2.3.3.1 k-Means Clustering
2.3.3.2 Cluster Center Initialization Algorithm (CCIA)
2.3.3.3 Intelligent Kernel k-Mean (IKKM)
2.3.3.4 Clustering Large Applications (CLARA)
2.3.4 Hierarchical Clustering Algorithms
2.3.4.1 AGNES (Agglomerative Nesting)
2.3.4.2 DIANA (Divisive Analysis)
2.3.4.3 CURE (Clustering Using Representatives)
2.3.4.4 CHAMELEON
2.3.4.5 BRICH (Balanced Iterative Reducing and Clustering Using Hierarchies)
2.3.5 Density-Based Approach
2.3.5.1 DBSCAN
2.3.6 Model-Based Approach
2.3.6.1 SOM (Self-Organizing Maps)
2.3.7 Grid-Based Clustering
2.3.7.1 STING (Statistical Information Grid-Based Algorithm)
2.3.8 Soft Clustering
2.3.8.1 FCM (Fuzzy Class Membership)
2.4 Conclusion
References
Страница 59
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
3.1.3.1 Issues Related to Structure
3.1.3.2 Sequence Analysis
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
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