It is exciting to be part of a new, growing, and ambitious research institution and to build a new and ambitious group in Bioinformatics and Computational Biology.
Research Focus at QCRI
At QCRI, Michele is focusing on developing algorithms and tools in the field of Integrative Bioinformatics and Systems Biology for Biomarker Discovery in Modeling and Diagnosis of Diseases. The availability of high-throughput technologies in the life sciences, and in particular for molecular biology, and their large-scale analysis gives the opportunity to pursue significant biomedical discoveries by associating molecular features to biological status and phenotypes such as stage and type of diseases, prognosis, response to and of therapies, and others. Searching and mining for the extraction of new knowledge from large available experimental dataset represents a novel great challenge for the computational science community.
Among the positions Michele held prior to joining QCRI was Associate Professor of Computer Science at the University of Sannio (Italy), where he worked on different problems of Computational Biology and Biomedical Imaging, and Principal Investigator of the Bioinformatics and Computational Biology Laboratory at the BIOGEM Research Center in Italy. Michele has made significant contributions to the field of cancer genomics, developing novel tools and algorithms for gene expression data analysis, gene network identification and structural genomic alterations. The algorithms developed by him and his teams have been used to discover novel biomarkers in colon and breast cancer, identification of novel gene fusions in brain tumors, and detection of specific gene signatures in developmental biology.
Michele has authored over 120 papers in international journals and conferences. In addition, he currently serves as associate editor of the journal Biomedical Data Mining, Conference Proceeding in Engineering. He has also been a committee member of many national and international conferences in the fields of Image Processing, Machine Learning and Bioinformatics; and chair of several international conferences such as the IEEE Conference on Imaging and Systems Techniques, and a workshop on Pattern Recognition Structural Proteomics and Bioinformatics. Moreover, Michele has served as the lead of several PRIN research projects in his research field supported by the Minister of University and Research in Italy, and joint projects between Academia and Industry such as KON3: Knowledge Ontology on Oncology Protocol. He has also served on the Boards of several Italian Regional Government Committees on eHealth and Information Society.
- BIOGEM Research Center, Principal Investigator of the Bioinformatics Laboratory - 2009
- University of Sannio, School of Sciences, Associate Professor of Computer Science - 2002
- National Research Council, Research Associate - 1994 - 1997
- University of Sannio, School of Sciences, Assistant Professor of Computer Science - 1997 - 2002
- Adjunct Professor at University of Salerno (Italy) - 1991 - 1994
- National Research Council, Scholarship - 1989 - 1993
Professional Associations and Awards
- Member of IEEE
- Member of IAPR (International Association for Pattern Recognition)
- Member of BITS (Bioinformatics Italy)
- Best paper award at “International Conference on Software Maintenance 2010” for the paper ”Using Multivariate Time Series and Association Rules to Detect Logical Change Coupling: an Empirical Study by Gerardo Canfora, Michele Ceccarelli, Luigi Cerulo, Massimiliano Di Penta.
- "Laurea" summa cum laude - University of Salerno (Italy), 1989
- Ceccarelli, M., L. Cerulo, and A. Santone (2014). De novo Reconstruction of Gene Regulatory Networks from Time Series Data, an approach based on Formal Methods. METHODS. [Pubmed].
- Cerulo, L., D. Tagliaferri, P. Marotta, P. Zoppoli, F. Russo, C. Mazio, M. DeFelice, M. Ceccarelli, and G. Falco (2014). Identification of a novel Gene Signature of ES cells self-renewal fluctuation through system-wide analysis. PLoS One 9(1), e83235. [Pubmed]
- Cerulo, L., V. Paduano, Z. Pietro, and M. Ceccarelli (2013). A negative selection heuristic to predict new transcriptional targets. BMC Bioinformatics 14(S1) [Pubmed]
- Paduano, V., D. Tagliaferri, G. Falco, and M. Ceccarelli (2013). Automated Identification and Location Analysis of Marked Stem Cells Colonies in Optical Microscopy Images. PLoS One 8(12), e80776. [Pubmed]
- Pagnotta, S. M., C. Laudanna, M. Pancione, L. Sabatino, C. Votino, A. Remo, L. Cerulo, P. Zoppoli, E. Manfrin, V. Colantuoni, and M. Ceccarelli (2013). Ensemble of Gene Signatures Identifies Novel Biomarkers in Colorectal Cancer Activated Through PPARγ and TNFα Signaling. PLoS One 8(8), e72638 [Pubmed]
- Morganella, S. and M. Ceccarelli (2012). VegaMC: A R/Bioconductor Package for Fast Down- stream Analysis of Large Array Comparative Genomic Hybridization Datasets. Bioinformatics 28(19), 2512–2514. [Pubmed]
- Morganella, S., S. M. Pagnotta, and M. Ceccarelli (2011). Finding recurrent copy number alter- ations preserving within-sample homogeneity. Bioinformatics 27(21), 2949–2956. [Pubmed]
- Cerulo, L., Charles, and M. Ceccarelli (2010). Learning Gene Regulatory Networks from Only Positive and Unlabeled Data. BMC Bioinformatics 11(1), 228. [Pubmed]
- Morganella, S., L. Cerulo, G. Viglietto, and M. Ceccarelli (2010). VEGA: Variational segmentation for copy number detection. Bioinformatics 26(24), 3020–3027. [Pubmed]
- Zoppoli, P., S. Morganella, and M. Ceccarelli (2010). TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an Information theoretic approach. BMC Bioinformatics 11, 154. [Pubmed]