Data Analytics in Bioinformatics. Группа авторов

14.11 Left occipital recording.Figure 14.12 Right occipital region recording.Figure 14.13 Right outer canthus region recording.Figure 14.14 Left outer canthus region recording (typical activity of the EOG (L...Figure 14.15 Chin EMG region recording (typical activity of the EMG (EMG-X1)).Figure 14.16 Limb EMG (left leg) region recording (typical activity of the EMG (...Figure 14.17 Limb EMG (Right Leg) Region Recording (Typical activity of the EMG ...Figure 14.18 Sleep stages behavior of the subjects (a) Subject-16, (b) Subject-1...Figure 14.19 Framework of the proposed research work.Figure 14.20 Performance graphs of the proposed SleepEEG test model with the C4-...

      14 Chapter 15Figure 15.1 Electrode placement method.Figure 15.2 EEG delta, theta, alpha, beta and gamma [7].Figure 15.3 Steps involved in processing of EEG signal.

      15 Chapter 16Figure 16.1 Bioinformatics suit.Figure 16.2 Amount of data stored by EBI over the years [3].Figure 16.3 Depicts prokaryotic & eukaryotic cells.Figure 16.4 Eukaryotic nuclear DNA within the chromosomes.Figure 16.5 Depicts RNA transcription process shows a strand RNA given from a do...Figure 16.6 Depicts transcription and translation describing the process of conv...Figure 16.7 Depicts a protein handshake.Figure 16.8 Depicts Protein–Protein interaction render to hotspot.Figure 16.9 Complex plane of unit circle taken for 20 complex numbers in CPNR.Figure 16.10 Mapping of amino acids using CPNR.Figure 16.11 Mapping of amino acids using EIIP.Figure 16.12 Depicts Flow of EIIP, CPNR mapping with DWT for hotspots identifica...Figure 16.13 CPNR results.Figure 16.14 EIIP results.Figure 16.15 Confusion matrix.Figure 16.16 Precision calcuation details.Figure 16.17 Recall calculation details.Figure 16.18 Overall performance analysis visualization.

      16 Chapter 18Figure 18.1 Overview of experimental methodology.Figure 18.2 Correlation heatmap.Figure 18.3 Encoding process.

      17 Chapter 19Figure 19.1 Framework of the book chapter.Figure 19.2 Actual vs predicted behavior BSE February, 2001.Figure 19.3 Actual vs predicted behavior BSE February, 2004.Figure 19.4 Actual vs predicted behavior BSE February, 2005.Figure 19.5 Actual vs predicted behavior BSE February, 2008.Figure 19.6 Actual vs predicted behavior BSE February, 2010.Figure 19.7 Actual vs predicted behavior BSE March, 2001.Figure 19.8 Actual vs predicted behavior BSE March, 2002.Figure 19.9 Actual vs predicted behavior BSE March, 2003.Figure 19.10 Actual vs predicted behavior BSE March, 2006.Figure 19.11 Actual vs predicted behavior BSE March, 2008.Figure 19.12 Actual vs predicted behavior BSE March, 2010.Figure 19.13 Actual vs predicted behavior BSE April, 2001.Figure 19.14 Actual vs predicted behavior BSE April, 2003.Figure 19.15 Actual vs predicted behavior BSE April, 2004.Figure 19.16 Actual vs predicted behavior BSE April, 2007.Figure 19.17 Actual vs predicted behavior BSE April, 2008.Figure 19.18 Actual vs predicted behavior BSE May, 2001.Figure 19.19 Actual vs predicted behavior BSE May, 2002.Figure 19.20 Actual vs predicted behavior BSE October, 2002.Figure 19.21 Actual vs predicted behavior BSE October, 2003.

      18 Chapter 20Figure 20.1 Framework for prediction using Markov model.Figure 20.2 Example for zero order Markov model of BSE (2001).Figure 20.3 Example for first order Markov model of BSE (2001).Figure 20.4 Behavior of BSE, January 2005 (actual vs predicted).Figure 20.5 Behavior of BSE, February 2005 (actual vs predicted).Figure 20.6 Behavior of BSE, March 2005 (actual vs predicted).Figure 20.7 Behavior of BSE, March 2010 (actual vs predicted).Figure 20.8 First two days prediction accuracy percentage of BSE (November).Figure 20.9 First four days prediction accuracy percentage of BSE (November).Figure 20.10 First six days prediction accuracy percentage of BSE (November).

      List of Tables

      1 Chapter 1Table 1.1 Regression statistics.Table 1.2 AUC: Logistic regression.Table 1.3 Difference between linear & logistic regression.Table 1.4 AUC: Random forest.Table 1.5 AUC: K-nearest neighbor.Table 1.6 AUC: Decision trees.Table 1.7 AUC: Support vector machines.Table 1.8 AUC: Neural network.Table 1.9 AUC: Comparison of numerical interpretations.

      2 Chapter 2Table 2.1 Gene expression data matrix representation.

      3 Chapter 3Table 3.1 Shows published articles of ANN used for biological data.

      4 Chapter 5Table 5.1 Highlights the important work of literature published in the last two ...Table 5.2 Depicts the features influencing each of the 9 principal components.

      5 Chapter 6Table 6.1 Published studies of multi-level omics data integration using unsuperv...Table 6.2 Published studies of multi-level omics data integration using supervis...

      6 Chapter 7Table 7.1 The classification accuracy, recall, precision and F-score of ensemble...Table 7.2 The classification accuracy, recall, precision and F-score of ensemble...Table 7.3 The classification accuracy, recall, precision and F-score of ensemble...

      7 Chapter 8Table 8.1 Dataset Attributes.

      8 Chapter 9Table 9.1 Literature Review for Hybridization of GWO Algorithm.Table 9.2 Literature Review for GWO Extension Algorithms.Table 9.3 Literature Review for GWO Modification Algorithm.Table 9.4 Literature Review for GWO in Medical Applications.Table 9.5 Literature Review for GWO in Medical Application for Microarray Datase...

      9 Chapter 12Table 12.1 Sample dataset.Table 12.2 Performance analysis of the proposed algorithms.

      10 Chapter 13Table 13.1 SNR Comparison of different images.

      11 Chapter 14Table 14.1 Composed channels of polysomnography.Table 14.2 Details of enrolled subjects in this proposed work.Table 14.3 Description of epochs of various sleep stages from four subjects used...Table 14.4 Confusion matrix performance achieved by the SleepEEG study using C4-...Table 14.5 Performances Statistics of Subjects with input of C4-A1 channel sleep...

      12 Chapter 15Table 15.1 Different part of cerebrum and its operations.Table 15.2 Literature Review on the Analysis of EEG Signal for Detection of Schi...

      13 Chapter 16Table 16.1 Genetic code describing 64 possible codons and the corresponding amin...Table 16.2 Amino acids listed in table with 3-letter and 1-letter codes.Table 16.3 Depicts EIIP mapping for amino acids.Table 16.4 Number of codons for 20 amino acids.Table 16.5 Different pattern among each of the non-composable number and its bes...Table 16.6 Complex Prime Numerical Representation (CPNR).Table 16.7 Dataset of protein sequences used for experiment.Table 16.8 Details of identified hotspots using CPNR.Table 16.9 Details of identified hotspots using EIIP.Table 16.10 Analysis of experimental results.

      14 Chapter 18Table 18.1 Data types of features.Table 18.2 Comparison of Performance Metrics on various Machine Learning Models.Table 18.3 Accuracy on application of 10-fold cross validation of logistic regre...

      15 Chapter 19Table 19.1 Hamming distance result year wise.

      16 Chapter 20Table 20.1 Example for second order Markov model of BSE (2001).Table 20.2 Hamming distance of BSE (2002 to 2014) using zero order Markov model.Table 20.3 Hamming distance of BSE (2002 to 2014) using first order Markov model...Table 20.4 Hamming distance of BSE (2002 to 2014) using Second order Markov mode...

      Guide

      1  Cover

      2 Table of Contents

      3  Title page

      4  Copyright

      5  Preface

      6  Acknowledgement

      7  Begin Reading

      8  Index

      9  End User License Agreement

      Pages

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