Note: Intro -- 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.