Data Analytics in Bioinformatics. Группа авторов
Introduction 17.2 Disease Ontology 17.3 Infectious Disease Ontology 17.4 Biomedical Ontologies on IDO 17.5 Various Methods on IDO 17.6 Machine Learning-Based Ontology for IDO 17.7 Recommendation or Suggestions for Future Study 437 17.8 Conclusions References 18 An Efficient Model for Predicting Liver Disease Using Machine Learning 18.1 Introduction 18.2 Related Works 18.3 Proposed Model 18.4 Results and Analysis 18.5 Conclusion References
9 Part 4: BIOINFORMATICS AND MARKET ANALYSIS 19 A Novel Approach for Prediction of Stock Market Behavior Using Bioinformatics Techniques 19.1 Introduction 19.2 Literature Review 19.3 Proposed Work 19.4 Experimental Study 19.5 Conclusion and Future Work References 20 Stock Market Price Behavior Prediction Using Markov Models: A Bioinformatics Approach 20.1 Introduction 20.2 Literature Survey 20.3 Proposed Work 20.4 Experimental Work 20.5 Conclusions and Future Work References
10 Index
List of Illustrations
1 Chapter 1Figure 1.1 Traditional learning.Figure 1.2 Machine learning.Figure 1.3 Learning behavior of a machine.Figure 1.4 Block diagram of supervised learning.Figure 1.5 Block diagram of unsupervised learning.Figure 1.6 Block diagram of reinforcement learning.Figure 1.7 Concept of classification.Figure 1.8 Classification based on gender.Figure 1.9 Regression.Figure 1.10 Cholesterol line fit plot.Figure 1.11 ROC curve for logistic regression.Figure 1.12 Random forest.Figure 1.13 ROC curve for random forest.Figure 1.14 ROC curve for k-nearest neighbor.Figure 1.15 Decision tree.Figure 1.16 Support vector machine.Figure 1.17 Neural network (general).Figure 1.18 Neural network (detailed).
2 Chapter 2Figure 2.1 Machine learning in bioinformatics.Figure 2.2 Example matrix of gene expression (10 genes in a row and 2 samples in...Figure 2.3 Partition clustering algorithms.Figure 2.4 (a) Agglomerative clustering, (b) divisive clustering.Figure 2.5 Self Organizing Map (SOM).
3 Chapter 3Figure 3.1 Areas of research of bioinformatics [4].Figure 3.2 Simple network architecture of ANN with four input unit [17].Figure 3.3 Single layer perceptron (left) and multilayer perceptron with one hid...
4 Chapter 4Figure 4.1 The Steps of LDA reduction technique.Figure 4.2 The steps of backward feature elimination technique.Figure 4.3 The steps of backward feature elimination technique.Figure 4.4 Low variance ratio feature reduction techniques.Figure 4.5 The steps of the random forest algorithm.
5 Chapter 5Figure 5.1 Flowchart to depict the structure of the book chapter.Figure 5.2 Depicts the different steps in which PC1 is created, by considering a...Figure 5.3 Shows the screen plot which depicts the variance of each of the princ...Figure 5.4 Shows the steps followed in extraction of the features by ORB in case...Figure 5.5 Shows the steps followed in extraction of the features by ORB in case...Figure 5.6 Shows the steps followed in extraction of the features by ORB in case...Figure 5.7 Color histogram comparison between three pairs of images: images of b...
6 Chapter 6Figure 6.1 Multiple levels of omics data in biological system, from genome, epig...Figure 6.2 (a) Hypothesis 1: Molecular variations propagates linearly in a hiera...Figure 6.3 Different integration pipelines of multi-level omics data. (a) Horizo...Figure 6.4 Methods for parallel integration. (a) Concatenation-based, (b) transf...
7 Chapter 7Figure 7.1 Knowledge discovery process in genomic data.Figure 7.2 Decision tree classifier on a dataset having two features (X1 and X2)...Figure 7.3 Ensemble learning model to increase the accuracy of classification mo...Figure 7.4 Bootstrap aggregation (bagging) technique.Figure 7.5 Implementing RF classifier in a dataset having four features (X1…X4) ...Figure 7.6 Genes, proteins and molecular machines.
8 Chapter 8Figure 8.1 Ensemble learning.Figure 8.2 Output label.Figure 8.3 Heat map.Figure 8.4 Correlation matrix.Figure 8.5 Confusion matrix.Figure 8.6 Confusion matrix.Figure 8.7 Comparison of different methods.
9 Chapter 9Figure 9.1 Schematic representation of DNA microarray technology [12].Figure 9.2 Model for microarray data analysis [13].Figure 9.3 Social hierarchy of grey wolves.Figure 9.4 Flow chart of GWO.
10 Chapter 10Figure 10.1 Flowchart for K-mean clustering.Figure 10.2 Linear classifier.Figure 10.3 Hyper plane classifier.Figure 10.4 Optimal line.Figure 10.5 Working process of SVM.Figure 10.6 Hybrid algorithm.Figure 10.7 C—means clustering algorithm.Figure 10.8 Model of data prediction.Figure 10.9 The flow-chart signifies different systems, approaches and investiga...
11 Chapter 12Figure 12.1 Total Confirmed Cases across the world on May, 2020. (Source : https...Figure 12.2 Confirmed Covid-19 cases in Tamilnadu—Highly affected cities. (Sourc...Figure 12.3 Proposed model for forecasting covid-19.Figure 12.4 Working of LSTM.Figure 12.5 Working of the gradient boosting algorithm.Figure 12.6 Output of linear regression for linear data.Figure 12.7 Working of polynomial regression with the non-linear data.Figure 12.8 Actual and predicted values of Confirmed cases using polynomial Regr...Figure 12.9 Actual and predicted values of fatalities using polynomial regressio...
12 Chapter 13Figure 13.1 Block diagram.Figure 13.2 Input image.Figure 13.3 Gray image.Figure 13.4 Filtered image.Figure 13.5 SNR comparison.Figure 13.6 Identification of cancer.
13 Chapter 14Figure 14.1 C3-A2 channel of EEG signal for different stages behavior of sleep: ...Figure 14.2 60 s epoch original EEG signal behavior from Healthy Subject.Figure 14.3 Different brain behaviors of healthy subject during different sleep ...Figure 14.4 EEG signal behavior from sleep disorder subject with duration of 60 ...Figure 14.5 Different sleep stage characteristics from subject affected with sle...Figure 14.6 (a) 10–20 Electrode placement system, (b) Notations for placement of...Figure 14.7 Left frontal region recording.Figure 14.8 Left central region recording.Figure 14.9 Right frontal recording.Figure