Edited by Paolo Cazzaniga, Maria Raposo, Daniela Besozzi, Ivan Merelli, Antonino Staiano, Angelo Ciaramella, Riccardo Rizzo and Luca Manzoni.
Volume 22 Supplement 2
15th and 16th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB 2018-19)
Research
Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.
Bergamo, Italy4-6 September 2019
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Citation: BMC Bioinformatics 2021 22(Suppl 2):90
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Tremor assessment using smartphone sensor data and fuzzy reasoning
Tremor severity assessment is an important step for the diagnosis and treatment decision-making of essential tremor (ET) patients. Traditionally, tremor severity is assessed by using questionnaires (e.g., ETRS...
Citation: BMC Bioinformatics 2021 22(Suppl 2):57 -
Evaluation of open search methods based on theoretical mass spectra comparison
Mass spectrometry remains the privileged method to characterize proteins. Nevertheless, most of the spectra generated by an experiment remain unidentified after their analysis, mostly because of the modificati...
Citation: BMC Bioinformatics 2021 22(Suppl 2):65 -
Accelerated global sensitivity analysis of genome-wide constraint-based metabolic models
Genome-wide reconstructions of metabolism opened the way to thorough investigations of cell metabolism for health care and industrial purposes. However, the predictions offered by Flux Balance Analysis (FBA) c...
Citation: BMC Bioinformatics 2021 22(Suppl 2):78 -
MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction
Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical ima...
Citation: BMC Bioinformatics 2021 22(Suppl 2):31 -
A new Bayesian piecewise linear regression model for dynamic network reconstruction
Linear regression models are important tools for learning regulatory networks from gene expression time series. A conventional assumption for non-homogeneous regulatory processes on a short time scale is that ...
Citation: BMC Bioinformatics 2021 22(Suppl 2):196 -
Advantages of using graph databases to explore chromatin conformation capture experiments
High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices descri...
Citation: BMC Bioinformatics 2021 22(Suppl 2):43
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