Computational Intelligence Methods for Bioinformatics and Biostatistics (eBook, PDF)

17th International Meeting, CIBB 2021, Virtual Event, November 15-17, 2021, Revised Selected Papers
Redaktion: Chicco, Davide; Cazzaniga, Paolo; Avesani, Simone; Bernasconi, Anna; Vettoretti, Martina; Longato, Enrico; Tavazzi, Erica; Facchiano, Angelo

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 253
• Erscheinungstermin: 25. November 2022
• Englisch
• ISBN-13: 9783031208379
• Artikelnr.: 66680066

Inhaltsangabe

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.