Edited by Riccardo Rizzo, Claudia Angelini, Andrea Bracciali and David Gilbert.
Volume 19 Supplement 7
12th and 13th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2015/16)
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 and that none of the Supplement Editors were involved in the peer review process for any articles for which they are an author.
Naples, Italy and Stirling, UK10-12 September 2015, 1-3 September 2016
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Citation: BMC Bioinformatics 2018 19(Suppl 7):201
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NetControl4BioMed: a pipeline for biomedical data acquisition and analysis of network controllability
Network controllability focuses on discovering combinations of external interventions that can drive a biological system to a desired configuration. In practice, this approach translates into finding a combine...
Citation: BMC Bioinformatics 2018 19(Suppl 7):185 -
Mendelian randomisation analysis of clustered causal effects of body mass on cardiometabolic biomarkers
Recent advances in data analysis methods based on principles of Mendelian Randomisation, such as Egger regression and the weighted median estimator, add to the researcher’s ability to infer cause-effect links ...
Citation: BMC Bioinformatics 2018 19(Suppl 7):195 -
Modeling the covariates effects on the hazard function by piecewise exponential artificial neural networks: an application to a controlled clinical trial on renal carcinoma
In exploring the time course of a disease to support or generate biological hypotheses, the shape of the hazard function provides relevant information. For long follow-ups the shape of hazard function may be c...
Citation: BMC Bioinformatics 2018 19(Suppl 7):186 -
Effect of the number of removed lymph nodes on prostate cancer recurrence and survival: evidence from an observational study
The aim of this article is to analyze the effect on biochemical recurrence and on overall survival of removing an extensive number of pelvic lymph nodes during prostate cancer surgery. The lack of evidence fro...
Citation: BMC Bioinformatics 2018 19(Suppl 7):200 -
Emerging ensembles of kinetic parameters to characterize observed metabolic phenotypes
Determining the value of kinetic constants for a metabolic system in the exact physiological conditions is an extremely hard task. However, this kind of information is of pivotal relevance to effectively simul...
Citation: BMC Bioinformatics 2018 19(Suppl 7):251 -
Deep learning models for bacteria taxonomic classification of metagenomic data
An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered ...
Citation: BMC Bioinformatics 2018 19(Suppl 7):198 -
INBIA: a boosting methodology for proteomic network inference
The analysis of tissue-specific protein interaction networks and their functional enrichment in pathological and normal tissues provides insights on the etiology of diseases. The Pan-cancer proteomic project, ...
Citation: BMC Bioinformatics 2018 19(Suppl 7):188 -
Comparison of metaheuristics to measure gene effects on phylogenetic supports and topologies
A huge and continuous increase in the number of completely sequenced chloroplast genomes, available for evolutionary and functional studies in plants, has been observed during the past years. Consequently, it ...
Citation: BMC Bioinformatics 2018 19(Suppl 7):218 -
Beyond the one-way ANOVA for ’omics data
With ever increasing accessibility to high throughput technologies, more complex treatment structures can be assessed in a variety of ’omics applications. This adds an extra layer of complexity to the analysis...
Citation: BMC Bioinformatics 2018 19(Suppl 7):199 -
STAble: a novel approach to de novo assembly of RNA-seq data and its application in a metabolic model network based metatranscriptomic workflow
De novo assembly of RNA-seq data allows the study of transcriptome in absence of a reference genome either if data is obtained from a single organism or from a mixed sample as in metatranscriptomics studies. G...
Citation: BMC Bioinformatics 2018 19(Suppl 7):184 -
A study on multi-omic oscillations in Escherichia coli metabolic networks
Two important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They a...
Citation: BMC Bioinformatics 2018 19(Suppl 7):194
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