title: A multi-view genomic data simulator.
abstract: BACKGROUND
  OMICs technologies allow to assay the state of a large number of different features (e.g., mRNA expression, miRNA expression, copy number variation, DNA methylation, etc.) from the same samples. The objective of these experiments is usually to find a reduced set of significant features, which can be used to differentiate the conditions assayed. In terms of development of novel feature selection computational methods, this task is challenging for the lack of fully annotated biological datasets to be used for benchmarking. A possible way to tackle this problem is generating appropriate synthetic datasets, whose composition and behaviour are fully controlled and known a priori.
  
  
  RESULTS
  Here we propose a novel method centred on the generation of networks of interactions among different biological molecules, especially involved in regulating gene expression. Synthetic datasets are obtained from ordinary differential equations based models with known parameters. Our results show that the generated datasets are well mimicking the behaviour of real data, for popular data analysis methods are able to selectively identify existing interactions.
  
  
  CONCLUSIONS
  The proposed method can be used in conjunction to real biological datasets in the assessment of data mining techniques. The main strength of this method consists in the full control on the simulated data while retaining coherence with the real biological processes. The R package MVBioDataSim is freely available to the scientific community at http://neuronelab.unisa.it/?p=1722.
authors.affiliation: Department of Medical, Surgical, Neurological, Metabolic and Ageing Sciences, Second University of Napoli, Napoli, Italy. michele.fratello@unina2.it.|Department of Computer Science, Fisciano, Italy. michele.fratello@unina2.it.
authors.forename: Michele
authors.initials: M
authors.lastname: Fratello
authors.affiliation: Department of Computer Science, Fisciano, Italy. aserra@unisa.it.
authors.forename: Angela
authors.initials: A
authors.lastname: Serra
authors.affiliation: Unit of Systems Toxicology and Nanosafety Research Centre, Finnish Institute of Occupational Health, FIOH, Helsinki, Finland. vittorio.fortino@ttl.fi.
authors.forename: Vittorio
authors.initials: V
authors.lastname: Fortino
authors.affiliation: Department of Computer Science, Fisciano, Italy. gianni@unisa.it.
authors.forename: Giancarlo
authors.initials: G
authors.lastname: Raiconi
authors.affiliation: Department of Computer Science, Fisciano, Italy. rtagliaferr@unisa.it.
authors.forename: Roberto
authors.initials: R
authors.lastname: Tagliaferri
authors.affiliation: Unit of Systems Toxicology and Nanosafety Research Centre, Finnish Institute of Occupational Health, FIOH, Helsinki, Finland. dario.greco@ttl.fi.
authors.forename: Dario
authors.initials: D
authors.lastname: Greco
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D056915:DNA Copy Number Variations; D019175:DNA Methylation; D066264:Datasets as Topic; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D023281:Genomics / Q000379:methods*; D006801:Humans; D035683:MicroRNAs / Q000235:genetics
chemical_list: D035683:MicroRNAs