Computational Intelligence Methods for Bioinformatics and Biostatistics

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.