Note: Title -- Preface -- Special Guest Message for the 150th Anniversary of Italian Unification -- Organization -- Table of Contents -- Invited Lectures -- Modelling the Effect of Genes on the Dynamics of Probabilistic Spiking Neural Networks for Computational Neurogenetic Modelling -- Introduction -- Probabilistic Neural Models -- Modelling the Effect of Gene Dynamics on the Spiking Dynamics of a pSNN for a pCNGM -- Conclusion and Further Research -- References -- Biostatistics Meets Bioinformatics in Integrating Information from Highdimensional Heterogeneous Genomic Data: Two Examples from Rare Genetic Diseases and Infectious Diseases -- Introduction -- Statistics and Bioinformatics in Gene Therapy Frameworks -- Statistics and Bioinformatics in High Incidence Infectious Diseases: An Application to Mycobacterium Tubercolosis -- Methods -- sRNA Candidates Definition -- Candidates sRNA Encoding Region -- Final Comments -- References -- Statistical Learning -- Bayesian Models for the Multi-sample Time-Course Microarray Experiments -- Introduction -- Statistical Modeling, Estimation and Classification of Gene Expression Profiles -- The Data Structure -- Modeling the Gene Expression Profiles -- Modeling the Errors -- Estimation of Gene-Dependent Parameters -- Identification and Classification of Genes -- Evaluation of Class Probabilities -- Identification and Classification of Differentially Expressed Genes -- Estimation of Gene Expression Profiles -- Estimation of Global Parameters and Prior Hyperparameters -- Algorithm -- Simulations Results and Discussion -- References -- A Machine Learning Pipeline for Discriminant Pathways Identification -- Introduction -- Methods -- The Pipeline -- Experimental Setup for the Examples -- Data Description -- Results -- Air Pollution Experiment -- Parkinson Disease Experiment -- Conclusions -- References. , Discovering Hidden Pathways in Bioinformatics -- Introduction -- Materials and Methods -- Data -- Existing Methods -- Proposed Method -- Experimental Results -- Discussion and Conclusions -- References -- Genomics -- Reliability of miRNA Microarray Platforms: An Approach Based on Random Effects Linear Models -- Introduction -- Materials and Methods -- Results -- Experimental Data Description -- Model Estimation -- Validation -- Discussion -- Conclusions -- References -- A Bioinformatics Procedure to Identify and Annotate Somatic Mutations in Whole-Exome Sequencing Data -- Introduction -- Materials and Methods -- Results -- Discussion and Conclusion -- References -- Computational Intelligence for Health at the Edge -- Feature Selection for the Prediction and Visualization of Brain Tumor Types Using Proton Magnetic Resonance Spectroscopy Data -- Introduction -- Literature Review -- Class-Separability Feature Selection -- A Criterion for Class-Separability -- Experimental Work -- Datasets -- Experimental Settings -- Discussion of the Results -- Data Visualization -- Metabolic Interpretation -- The Effect of Redundancy in Class Separability -- Conclusions -- References -- On the Use of Graphical Models to Study ICU Outcome Prediction in Septic Patients Treated with Statins -- Introduction -- Materials -- Methods -- Statistic Algebraic Models -- Models of Conditional Independence -- Bayesian Networks -- Results -- Marginal Dependence between the Pre-admission Use of Statins and the ICU Outcome -- Study of the Protective Effect of Pre-admission Use of Statins with Bayesian Networks -- Conclusions -- References -- Integration of Biomolecular Interaction Data in a Genomic and Proteomic Data Warehouse to Support Biomedical Knowledge Discovery -- Introduction -- Related Work -- Genomic and Proteomic Data Warehouse (GPDW) -- Data Import Procedures of GPDW. , Data Integration Procedures of GPDW -- Generalization of Metadata -- GPDW Data Schema and Queries -- Quality Controls of Integrated Data -- Integrated Biomolecular Interaction Data -- Conceptual and Logical Analysis -- XML Design of Molecular Interaction Data -- Integration Results and Analysis -- Conclusions -- References -- Proteomics -- Machine-Learning Methods to Predict Protein Interaction Sites in Folded Proteins -- Introduction -- Dataset -- Definition of Protein Surface, Interaction Contacts and Patches -- ISPRED2 Implementation -- Measures of Accuracy -- ISPRED2 at Work -- The Effect of the Definition of Interaction Patches -- Comparison with Other Method -- Conclusions -- References -- Complementing Kernel-Based Visualization of Protein Sequences with Their Phylogenetic Tree -- Introduction -- Proteins and Pharmacology -- Materials and Methods -- Kernel Generative Topographic Mapping -- The GPCR Data -- Phylogenetic Trees -- Results and Discussion -- Conclusions -- References -- DEEN: A Simple and Fast Algorithm for Network Community Detection -- Introduction -- The Algorithm -- DEEN: Delete Edges and Expand Nodes -- Delete Edges. -- Expand Nodes. -- Time Complexity. -- Related Work -- Experimental Evaluation -- Benchmark Networks -- The Karate Club Network. -- The US College Football Network. -- Protein Complex Detection in the Budding Yeast PPI Network -- Assignment of Annotation and p-values to Clusters. -- Results. -- Comparison with MCL. -- Conclusion -- References -- Intelligent Clinical Decision Support Systems(i-CDSS) -- Self-similarity in Physiological Time Series:New Perspectives from the Temporal Spectrum of Scale Exponents -- Introduction -- DFA and the Temporal Spectrum of Scale Exponents -- DFA Temporal Spectrum of Physiological Time Series -- Temporal Spectrum of EEG -- Temporal Spectrum of Cardiovascular Signals. , Discussion and Conclusions -- References -- Support Vector Machines for Survival Regression -- Introduction -- Survival Analysis as Quantile Regression -- Loss Function -- Censored Loss Function -- Theoretical Analysis -- Bounds on the Quantile Risk -- Bounds on the Quantile Property -- Optimisation of the Risk Functional -- Dual Optimisation -- Monotonicity Constraints -- Experiments -- Simulated Data -- German Breast Cancer Study Group 2 -- Conclusions -- References -- Boosted C5 Trees i-Biomarkers Panel for Invasive Bladder Cancer Progression Prediction -- Introduction -- Methods -- Data Preprocessing -- i-Biomarker Development Using C5 Decision Trees -- Results and Discussions -- Samples Data -- i-Biomarkers Development -- Panel of i-Biomarkers Using the KDD Set: -- Single i-Biomarker Using the KM Set: -- Conclusion -- References -- Bioinformatics -- A Faster Algorithm for Motif Finding in Sequences from ChIP-Seq Data -- Introduction -- The Problem -- The Algorithm -- Experimental Evaluation -- Conclusions -- References -- Case/Control Prediction from Illumina Methylation Microarray's β and Two-Color Channels in the Presence of Batch Effects -- Introduction -- Methods -- Results -- Discussion -- Conclusion -- References -- Supporting the Design, Communication and Management of Bioinformatic Protocols through the Leaf Tool -- Introduction -- Formalizing Bioinformatic Protocols -- Resources and Processors -- Protocols as Annnotated Directed Graphs -- The ``Leaf'' System -- The Leaf Graph Language -- The Leaf Protocol Engine -- A Real Application Example -- Conclusions -- References -- Data Clustering -- Genomic Annotation Prediction Based on Integrated Information -- Introduction. -- Data Warehousing and Information Integration -- Genomic and Proteomic Data Warehouse -- Information and Data Integration Approach -- Computational Methods. , Prediction of Biomolecular Annotations -- SVD - Singular Value Decomposition -- SIM - Semantic IMprovement -- Results -- Software Infrastructure and Performances -- ACML and SVDLIBC -- Performances -- Conclusions -- References -- Solving Biclustering with a GRASP-Like Metaheuristic: Two Case-Studies on Gene Expression Analysis -- Introduction -- Problem Formulation -- GRASP -- A Reactive GRASP-Like Algorithm for Biclustering -- Experimental Results and Biological Significance -- References -- Author Index.

Note: Intro -- Preface -- Organization -- Steering Committee -- Program Committee -- Contents -- Self-organizing Maps: Theoretical Developments -- Look and Feel What and How Recurrent Self-Organizing Maps Learn -- 1 Introduction -- 2 Methods -- 2.1 Algorithm -- 2.2 Representations -- 2.3 Evaluation -- 3 Results -- 3.1 Ambiguous Observations -- 3.2 Long Term Dependencies -- 3.3 Adapting to a Changing Dynamics -- 3.4 Noisy Observations -- 3.5 Perturbed by a Noise State -- 4 Conclusion -- References -- Self-Organizing Mappings on the Flag Manifold -- 1 Introduction -- 2 Introduction to Flag Manifold with Data Analysis Examples -- 3 Numerical Representation and Geodesics -- 3.1 Flag Manifold -- 3.2 Geodesic and Distance Between Two Points on Flag Manifold -- 3.3 Iterative Alternating Algorithm -- 4 SOM on Flag Manifolds -- 4.1 Numerical Experiment -- 5 Conclusions and Future Work -- References -- Self-Organizing Maps with Convolutional Layers -- 1 Introduction -- 2 Self-Organizing Maps -- 3 Related Work -- 4 Convolutional Layers -- 5 SOM with Convolutional Layers -- 6 Quality Measures -- 6.1 Kruskal Shepard Error -- 6.2 Cross Entropy -- 6.3 Minor Class Occurrence -- 6.4 Class Scatter Index -- 7 Experimental Analysis -- 7.1 Experimental Settings -- 7.2 Quality Measure Results -- 7.3 Visualization Results -- 8 Conclusion -- References -- Cellular Self-Organising Maps - CSOM -- 1 Introduction -- 2 Self-Organising Maps: SOM and Cellular SOM -- 2.1 SOM: Self-Organising Maps -- 2.2 CSOM: Cellular Self-Organising Maps -- 2.3 Algorithms -- 3 Experimental Setup and Results -- 3.1 Quantisation of Artificial d-dimensional Distributions -- 3.2 Video Compression -- 4 Conclusion -- References -- A Probabilistic Method for Pruning CADJ Graphs with Applications to SOM Clustering -- 1 Introduction: The CADJ Graph -- 2 A Probabilistic Model for CADJ -- 3 A Multi-focal View. , 4 The Metric -- 5 Ranking Connections for Removal -- 6 Clustering Applications -- 6.1 6d Synthetic Spectral Image -- 6.2 Real Data: Ocean City Spectral Image -- 7 Conclusions and Outlook -- References -- Practical Applications of Self-Organizing Maps, Learning Vector Quantization and Clustering -- SOM-Based Anomaly Detection and Localization for Space Subsystems -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Self-Organizing Map Background -- 4 Methods -- 4.1 Data Processing -- 4.2 Anomaly Detection via MQE -- 4.3 Anomaly Localization via Supervised Feature Extraction -- 5 Experiments and Discussion -- 5.1 Data Collection -- 5.2 Anomaly Detection Analysis -- 5.3 Anomaly Localization Analysis -- 6 Conclusions and Future Work -- References -- Self-Organizing Maps in Earth Observation Data Cubes Analysis -- 1 Introduction -- 2 Land Use and Cover Change Information from Earth Observation Data Cubes -- 2.1 Earth Observation Satellite Image Time Series -- 2.2 Vegetation Indexes -- 2.3 Using SOM to Improve the Quality of Land Use and Cover Samples -- 3 Case Study -- 4 Final Remarks -- References -- Competencies in Higher Education: A Feature Analysis with Self-Organizing Maps -- Abstract -- 1 Introduction -- 2 State of the Art -- 3 Materials and Methods -- 3.1 Training Dataset -- 3.2 Clustering Students and Obtaining Main Features -- 4 Results -- 5 Conclusions and Future Works -- References -- Using SOM-Based Visualization to Analyze the Financial Performance of Consumer Discretionary Firms -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Novelty Detection with Self-Organizing Maps for Autonomous Extraction of Salient Tracking Features -- 1 Introduction -- 2 Image Representation with SOM -- 2.1 Self-Organizing Maps -- 2.2 Image Representation -- 3 Dynamic Neural Fields. , 4 Our Tracking Application -- 5 Results -- 6 Conclusion -- References -- Robust Adaptive SOMs Challenges in a Varied Datasets Analytics -- Abstract -- 1 Introduction -- 2 SOM Algorithm -- 3 RA-SOM Algorithm -- 4 Simulation Results -- 4.1 Balance Dataset -- 4.2 Dermatology Dataset -- 4.3 Arcene Dataset -- 4.4 Gisette Dataset -- 5 Conclusion and Future Work -- References -- Detection of Abnormal Flights Using Fickle Instances in SOM Maps -- 1 Introduction -- 2 The Data -- 3 First Level of Labeling -- 4 Two-Levels Clustering and Resulting Labels -- 5 Dissimilarity Matrix and Relational SOM -- 5.1 Substitutions Costs -- 5.2 Adding Costs and Deletion Costs -- 6 Clustering the Labeled Sequences and Identifying Fickle Flights -- 7 Conclusion -- References -- LVQ-type Classifiers for Condition Monitoring of Induction Motors: A Performance Comparison -- 1 Introduction -- 2 Basics of Cluster Validation Techniques -- 2.1 Cluster Validity Indices -- 3 Prototype-Based Classifiers -- 3.1 LVQ Classifiers -- 4 Results and Discussion -- 5 Conclusions and Further Work -- References -- When Clustering the Multiscalar Fingerprint of the City Reveals Its Segregation Patterns -- 1 Introduction -- 2 Building a Multiscalar Fingerprint of the City -- 3 Focal Distances and Distortion Coefficients -- 4 Clustering Trajectories -- 4.1 Defining Contrasts and Indices of Features Importance -- 4.2 Five hotspots of Segregation for the City of Paris -- 5 Conclusion and Perspectives -- References -- Using Hierarchical Clustering to Understand Behavior of 3D Printer Sensors -- Abstract -- 1 Introduction -- 1.1 3D Printing Overview -- 1.2 Data Collection and Parsing -- 1.3 Data Preprocessing -- 2 Statistical Clustering Method -- 3 Interpretation of Clusters -- 3.1 Analysis of Conditional Distributions Versus Classes -- 3.2 Detection of Non-informative and Redundant Variables. , 3.3 Pattern Conceptualization -- 4 Conclusion -- References -- A Walk Through Spectral Bands: Using Virtual Reality to Better Visualize Hyperspectral Data -- 1 Introduction -- 2 Background -- 2.1 Hyperspectral Data -- 2.2 Virtual Reality for Data Visualization -- 3 Example Visualizations -- 3.1 Indian Pines -- 3.2 Chemical Plume Detection -- 4 Conclusion -- References -- Incremental Traversability Assessment Learning Using Growing Neural Gas Algorithm -- 1 Introduction -- 2 Problem Specification -- 3 Evaluation Results -- 4 Conclusion -- References -- Learning Vector Quantization: Theoretical Developments -- Investigation of Activation Functions for Generalized Learning Vector Quantization -- 1 Introduction -- 2 Generalized Learning Vector Quantization - A Multilayer Network Perspective -- 2.1 Basics of GLVQ -- 2.2 GLVQ - A Neural Network Perspective -- 2.3 Activation Function for MLP and GLVQ-MLN -- 3 Numerical Results for Activation Functions in GLVQ -- 3.1 Data Sets -- 3.2 Results -- 4 Conclusions -- References -- Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks -- 1 Introduction -- 2 Learning Vector Quantization -- 3 Experimental Setup -- 3.1 Adversarial Attacks -- 3.2 Robustness Metrics -- 3.3 Training Setup and Models -- 4 Results -- 5 Conclusion -- References -- Passive Concept Drift Handling via Momentum Based Robust Soft Learning Vector Quantization -- 1 Introduction -- 2 Related Work -- 3 Streaming Data and Concept Drift -- 3.1 Concept Drift -- 4 Robust Soft Learning Vector Quantization -- 4.1 Momentum Based Optimization -- 5 Experiments -- 5.1 Results -- 6 Conclusion -- References -- Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework -- 1 Introduction -- 2 Models and Methods -- 2.1 Learning Vector Quantization -- 2.2 The Dynamics of LVQ. , 2.3 LVQ Dynamics Under Concept Drift -- 3 Results and Discussion -- 4 Summary and Outlook -- References -- Theoretical Developments in Clustering, Deep Learning and Neural Gas -- Soft Subspace Topological Clustering over Evolving Data Stream -- 1 Introduction -- 2 Model Proposition -- 3 Experimental Evaluation -- 4 Conclusion -- References -- Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention -- 1 Introduction -- 2 Reinforcement Learning -- 2.1 The REINFORCE Algorithm -- 3 The RAA3C MODEL -- 3.1 The Location Network -- 3.2 Glimpse Network -- 3.3 Context Network -- 3.4 The Actor-Critic Network -- 3.5 Training -- 4 Learning Domain -- 5 Experiments/Results -- 6 Conclusion -- References -- Approximate Linear Dependence as a Design Method for Kernel Prototype-Based Classifiers -- 1 Introduction -- 2 Basics of Prototype-Based Classification -- 2.1 Kernel Functions -- 3 The Proposed Approach -- 4 Results and Discussion -- 4.1 Initial Tests -- 4.2 More General Tests -- 5 Conclusions and Further Work -- References -- Subspace Quantization on the Grassmannian -- 1 Introduction -- 2 The Grassmannian -- 3 Averaging Subspaces -- 4 Grassmann K-means Algorithm -- 5 The LBG Algorithm on the Grassmannian -- 6 Numerical Experiments -- 6.1 MNIST Results -- 6.2 Indian Pines Results -- 7 Conclusions -- References -- Variants of Fuzzy Neural Gas -- 1 Introduction -- 2 Interpretation of Distance for Different Types of Data -- 3 Possibilistic Fuzzy c-Means -- 4 Possibilistic Fuzzy Neural Gas -- 4.1 Vectorial Data -- 4.2 Relational Data -- 4.3 Median Data -- 4.4 Remarks -- 5 Experiments -- 5.1 Artificial Gaussian Distributions -- 5.2 Clustering Transcripts of Psychotherapy Sessions -- 6 Conclusion -- References -- Autoencoders Covering Space as a Life-Long Classifier -- 1 Introduction -- 2 Related Work -- 3 Analysis -- 4 Method. , 5 Results.