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Computational Intelligence Methods for Bioinformatics and Biostatistics : 17th International Meeting, CIBB 2021, Virtual Event, November 15-17, 2021, Revised Selected Papers / edited by Davide Chicco, Angelo Facchiano, Erica Tavazzi, Enrico Longato, Martina Vettoretti, Anna Bernasconi, Simone Avesani, Paolo Cazzaniga.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- Lecture notes in computer science. Lecture notes in bioinformatics 2366-6331 ; 13483
- Lecture Notes in Bioinformatics, 2366-6331 ; 13483
- Language:
- English
- Subjects (All):
- Bioinformatics.
- Data mining.
- Signal processing.
- Computer networks.
- Machine learning.
- Social sciences-Data processing.
- Data Mining and Knowledge Discovery.
- Signal, Speech and Image Processing .
- Computer Communication Networks.
- Machine Learning.
- Computer Application in Social and Behavioral Sciences.
- Local Subjects:
- Bioinformatics.
- Data Mining and Knowledge Discovery.
- Signal, Speech and Image Processing .
- Computer Communication Networks.
- Machine Learning.
- Computer Application in Social and Behavioral Sciences.
- Physical Description:
- 1 online resource (XV, 253 pages) : 95 illustrations, 76 illustrations in color.
- Edition:
- 1st ed. 2022.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2022.
- System Details:
- text file PDF
- Summary:
- 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.
- Contents:
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-031-20837-9
- 9783031208379
- Access Restriction:
- Restricted for use by site license.
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