Computational Data Science

within the activities of the CDS-group (formerly ), ICAR-CNR

Objectives

The activity concerns the design of models, algorithms, and software tools to discover, understand, and model scientific phenomena through the analysis of experimental data and/or through the simulation of their generation processes.

Main achievements

Omics Imaging:
- Integrating Imaging and Omics Data [1][4]. See also our companion web page on OmicsImaging.

- Glioma Grade Classification via Omics Imaging [6], Best Paper Award at the 7th International Conference on Bioimaging (BIOIMAGING2020).


Network Analysis:
- Clustering [2],[8] and classification [3],[5],[7] of metabolic networks. See also our poster on Supervised and Unsupervised Learning on Biological Networks (2019).

- Network similarities [7],[9].

Main collaborations:

- National Research University Higher School of Economics, Lab LATNA, Russia

- Stazione Zoologica “Anton Dohrn”, Naples, Italy

- University of Cassino and Southern Lazio, Italy

- University of Florida, U.S.A.

- University of Naples "L'Orientale", Naples, Italy

Useful/downloadable material:

Software:

- MetabolitesGraphs: R package to integrate gene expression data into a metabolic model and extract metabolites-based graphs, as used in [3],[5],[9]

- GraphDistances: R package to compute distribution-based distance measures between directed and undirected graphs/networks, as used in [3],[5],[9]

Web pages:

- OmicsImaging: Compendium of [4]

Publications on Computational Data Science:

[9] I. Manipur, I. Granata, L. Maddalena, and M.R. Guarracino, Network Distances for Weighted Digraphs, in Y. Kochetov et al. (Eds.), Mathematical Optimization Theory and Operations Research, CCIS 1275, Springer Nature Switzerland, ISBN: 978-3-030-58657-7, DOI: 10.1007/978-3-030-58657-7_31, 389-408, 2020.

[8] I. Manipur, I. Granata, L. Maddalena, and M.R. Guarracino, Clustering analysis of tumor metabolic networks, BMC Bioinformatics, ISSN: 1471-2105, DOI: 10.1186/s12859-020-03564-9, vol.21, n. 349, 2020.

[7] I. Granata, M.R. Guarracino, L. Maddalena, I. Manipur, and P.M. Pardalos, On Network Similarities and Their Applications, in R.P. Mondaini (Ed.), Trends in Biomathematics: Modeling Cells, Flows, Epidemics, and the Environment: Selected Works from the BIOMAT Consortium Lectures, Szeged, Hungary, 2019, Springer International Publishing, 23-41, ISBN: 978-3-030-46306-9, DOI: 10.1007/978-3-030-46306-9_3, 2020.

[6] L. Maddalena, I. Granata, I. Manipur, M. Manzo, and M.R. Guarracino, Glioma Grade Classification via Omics Imaging, in F. Soares, A. Fred, and H. Gamboa (Eds.), Proceed. 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC2020), Volume 2: BIOIMAGING, ISBN: 978-989-758-398-8, 82-92, 2020 (Best Paper Award).

[5] I. Granata, M.R. Guarracino, V. Kalyagin, L. Maddalena, I. Manipur, and P. Pardalos, Model simplification for supervised classification of metabolic networks, Annals of Mathematics and Artificial Intelligence 88, Springer, 91-104, 2020.

[4] L. Antonelli, M. R. Guarracino, L. Maddalena, and M. Sangiovanni, Integrating imaging and omics data: A review, Biomedical Signal Processing and Control 52, 264–280, 2019.

[3] I. Granata, M.R. Guarracino, V. Kalyagin, L. Maddalena, I. Manipur, and P. Pardalos, Supervised Classification of Metabolic Networks, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, 2688-2693, 2018.

[2] I. Manipur, I. Granata, L. Maddalena, K.P. Tripathi, and M.R.Guarracino, Clustering Analysis of Tumor Metabolic Networks, Bioinformatics and Computational Biology Conference (BBCC2018), Naples, November 2018.

[1] L. Antonelli, M. R. Guarracino, L. Maddalena, and M. Sangiovanni, Integrating Imaging and Omics Data: A Review, 17th International Symposium on Mathematical and Computational Biology - BIOMAT 2017, Moscow (Russia), November 2017.


Last update: October 15th, 2020