Enrico Capobianco, PhD

Summary

Lead Senior Bioinformatics Scientist

Enrico Capobianco holds a Doctorate in Statistical Sciences from the University of Padua.  After conducting graduate studies at LSE (London, UK), Northwestern University, and UC Berkeley, he pursued research in computational fields at Stanford University (US) (1994-1998).  He received a NATO-CNR grant in Denmark (Niels Bohr Institute and Danish Technical University) and later (2001-2002) became an ERCIM fellow at CWI (Center for Mathematics and Computer Science) in  Amsterdam (NL). He returned to US in 2003 working for as a Staff scientist at the  Mathematical Sciences Research Institute in Berkeley, and then as a Senior Scientist at  Boston University, Biomedical Engineering (2004-2005). In 2005, he was appointed Head of Methods at Serono (Evry, FR) and then in 2006, he joined the Center for Advanced Studies, Research and Development in Sardinia at the Polaris Science and Tech Park leading a quantitative systems biology group.

Dr. Capobianco’s core expertise is in statistical methods for bioinformatics, bio-network inference and analysis, machine learning and signal/image processing applied to biomedicalstudies.  His background includes participation in academic and scientific activities at SAMSI, IMA, MSRI and IPAM institutes in US, CAS in China, ICTP in Italy, Fiocruz in  Brazil (visiting professor 2008-2010 within the Program, Capes – FIOCRUZ), and the Institut des Hautes Études Scientifiques (IHES) in France (Visiting scientist 2010).

Publications

  1. M. Orsini, A. Travaglione, E. Capobianco (2013) Cancer Markers: Integratively Annotated Classification. Gene, accepted
  2. E. Capobianco (2013) Protein Networks Tomography. Targeting Cancer and Associated Morbidities. Systems Biomedicine. In press.
  3. Warehousing Re-annotated Cancer Genes for Biomarker Meta-analysis.M. Orisini, A. Travaglione, E. Capobianco, Computer Methods and Programs in Biomedicine: Volume 111, Issue 1, July 2013, Pgs. 166-18]=
  4. Multiscale Characterization of Signaling Network Dynamics through Features.  E. Capobianco, E. Marras, A. Travaglione  Statistical Applications in Genetics and Molecular Biology Vol. 10: Iss. 1, Article 53, 2011. DOI: 10.2202/1544-6115.165
  5.  Modules as Dynamic Entities in Protein Interactomes.  A.Travaglione, E. Marras, E. Capobianco Invited book chapter in ‘Statistical and Machine Learning Approaches for Network Analysis’, edited by Wiley, USA, 2011.
  6. On network entropy in bio-interactome applications.  E. Capobianco Journal of Computational Science, 2, 144-152, 2011.   
  7. Protein Interactomic Manifold Learning.  E. Marras, A. Travaglione, and E. Capobianco Journal of Computational Biology, 18(1), 81-96, 2011.
  8. Dynamic Modularization Assessment in Affine Protein Interaction Networks.  A.Travaglione, E. Marras, E. Capobianco Biostatistics, Bioinformatics, and Biomathematics (BIOSIM), 2(3), 137-156, 2011.
  9. A proteomic study of microgravity cardiac effects: feature maps of label-free LC-MALDI data for differential expression analysis.  S. Rocchiccioli, E. Congiu, C. Boccardi, L. Citti, L. Callipo, A. Lagana’, E. Capobianco  Molecular Biosystems, 6, 2218-2229, 2010. DOI:10.1039/C0MB00065E.
  10. Inferring Modularity from Human Protein Interactome Classes.  E. Marras, A. Travaglione, G. Chaurasia, M. Futschik, and E. Capobianco (2010). BMC Systems Biology, 4:102, 2010. doi:10.1186/1752-0509-4-102 ‘Highly accessed’.    
  11. Sub-Modular Resolution Analysis by Network Mixture Models.  E. Marras, A. Travaglione and E. Capobianco  Statistical Applications in Genetics and Molecular Biology, 9(1), Article 19, 2010. DOI: 10.2202/1544-6115.1523.
  12. Two-dimensional Electrophoresis Gel Images Scan for decomposition and depletion analysis.  O. Marullo, A. Soggiu, E. Capobianco Advances in Adaptive Data Analysis, Vol 2(3), 359-371, 2010. DOI No: 10.1142/S1793536910000525.
  13. Coupling genomic expression with copy number variation in brain cancer regulation studies.  M.Orsini and E. Capobianco Biostatistics, Bioinformatics, and Biomathematics (BIOSIM), 1(1), 55, 2010.
  14. Time-scale analysis from fragmented protein interactome networks. A. Travaglione, E. Marras, E. Capobianco Proceedings Computational Systems Biology (WCSB 2010).
  15. Network Mixture Models: an Introduction and Application to Protein Interactomes. E. Marras and E. Capobianco. Institute Mathematics and its Applications – IMA, 2010 preprint series 2230.
  16. Gene Feature Interference Deconvolution.  E. Capobianco Mathematical Biosciences, 227, 136-146 http://dx.doi.org/10.1016/j.mbs.2010.07.003, 2010.
  17. Inference by mixture models in protein interactome networks.  E. Marras and E. Capobianco WCSB 2009 Proceedings, TICSP series # 48, 119-122, 2009.   
  18. Entropy Embedding and Fluctuation Analysis in Genomic Manifolds.  E. Capobianco Communications in Nonlinear Science and Numerical Simulation 14: 2602-2618, 2009.
  19. Aliasing in gene feature detection by projective methods.  E. Capobianco.  Journal of Bioinformatics and Computational Biology, 7(4), 685-700, 2009.
  20. Empowering spot detection in 2DE images by wavelet denoising.  A. Soggiu, O. Marullo, P. Roncada,E. Capobianco In Silico Biology, 9, 0011, 2009.
  21. In Vivo quantitation of metabolites with an incomplete model function.  E. Popa, E. Capobianco, R. de Beer, D. van Ormondt, D. Graveron-Demilly Measurement Science Technology, IOP Press, 20, 104032, 2009 (Special Issue Imaging Systems and Techniques).
  22. Backing up 2DGE Depletion with Source Separation and Multiscale Decomposition Methods.  A. Soggiu, O. Marullo,  P. Roncada, and E. Capobianco Biophysical Reviews and Letters, vol. 4(4), 319-330, 2009.
  23. Mining Protein-Protein Interaction Networks: Denoising Effects. E. Marras and E. Capobianco Journal of Statistical Mechanics: Theory and Experiments P01006, 2009.
  24.  Model validation for gene selection and regulation maps. E. Capobianco Functional and Integrative Genomics, 8(2):87-99, 2008.
  25. Advances in Human Protein Interactome Inference.  E. Marras and E. Capobianco In: Functional and Operational Statistics, S. Dabo-Niang and F. Ferraty Eds., Physica- Verlag, Heidelberg 89-94, 2008.
  26. Kernel Methods and Flexible Inference for Complex Stochastic Dynamics.  E. Capobianco Physica A: Statistical Mechanics and its Applications, 387(16-17): 4077-4098, 2008.
  27. Cascade Systems Denoising and Greedy Calibrated Approximation.  E. Capobianco Journal of Interdisciplinary Mathematics, 11(3): 429-442, 2008.   
  28. Dimensionality Reduction and Greedy Learning of Convoluted Stochastic Dynamics.  E. Capobianco Nonlinear Analysis, Real World Applications, Series B, 9(5): 1928-1941, 2008.
  29.  Protein networking: insights into global functional organization of proteomes.  E. Pieroni, S. de la Fuente van Bentem, G. Mancosu, E. Capobianco, H. Hirt, and A. de la Fuente. Proteomics, 8(4):799-816, 2008.
  30.  Lineshape estimation in in vivo MRS without using a reference signal.  E. Popa, E. Capobianco, R. de Beer, D. van Ormondt, D. Graveron-Demilly  IEEE-IST (Imaging Systems and Techniques) Proceedings, 2008, 315-320.
  31. Exploring Protein Interactome Features  E. Marras and E.Capobianco WCSB 2008 Proceedings, TICSP Series  n. 41, 105-108, 2008.
  32. Sieving Genomic Features by Signal Separation and Interference Sparsification. E.Capobianco SAMSI (Statistical and Applied Mathematical Sciences Institute) TR 2007-3.
  33. Signal Disentanglement in In Vivo MR Spectroscopy: by semi-parametric processing or by measurement?  H. Rabeson, H. Ratiney, C. Cudalbu, S. Cavassila, E. Capobianco, R. de Beer, D. van Ormondt, and D.Graveron-Demilly. ProRISC, IEEE Benelux, Veldhoven, The Netherlands, November 2006.
  34. Error Bounds for Semi-Parametric Estimation in MRS. H. Ratiney, E. Capobianco, R. de Beer, D. van Ormondt, and D.Graveron-Demilly. Proc. Int. Soc. Mag. Res. Med., 14th Scient. Meet., Seattle, WA (US) 3237,  6-2, 2006.
  35. Electronic Journal of Theoretical Physics, 4(15), 165-180, 2007.
  36. SPIE Proc., Complex dynamics and Fluctuations in Biomedical Photonics IV, 6436,2007.
  37. Journal of Numerical Analysis, Industrial and Applied Mathematics (JNAIAM), 1(2) 147-174, 2007.
  38. Electronic Journal of Theoretical Physics, 3 (11), 85-109, 2006.   
  39. Statistical Embedding in Complex Biosystems. E. Capobianco Journal of Integrative Bioinformatics, 3(2) R. Hofestadt and T. Topel (Eds.), Shaker-Verlag, 117-135, 2006.
  40. Semiparametric Estimation in In-vivo MR Spectroscopy.  H.Ratiney, E. Capobianco, M.Sdika, H.Rabeson, C.Cudalbu, S.Cavassila, R.de Beer, D.van Ormondt, D.Graveron-Demilly ProRISC, IEEE Benelux, Veldhoven, The Netherlands, 658-667, 2005.   
  41. Mining Time-dependent Gene Features.  E. Capobianco Journal of Bioinformatics and Computational Biology, 3(5), 1191-1205, 2005.
  42. Denoising and Dimensionality Reduction of Genomic Data. E. Capobianco SPIE Proc., Fluctuation and Noise in Biological, Biophysical, and Biomedical  Systems III, N.G. Stocks, D. Abbott, R.P. Morse (Eds.), vol. 5841 (SPIE, WA, 2005), pp. 69-80, 2005.
  43. Robustness versus Redundancy in Biological Systems. E. Capobianco Fluctuation and Noise Letters, 5(3), L375-L385, 2005.   
  44. Robustness versus Redundancy in Biological Systems (short version). E. Capobianco American Institute of Physics, Unsolved Problems of Noise and Fluctuations in Physics, Biology, and High Technology, L. Reggiani et. al (Eds.), Conf. Proceedings Vol. 800, pp. 361-367, 2005.
  45. Preprint 2020, IMA, Institute for Mathematics and its Applications, Minneapolis, MN, 2005
  46. Preprint 2031, IMA, Institute for Mathematics and its Applications, Minneapolis, MN, 2005
  47. Physica  A, 340 (1-3), 340-346, 2004
  48. Fractals, 12(2), 179-195, 2004.
  49. Nonlinear Phenomena in Complex Systems. 7(4), 314-331, 2004.
  50. Physica A, 344, 122-127, 2004. 
  51. Preprint 2011, IMA, Institute for Mathematics and its Applications, Minneapolis, MN, 2004
  52. Preprint 2013, IMA, Institute for Mathematics and its Applications, Minneapolis, MN, 2004
  53. On Support Vector Machines and Sparse Approximation for Random Processes.  E. Capobianco Neurocomputing, 56, 39-60, 2004.
  54. Independent Multi-resolution Component Analysis and Matching Pursuit (2003) E. Capobianco Computational Statistics and Data Analysis, 42(3), 385-402   
  55. Computationally efficient atomic representations for non-stationary stochastic processes (2003) E. Capobianco, International Journal on Wavelets, Multiresolution and Information Processing,1(3), 325-351.
  56. Physica A, 319, 495-518, 2003.
  57. IEEE Proc. PhysCon 2003, Physics & Control, pp. 222-227. S.Petersburg, RU, Aug 20-22, 2003.
  58. SPIE Proc., ICA, Wavelets NN, 5102, pp. 360-370, 2003. Orlando, Fl, April 21-25, 2003.
  59. Functional Approximation in Multi-scale Complex Systems.  E. Capobianco Advances in Complex Systems, 6(2), 177-204, 2003.
  60. Preprint 2003-008, MSRI, Mathematical Sciences Research Institute, Berkeley, 2003
  61. Quntitative Finance, 2(2), 2002. DOI:10.1088/1469-7688/2/2/301
  62. The European Physical Journal B, 27(2), 201-212, 2002.
  63. Neurocomputing 48(4), 779-806, 2002.
  64. Technical Report, PNA-R0217, CWI, Amsterdam, 2002
  65. Technical Report, PNA-R0111, CWI, Amsterdam, 2001
  66. Technical Report, PNA-R0114, CWI, Amsterdam, 2001
  67. EURASIP Journal on Applied Signal Processing, 2, 1-7, 2001.
  68. ENUMATH (4th European Numerical Mathematics and Advanced Applications) Proc. (Springer). Ischia, IT, July 23-28, 391-408, 2001.
  69. Nonlinear Estimation and Classification, pp. 273-285. Proc. (Springer-Verlag) of Workshop at MSRI, Berkeley, March 19-29, 2001.
  70. International Journal of Theoretical and Applied Finance, 4(3), 511-534, 2001.
  71. Artificial Neural Nets and Genetic Algorithms, pp. 426-430 (Springer-Verlag). Proc. of International Conf. in Prague (Czech Rep.), 2001.
  72. Computational Statistics and Data Analysis, 32, 443-454, 2000.   
  73. Methodology and Computing in Applied Probability, I(4), 423-443, 1999.
  74. Badanja Operacyjne I Decyzje, 1, 21-33, 1999.  
  75.  Badanja Operacyjne I Decyzje, 2, 15-25, 1999.
  76. Methodology and Computing in Applied Probability, I (4), 423-443, 1999.
  77. Stochastic Modelling and Applications, I(1), 44-59, 1998.
  78. Chapter in: Mathematics of Neural Networks; Models, Algorithms and Applications, S.W.Ellacott, J.C.Mason and I.J.Anderson (Eds) Kluwer Ac. Pub.,  140-147, 1997.
  79. Computing Science and Statistics, (Interface Foundation of North America) 29 (2), pp. 95-101, 1997. Proc. of 2nd IASC World Conf., Pasadena,  CA (US), 02/19-22/1997.
  80. Neural Networks World: International Journal on Neural and Mass Parallel  Computing and Information Systems, 7 (4-5), 439-450, 1997.
  81. International Journal of Modelling and Simulation, 17, 137-142, 1997.
  82. Applied Stochastic Models and Data Analysis, 12 (4), 265-279, 1996.
  83. CISS’96, Proc. Conf. Information Sciences and Statistics, II, Princeton Un., 1037-1042, 1996.
  84. NNSP’95, Neural Networks for Signal Processing Proc. of IEEE, MIT Press, 87-94, 1995. Complex Systems, 9, 477-490, 1995