Data analytics in bioinformatics : a machine learning perspective

"Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more"-- Provided by publisher 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