Advances in Artificial Intelligence, Computation, and Data Science: For Medicine and Life Science

Preface
Contents
Part I Bioinformatics
1 Intelligent Learning and Verification of Biological Networks
	1.1 Introduction
	1.2 Statistical Learning of Regulatory Networks
		1.2.1 INSPECT Change-Points Identification
		1.2.2 Network Structure Learning and Searching
		1.2.3 Regulatory Relationship Identification
	1.3 Formal Analysis of Regulatory Networks
		1.3.1 Temporal Logic Formula
		1.3.2 Symbolic Model Checking
		1.3.3 Time-Bounded Linear Temporal Logic (BLTL)
		1.3.4 Probabilistic Model Checker PRISM
	1.4 Integrative Data Analysis
	1.5 Discussions
	References
2 Differential Expression Analysis of RNA-Seq Data and Co-expression Networks
	2.1 Systems Biology
	2.2 High Throughput Sequencing
	2.3 RNA-seq Analysis
	2.4 Formulating a Sequencing Library
	2.5 Biological and Technical Variations
	2.6 Assessment of Variations
		2.6.1 Poisson’s Distribution
		2.6.2 Negative Binomial Distribution
	2.7 Method for Differential Expression Analysis
	2.8 Generalized Linear Model (GLM)
	2.9 Hypothesis Test
	2.10 Normalization of Data
	2.11 Trimmed Mean of M-values (TMM)
	2.12 Relative Log Expression (RLE)
	2.13 Upper-Quartile Normalization
	2.14 Principal Component Analysis
		2.14.1 Steps of PCA Analysis
	2.15 Data Analysis of Gene Expression Profiles
	2.16 An Illustration: A Differential Gene Expression Analysis Conducted on a Real Dataset
	2.17 R Packages Used in the RNA-Seq Analysis
	2.18 Removal of Lowly Transcribed Genes
	2.19 Formation of DGEList Object Using EdgeR
	2.20 Density Distributions
	2.21 Normalization
	2.22 Principal Component Analysis
	2.23 Design Matrix
	2.24 NB and QL Dispersion Evaluation
	2.25 Annotating Genes
	2.26 Gene Testing
	2.27 GO Analysis
	2.28 ROAST Analysis
	2.29 CAMERA Test
	2.30 Visualizing Gene Tests
	2.31 Graph Theory Terminologies
	2.32 Gene Regulatory Network (GRN)
	2.33 Inference of Gene Regulatory Networks
	2.34 Gene Regulatory Network Modelling
	2.35 Correlation and Partial Correlation-based Methods
	2.36 Co-expression Networks
	2.37 Pre-processing of Data
	2.38 Construction of Covariance Matrix
	2.39 Measure of Similarity
	2.40 Network Construction
	2.41 Module Detection
	2.42 Module Enrichment
	2.43 WGCNA Package in R
	2.44 Co-expression Network Analysis with Real Dataset
	2.45 Concluding Remarks
	References
3 Learning Biomedical Networks: Toward Data-Informed Clinical Decision and Therapy
	3.1 Biological Data and the Rise of Targeted Therapies
	3.2 Network Analysis in Biomedical Informatics
		3.2.1 Differential Network Analysis
		3.2.2 Network-Based Regularization
		3.2.3 Causal Discovery and Inference
	3.3 Software and Biomedical Applications
	3.4 Conclusions and Future Work
	References
4 Simultaneous Clustering of Multiple Gene Expression Datasets for Pattern Discovery
	4.1 Simultaneous Clustering Methods
		4.1.1 Cluster of Clusters (COCA)
		4.1.2 Bi-CoPaM
		4.1.3 UNCLES and M–N Scatter Plots
		4.1.4 Clust
		4.1.5 Deep Learning Approaches
	4.2 Case Study 1: A Novel Subset of Genes with Expression Consistently Oppositely Correlated with Ribosome Biogenesis in Forty Yeast Datasets
		4.2.1 Data and Approach
		4.2.2 Results and Discussion
		4.2.3 Summary and Conclusions
	4.3 Case Study 2: A Transcriptomic Signature Derived from a Study of Sixteen Breast Cancer Cell-Line Datasets Predicts Poor Prognosis
		4.3.1 Data and Approach
		4.3.2 Results and Discussion
		4.3.3 Summary and Conclusions
	4.4 Case Study 3: Cross-Species Application of Clust Reveals Clusters with Contrasting Profiles Under Thermal Stress in Two Rotifer Animal Species
	4.5 Summary and Conclusions
	References
5 Artificial Intelligence for Drug Development
	5.1 Introduction
	5.2 Methodologies in Pre-clinical and Clinical Trials
	5.3 Post-Market Trials
	5.4 Concluding Remarks
	References
6 Mathematical Bases for 2D Insect Trap Counts Modelling
	6.1 Introduction
	6.2 Mean Field and Mechanistic Models of Insect Movement with Trapping
		6.2.1 Isotropic Diffusion Model and Computing Trap Counts
		6.2.2 Individual Based Modelling Using Random Walks
		6.2.3 Simple Random Walk (SRW)
		6.2.4 Simulating Trapping
		6.2.5 Equivalent Trap Counts
	6.3 Geometrical Considerations for Trap Counts Modelling
		6.3.1 Simulation Artefacts Due to the RW Jump Process
		6.3.2 Impact of the Arena Boundary Shape, Size and the Average Release Distance
		6.3.3 Impact of Trap Shape
	6.4 Anisotropic Models of Insect Movement
		6.4.1 Correlated Random Walk (CRW)
		6.4.2 MSD Formula for the CRW
		6.4.3 Measuring Tortuosity
		6.4.4 Biased Random Walk (BRW)
		6.4.5 MSD Formula for the BRW
		6.4.6 Equivalent RWs in Terms of Diffusion
		6.4.7 Drift Diffusion Equation
		6.4.8 Biased and Correlated Random Walk (BCRW)
	6.5 Effect of Movement on Trap Counts
		6.5.1 Effect of Movement Diffusion
		6.5.2 Baited Trapping
	6.6 Concluding Remarks
	References
Part II Medical Image Analysis
7 Artificial Intelligence in Dermatology: A Case Study for Facial Skin Diseases
	7.1 Introduction
	7.2 State of the Art
	7.3 Study Case
		7.3.1 Considered Skin Diseases
		7.3.2 Machine-Learning/Deep-Learning Approaches
		7.3.3 Preliminary Results
	7.4 Developed Software
		7.4.1 Patient Actions
		7.4.2 Doctor Actions
	7.5 Conclusion
	References
8 Medical Imaging Based Diagnosis Through Machine Learning and Data Analysis
	8.1 Introduction
	8.2 Classification
		8.2.1 Classifiers
		8.2.2 Example 1: Similarity Metric
		8.2.3 Example 2: Similarity Learning
	8.3 Dense Prediction
		8.3.1 Segmentation
		8.3.2 Synthesis
	8.4 Multi-modality Analysis
		8.4.1 Example: A Non-deep-Learning Based Approach for Multi-modal Feature Selection
		8.4.2 Example: A Deep Learning Based Approach for Multi-modality Fusion
	8.5 Conclusion
	References
9 EfficientNet-Based Convolutional Neural Networks for Tuberculosis Classification
	9.1 Introduction
	9.2 Related Work
	9.3 Methodology
	9.4 Description of Dataset
	9.5 Results and Discussions
		9.5.1 Feature Visualization of pretrained models for TB classification
	9.6 Conclusions
	References
10 AI in the Detection and Analysis of Colorectal Lesions Using Colonoscopy
	10.1 Introduction
		10.1.1 Colorectum and Colorectal Cancer
		10.1.2 Colorectal Cancer Stages
		10.1.3 Colonoscopy and Colorectal Polyps
		10.1.4 Application of AI in Colonoscopy
	10.2 Computer-Aided Detection in Colorectal Polyps
		10.2.1 Why Computer-Aided Detection
		10.2.2 Early Computer-Aided Detection Algorithm
		10.2.3 Recent Computer-Aided Detection Algorithms
	10.3 Computer-Aided Classification in Colorectal Polyps
		10.3.1 Why Computer-Aided Classification
		10.3.2 Early Computer-Aided Analysis (CADx)
		10.3.3 Recent Progress of CADx
		10.3.4 Limitations of CADx
	10.4 Conclusion
	References
11 Deep Learning-Driven Models for Endoscopic Image Analysis
	11.1 Introduction
	11.2 Deep Learning Architectures
		11.2.1 Convolutional Neural Networks for Image Classification
		11.2.2 Region-Level CNNs for Lesion Detection
		11.2.3 Fully Convolutional Neural Networks for Segmentation
	11.3 Case Study I: Gastrointestinal Hemorrhage Recognition in WCE Images
		11.3.1 Background of the Application
		11.3.2 Improved Learning Strategy
		11.3.3 Dataset
		11.3.4 Evaluation Metrics
		11.3.5 Experimental Results
	11.4 Case Study II: Colorectal Polyp Recognition in Colonoscopy Images
		11.4.1 Background of the Application
		11.4.2 Improved Learning Strategy
		11.4.3 Dataset
		11.4.4 Evaluation Metrics
		11.4.5 Experimental Results
	11.5 Conclusion and Future Perspectives
	References
Part III Physiology
12 A Dynamic Evaluation Mechanism of Human Upper Limb Muscle Forces
	12.1 Introduction
	12.2 Related Work
	12.3 Materials and Methods
		12.3.1 Data Collection and Preprocessing
		12.3.2 Joint Angle Estimation
		12.3.3 OpenSim Simulation
		12.3.4 Muscle Activation Dynamics
	12.4 Results
	12.5 Discussion
	12.6 Conclusions
	References
13 Resting-State EEG Sex Classification Using Selected Brain Connectivity Representation
	13.1 Introduction
	13.2 Related Work
	13.3 Data and Methods
		13.3.1 Dataset Description
		13.3.2 Preprocessing
		13.3.3 Signal Representation
		13.3.4 Feature Analysis
		13.3.5 Classification
	13.4 Results
		13.4.1 Feature Selection
		13.4.2 Validation Results
		13.4.3 Test Results
	13.5 Conclusions
	References
Part IV Innovation in Medicine and Health
14 Augmented Medicine: Changing Clinical Practice with Artificial Intelligence
	14.1 Introduction
	14.2 Implementation of Augmented Medicine in Clinical Practice: An Overview
		14.2.1 Monitoring with Wearable Technology
		14.2.2 AI for Diagnosis
		14.2.3 Machine Learning for Prediction
	14.3 Conclusions
	References
15 Environmental Assessment Based on Health Information Using Artificial Intelligence
	15.1 Introduction
	15.2 Environmental Parameters and Health
		15.2.1 Air Pollution
		15.2.2 Weather-Related Parameters
		15.2.3 Illumination
		15.2.4 Implications for Health-Related BACS
	15.3 System Concept for Health based Environmental Assessment
		15.3.1 System Components and Their Interactions
		15.3.2 Data Interpretation for Medical Staff
		15.3.3 Feedback Systems for Patients
		15.3.4 Communication Between BACS and EHR
	15.4 Approaches for Environmental Assessment
		15.4.1 Data Organization
		15.4.2 Derived Constraints
		15.4.3 Appropriate Methods for Environmental Risk Estimation
	15.5 Conclusion and Discussion
	References
Index