Computational learning approaches to data analytics in biomedical applications
Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and...
Uloženo v:
| Hlavní autoři: | , , , |
|---|---|
| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam
Academic Press
2020
Elsevier Science & Technology |
| Vydání: | 1 |
| Témata: | |
| ISBN: | 9780128144824, 0128144823 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
Obsah:
- 9.2.1.2 Reading data as formatted tables -- 9.2.1.3 Reading data as cellular arrays -- 9.2.1.4 Reading data as numerical arrays and matrices -- 9.2.1.4.1 xlsread function -- 9.2.1.4.2 Functions for reading in data from text files as numerical arrays/matrices -- 9.2.1.4.3 Reading images in MATLAB -- 9.2.2 Reading data in Python -- 9.2.2.1 Overview of external libraries and modules for Python -- 9.2.2.2 Opening files in Python -- 9.2.2.2.1 Reading text files in Python -- 9.2.2.2.2 read_csv() function -- 9.2.2.2.3 Other read functions -- 9.2.3 Handling big data in MATLAB -- 9.2.3.1 How to create data stores in MATLAB -- 9.2.3.1.1 read function -- 9.2.3.1.2 readall function -- 9.2.3.1.3 hasdata function -- 9.2.3.1.4 partition function -- 9.2.3.1.5 numpartitions function -- 9.2.3.2 Tall arrays -- 9.3 Data preprocessing -- 9.3.1 Missing values handling -- 9.3.1.1 Handling missing values during reading -- 9.3.1.2 Finding and replacing missing values -- 9.3.2 Normalization -- 9.3.2.1 z-score -- 9.3.3 Outliers detection -- 9.4 Tools and functions for implementing machine learning algorithms -- 9.4.1 Clustering -- 9.4.1.1 k-means -- 9.4.1.2 Gaussian mixture model -- 9.4.1.3 Hierarchical clustering -- 9.4.1.4 Self-organizing map -- 9.4.2 Prediction and classification -- 9.4.2.1 Machine learning workflow -- 9.4.2.1.1 Data Preparation -- 9.4.2.1.2 Fitting and predicting tools -- 9.4.2.2 Multiclass support vector machines -- 9.4.2.3 Neural network classifier -- 9.4.2.4 Performance evaluation and cross-validation tools -- 9.4.3 Features reduction and features selection tools in MATLAB -- 9.4.3.1 Built-in feature selection method -- 9.4.3.2 Sequential features selection -- 9.4.4 Features reduction and features selection tools in Python -- 9.4.4.1 Removing features with low variance -- 9.4.4.2 Recursive feature elimination -- 9.5 Visualization
- Front Cover -- Computational Learning Approaches to Data Analytics in Biomedical Applications -- Computational Learning Approaches to Data Analytics in Biomedical Applications -- Copyright -- Contents -- Preface and Acknowledgements -- 1 - Introduction -- References -- 2 - Data preprocessing -- 2.1 Introduction -- 2.2 Data preparation -- 2.2.1 Initial cleansing -- 2.2.2 Data imputation and missing values algorithms -- 2.2.2.1 Removal Methods -- 2.2.2.2 Utilization methods -- 2.2.2.3 Maximum likelihood -- 2.2.3 Imputation methods -- 2.2.3.1 Single imputation methods -- 2.2.3.1.1 Mean imputation -- 2.2.3.1.2 Substitution of related observations -- 2.2.3.1.3 Random selection -- 2.2.3.1.4 Weighted K-nearest neighbors (KNN) imputation -- 2.2.3.2 Multiple imputation -- 2.2.4 Feature enumeration -- 2.2.4.1 Special cases of categorical data representation using COBRIT traumatic brain injury data as an example -- 2.2.5 Detecting and removing redundant features -- 2.2.5.1 Pearson correlation -- 2.2.5.2 Spearman correlation -- 2.2.6 Recoding categorical features -- 2.2.7 Outlier detection -- 2.2.8 Normalization -- 2.2.9 Domain experts -- 2.2.10 Feature selection and extraction -- 2.3 Example -- 2.4 Summary -- References -- 3 - Clustering algorithms -- 3.1 Introduction -- 3.2 Proximity measures -- 3.3 Clustering algorithms -- 3.3.1 Hierarchical clustering -- 3.3.2 Density-based clustering -- 3.3.3 Subspace clustering -- 3.3.3.1 Basic subspace clustering -- 3.3.3.1.1 Grid-based subspace clustering -- 3.3.3.1.2 Window-based subspace clustering -- 3.3.3.1.3 Density-based subspace clustering -- 3.3.3.2 Advanced subspace clustering -- 3.3.3.2.1 3D subspace clustering -- 3.3.4 Squared error-based clustering -- 3.3.5 Fuzzy clustering -- 3.3.6 Evolutionary computational technology-based clustering -- 3.3.7 Neural network-based clustering
- 9.5.1 Multidimensional scaling -- 9.5.1.1 Pairwise distance calculation function pdist -- 9.5.1.2 Perform multidimensional scaling -- 9.5.2 Principal component analysis -- 9.5.3 Visualization functions -- 9.6 Clusters and classification evaluation functions -- 9.6.1 Cluster evaluation -- 9.6.2 Classification models evaluation -- 9.6.2.1 Confusion matrix confusionmat -- 9.7 Summary -- References -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- X -- Back Cover
- 3.3.8 Kernel learning-based clustering -- 3.3.9 Large-scale data clustering -- 3.3.10 High-dimensional data clustering -- 3.3.11 Sequential data clustering -- 3.3.11.1 Proximity-based sequence clustering -- 3.3.11.2 Feature-based sequence clustering -- 3.3.11.3 Model-based sequence clustering -- 3.4 Adaptive resonance theory -- 3.4.1 Fuzzy ART -- 3.4.2 Fuzzy ARTMAP -- 3.4.3 BARTMAP -- 3.5 Summary -- References -- 4 - Selected approaches to supervised learning -- 4.1 Backpropagation and related approaches -- 4.1.1 Backpropagation -- 4.1.2 Backpropagation through time -- 4.2 Recurrent neural networks -- 4.3 Long short-term memory -- 4.4 Convolutional neural networks and deep learning -- 4.4.1 Structure of convolutional neural network -- 4.4.2 Deep belief networks -- 4.4.3 Variational autoencoders -- 4.5 Random forest, classification and Regression Tree, and related approaches -- 4.6 Summary -- References -- 5 - Statistical analysis tools -- 5.1 Introduction -- 5.2 Tools for determining an appropriate analysis -- 5.3 Statistical applications in cluster analysis -- 5.3.1 Cluster evaluation tools: analyzing individual features -- 5.3.1.1 Hypothesis testing and the 2-sample t-test -- 5.3.1.2 Summary of hypothesis testing steps and application to clustering -- 5.3.1.3 One-way ANOVA -- 5.3.1.4 χ2 test for independence -- 5.3.2 Cluster evaluation tools: multivariate analysis of features -- 5.4 Software tools and examples -- 5.4.1 Statistical software tools -- 5.4.1.1 Example: clustering autism spectrum disorder phenotypes -- 5.4.1.2 Correlation analysis -- 5.4.1.3 Cluster evaluation of individual features -- 5.4.1.4 Summary of results -- 5.5 Summary -- References -- 6 - Genomic data analysis -- 6.1 Introduction -- 6.2 DNA methylation -- 6.2.1 Introduction -- 6.2.2 DNA methylation technology -- 6.2.3 DNA methylation analysis
- 6.2.4 Clustering applications for DNA methylation data -- 6.3 SNP analysis -- 6.3.1 Association studies -- 6.3.2 Clustering with family-based association test (FBAT) analysis -- 6.3.2.1 Quality control filtering -- 6.3.2.2 Family-based association testing -- 6.3.2.3 Multiple testing -- 6.3.2.4 Adjustments for small sample size -- 6.3.2.5 Implementation and analysis of results -- 6.4 Biclustering for gene expression data analysis -- 6.4.1 Introduction to biclustering -- 6.4.2 Commonly used biclustering methods -- 6.4.3 Evolutionary-based biclustering methods -- 6.4.4 BARTMAP: a neural network-based biclustering algorithm -- 6.4.5 External and internal validation metrics related to biclustering -- 6.5 Summary -- References -- 7 - Evaluation of cluster validation metrics -- 7.1 Introduction -- 7.2 Related works -- 7.3 Background -- 7.3.1 Commonly used internal validation indices -- 7.3.2 External validation indices -- 7.3.3 Statistical methods -- 7.4 Evaluation framework -- 7.5 Experimental results and analysis -- 7.6 Ensemble validation paradigm -- 7.7 Summary -- References -- 8 - Data visualization -- 8.1 Introduction -- 8.2 Dimensionality reduction methods -- 8.2.1 Linear projection algorithms -- 8.2.1.1 Principal component analysis -- 8.2.1.2 Independent component analysis -- 8.2.2 Nonlinear projection algorithms -- 8.2.2.1 Isomap -- 8.2.2.2 T-Distributed Stochastic Neighbor Embedding (t-SNE) -- 8.2.2.3 LargeVis -- 8.2.2.4 Self-organizing maps -- 8.2.2.5 Visualization of commonly used biomedical data sets from the UCI machine learning repository () -- 8.3 Topological data analysis -- 8.4 Visualization for neural network architectures -- 8.5 Summary -- References -- 9 - Data analysis and machine learning tools in MATLAB and Python -- 9.1 Introduction -- 9.2 Importing data -- 9.2.1 Reading data in MATLAB -- 9.2.1.1 Interactive import function