Computational intelligence methods for bioinformatics and biostatistics : 17th International Meeting, CIBB 2021, virtual event, November 15-17, 2021 : revised selected papers

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Preface Organization Contents Chemical Neural Networks and Synthetic Cell Biotechnology: Preludes to Chemical AI 1 Can ``Synthetic Cell'' Biotechnology Become a Useful Platform for Chemical AI? 2 Scientific Background - What Exactly are SCs? 2.1 Computer Gestalt ch1varelabook vs. Autopoiesis & Autonomy 3 Bio-Chemical Neural Network 3.1 Selected Examples of Potentially Interesting CNNs for SCs 4 Concepts and Experimental Perspectives on Chemical Neural Networks and Synthetic Cells 4.1 Machine Learning 4.2 Meaning 4.3 Embodiment References Development of Bayesian Network for Multiple Sclerosis Risk Factor Interaction Analysis 1 Introduction 2 Previous Work 2.1 Artificial Intelligence (AI) and Machine Learning (ML) in MS Research 2.2 Alignment with Epidemiology 3 BN Development 3.1 Relevant Risk Factors 3.2 Structure 3.3 Measurements 4 Results and Discussion 4.1 Interaction, Sufficiency, Necessity 4.2 Equivalent Odds Ratios 5 Conclusions References Real-Time Automatic Plankton Detection, Tracking and Classification on Raw Hologram 1 Introduction 2 Materials and Methods 2.1 Hologram Formation 2.2 Dataset 2.3 Object Detection Models and Tracking 2.4 Metrics 3 Results 3.1 Detection Performances 3.2 Tracking Performances 4 Conclusion and Perspectives References The First in-silico Model of Leg Movement Activity During Sleep 1 Scientific Background 2 Materials and Methods 2.1 The LMA Model 2.2 Model Calibration 3 Results and Discussion 4 Conclusion References Transfer Learning and Magnetic Resonance Imaging Techniques for the Deep Neural Network-Based Diagnosis of Early Cognitive Decline and Dementia 1 Introduction 2 Deep Learning for Medical Diagnosis 2.1 Convolutional Neural Network for Image Classification 2.2 Pretrained Convolutional Neural Network 3 Imaging Data Repositories 4 Proposed Transfer Learning Pipeline 5 Experiments and Results 6 Discussion and Conclusions References Improving Bacterial sRNA Identification By Combining Genomic Context and Sequence-Derived Features 1 Background 2 Materials and Methods 2.1 Data 2.2 Feature Sets 2.3 Model Generation 2.4 Comparative Assessment 3 Results and Discussion 3.1 Model Selection 3.2 Variable Importance Analysis 3.3 Comparative Assessment 4 Conclusion References High-Dimensional Multi-trait GWAS By Reverse Prediction of Genotypes Using Machine Learning Methods 1 Background 2 Methods 2.1 Reverse Genotype and Trans-eQTL Prediction 2.2 Datasets 2.3 Experimental Settings 2.4 Code and Supplementary Information 3 Results 3.1 Reverse Genotype Prediction and Trans-EQTL Analysis in Simulated Data 3.2 Reverse Genotype Prediction and Trans-EQTL Analysis in Yeast 4 Discussion References A Non-Negative Matrix Tri-Factorization Based Method for Predicting Antitumor Drug Sensitivity 1 Background 2 Material and Methods 2.1 Datasets 2.2 Model 2.3 Method 2.4 Prediction of Novel Associations 2.5 Prediction of the Whole Drug Profile for a New Cell Line 3 Results 3.1 Prediction of Novel Associations 3.2 Prediction of the Whole Drug Profile for a New Cell Line 4 Discussion and Concluding Remarks References A Rule-Based Approach for Generating Synthetic Biological Pathways 1 Scientific Background 1.1 Introduction 1.2 Related Work 2 Materials and Methods 2.1 Synthetic Data Generation 2.2 Implementation Details 3 Experimental Setup 3.1 Model 3.2 Data 4 Results 4.1 Synthetic Data for Mixed-Batches 4.2 When to Use Synthetic Data 4.3 Generalizing to New Tasks 4.4 Computational Time 5 Conclusion References Machine Learning Classifiers Based on Dimensionality Reduction Techniques for the Early Diagnosis of Alzheimer’s Disease Using Magnetic Resonance Imaging and Positron Emission Tomography Brain Data 1 Scientific Background 2 Methods 2.1 Dataset Description 2.2 Image Preprocessing 2.3 Feature Extraction 2.4 Dimensionality Reduction Techniques 2.5 Machine Learning Classifiers 2.6 Description of resampling Method and Performance Metrics 3 Result and Discussion 4 Conclusion References Text Mining Enhancements for Image Recognition of Gene Names and Gene Relations 1 Introduction 2 Related Work 3 Methods 3.1 Dataset 3.2 OCR Tool 3.3 Gene Name Enhancements 3.4 Gene Relation Enhancements 4 Results 4.1 Gene Name Enhancement Results 4.2 Gene Relation Enhancement Results 4.3 Use Cases 5 Discussion 6 Conclusion References Sentence Classification to Detect Tables for Helping Extraction of Regulatory Interactions in Bacteria 1 Introduction 2 Materials and Methods 2.1 Data Set 2.2 Feature Extraction and Vectorization 2.3 Supervised Learning 3 Results 3.1 Best Model 3.2 Best Features 4 Conclusion References RF-Isolation: A Novel Representation of Structural Connectivity Networks for Multiple Sclerosis Classification 1 Introduction 2 Materials and Methods 2.1 Study Population 2.2 MRI Acquisition and Processing 2.3 RF-Isolation Extraction 2.4 Classification Analysis 3 Results 3.1 Analysis of the MS-ProxIF Model 3.2 Comparison to Standard Network Measures 4 Conclusion References Summarizing Global SARS-CoV-2 Geographical Spread by Phylogenetic Multitype Branching Models 1 Introduction 2 Data and Methods 3 Results and Discussion 4 Conclusions References Explainable AI Models for COVID-19 Diagnosis Using CT-Scan Images and Clinical Data 1 Scientific Background 2 Materials and Methods 2.1 Datasets Description and Preprocessing 2.2 Models Design 2.3 Explainability and Interpretability 3 Results 3.1 Deep CNN for Image-Data Experimentation Results 3.2 Classifiers for Bio-Data Experimentation Results 3.3 Comparison Study 3.4 Explainability/Interpretability Results 4 Conclusion References The Need of Standardised Metadata to Encode Causal Relationships: Towards Safer Data-Driven Machine Learning Biological Solutions 1 Introduction 2 Considerations for the Development and Reporting of ML Solutions 2.1 The Desirable Properties of ML Models 2.2 Current Limitations in Biomedical ML Solutions 2.3 Origin and Error Types 2.4 Limitations of the Current Evaluation System 2.5 Helping Methodological Tools 3 Relevance of Induced Bias in Biological Studies for ML Analysis 4 An Approach to Overcome the Limitations: Accompanying Metadata with Causal Information 4.1 Incorporating Causal Information 5 Conclusion References Deep Recurrent Neural Networks for the Generation of Synthetic Coronavirus Spike Protein Sequences 1 Introduction 1.1 Coronaviridae 1.2 Recurrent Neural Networks 2 Methods 2.1 Recurrent Neural Network (RNN) Architecture 2.2 Coronavirus Training Set 3 Results 3.1 Characteristics of DL Simulated Spike Proteins 4 Conclusions References Recent Dimensionality Reduction Techniques for High-Dimensional COVID-19 Data 1 Introduction 2 State-of-the-Art Dimensionality Reduction Techniques 3 Experimental Analysis 3.1 Dataset Description and Preprocessing 3.2 Results 3.3 Discussion 4 Conclusions References Soft Brain Ageing Indicators Based on Light-Weight LeNet-Like Neural Networks and Localized 2D Brain Age Biomarkers 1 Introduction 2 Methods 2.1 Data Extraction, Preprocessing and Labeling 2.2 2D-CNN Models for Brain Age Classification and Regression 2.3 2D Brain-Age Biomarkers Model Explanation 3 Results 3.1 Classification Results 3.2 Linear Regression Results 4 Discussion 5 Architectural, Qualitative and Performance Comparisons 6 Conclusion References Author Index This book constitutes revised selected papers from the 17th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2021, which was held virtually during November 15–17, 2021. The 19 papers included in these proceedings were carefully reviewed and selected from 26 submissions, and they focus on bioinformatics, computational biology, health informatics, cheminformatics, biotechnology, biostatistics, and biomedical imaging.