Computational Intelligence Methods for Bioinformatics and Biostatistics

Chemical Neural Networks and Synthetic Cell Biotechnology: Preludes to Chemical AI.- Development of Bayesian network for multiple sclerosis risk factor interaction analysis.- Real-Time Automatic Plankton Detection, Tracking and Classification on Raw Hologram.- The first in-silico model of leg movement activity during sleep.- Transfer learning and magnetic resonance imaging techniques for deep neural network-based diagnosis of early cognitive decline and dementia.- Improving bacterial sRNA identification by combining genomic context and sequence-derived features.- High-dimensional multi-trait GWAS by reverse prediction of genotypes using machine learning methods.- A Non-Negative Matrix Tri-Factorization based Method for Predicting Antitumor Drug Sensitivity.- A Rule-based Approach for Generating Synthetic Biological Pathways.- 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.- Text Mining Enhancements for Image Recognition of Gene Names and Gene Relations.- Sentence Classification to Detect Tables for Helping Extraction of Regulatory Interactions in Bacteria.- RF-Isolation: a Novel Representation of Structural Connectivity Networks for Multiple Sclerosis Classification.- Summarizing Global SARS-CoV-2 Geographical Spread by Phylogenetic Multitype Branching Models.- Explainable AI Models for COVID-19 Diagnosis using CT-Scan Images and Clinical Data.- The need of standardised metadata to encode causal relationships: Towards safer data-driven machine learning biological solutions.- Deep Recurrent Neural Networks for the Generation of Synthetic Coronavirus Spike Protein Sequences.- Recent Dimensionality Reduction Techniques for High-Dimensional COVID-19 Data.- Soft brain ageing indicators based on light-weight LeNet-like neural networks and localized 2D brain age biomarkers.