Computational Intelligence Methods for Bioinformatics and Biostatistics

This book constitutes revised selected papers from the 16 th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019, which was held in Bergamo, Italy, during September 4-6, 2019.

The 28 full papers presented in this volume were carefully reviewed and selected from 55 submissions. The papers are grouped in topical sections as follows: Computational Intelligence Methods for Bioinformatics and Biostatistics; Algebraic and Computational Methods for the Study of RNA Behaviour; Intelligence methods for molecular characterization medicine; Machine Learning in Healthcare Informatics and Medical Biology; Modeling and Simulation Methods for Computational Biology and Systems Medicine.

Klappentext

This book constitutes revised selected papers from the 16th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019, which was held in Bergamo, Italy, during September 4-6, 2019. The 28 full papers presented in this volume were carefully reviewed and selected from 55 submissions. The papers are grouped in topical sections as follows: Computational Intelligence Methods for Bioinformatics and Biostatistics; Algebraic and Computational Methods for the Study of RNA Behaviour; Intelligence methods for molecular characterization medicine; Machine Learning in Healthcare Informatics and Medical Biology; Modeling and Simulation Methods for Computational Biology and Systems Medicine.

Inhalt

Computational Intelligence Methods for Bioinformatics and Biostatistics .- A Smartphone-Based Clinical Decision Support System for Tremor Assessment.- cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models.- Effective use of evolutionary computation to parameterise an epidemiological model.- Extending knowledge on genomic data and metadata of cancer by exploiting taxonomy-based relaxed queries on domain-specific ontologies.- GAN-Based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis.- Improving the Fusion of Outbreak Detection Methods with Supervised Learning.- Learning cancer drug sensitivities in large-scale screens from multi-omics data with local low-rank structure.- Mass Spectra Interpretation and the Interest of SpecFit for Identifying Uncommon Modifications.- MSAX: Multivariate symbolic aggregate approximation for time series classification.- NeoHiC: a Web Application forthe Analysis of Hi-C Data 100 Random sample consensus for the robust identification of outliers in cancer data.- Solving Equations on Discrete Dynamical Systems.- SW+: On Accelerating Smith-Waterman Execution of GATK HaplotypeCaller.- Algebraic and Computational Methods for the Study of RNA Behaviour .- Algebraic Characterisation of Non-coding RNA 141 Bi-Alignments as Models of Incongruent Evolution of RNA Sequence and Secondary Structure.- Label Core for Understanding RNA Structures.- Modification of Valiant's Parsing Algorithm for the String-Searching Problem.- On Secondary Structure Analysis by Using Formal Grammars and Artificial Neural Networks.- Intelligence methods for molecular characterization and dynamics in translational medicine .- Integration of single-cell RNA-sequencing data into flux balance cellular automata.- Machine Learning in Healthcare Informatics and Medical Biology .- Characterizing bipolar disorder-associated single nucleotide polymorphisms ina large UK cohort using Association Rules.- Evaluating deep semi-supervised learning for whole-transcriptome breast cancer subtyping.- Learning Weighted Association Rules in Human Phenotype Ontology.- Network modeling and analysis of normal and cancer gene expression data.- Regularization techniques in Radiomics: A case study on the prediction of pCR in Breast Tumours and the Axilla.- Modeling and Simulation Methods for Computational Biology and Systems Medicine .- In Silico evaluation of daclizumab and vitamin D effects in Multiple Sclerosis using Agent Based Models.- Multiple Sclerosis disease: a computational approach for investigating its drug interactions.- Observability of bacterial growth models in bubble column bioreactors.- On the simulation and automatic parametrization of metabolic networks through Electronic Design Automation.

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