Computational Biology & Bioinformatics

Computational Biology & Bioinformatics
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The Computational Biology & Bioinformatics (CBB) Program conducts research and offers services and training in the management and analysis of biological and biomedical data.

Computational Biology and Bionformatics Circos-AACR[1] Circos-VizBi[1]

The CBB faculty and staff are a diverse group of interdisciplinary specialists with research interests that span the life sciences, and Computational Biology & Bioinformatics expertise areas in building infrastructure, developing algorithms, and designing and implementing analytical approaches. The data CBB handles ranges from species ecology through to genomic medicine. Particular focus areas are illustrated on our Research page.

The CBB Program brings computer scientists and engineers together with clinicians and medical research scientists to develop the methods and tools needed for the analysis of complex high-dimensional data sets.


Program Director

Vance Lemmon, Center for Computational Science, University of Miami

Vance Lemmon, PhD



Enrico Capobianco, Center for Computational Science, University of Miami



Phone: 305.689.7031
Office: Room 600K, Gables One Tower

Enrico Capobianco holds a Doctorate in Statistical Sciences from the University of Padua.  After conducting graduate studies at LSE (The London School of Economics and Political Science, 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/Centrum Wiskunde & Informatica) 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 (aka CRS4) 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 biomedical studies.  His background includes participation in academic and scientific activities at:

  • SAMSI (Statistical and Applied Mathematics Sciences Institute) (North Carolina),
  • IMA (Institute for Mathematics and its Applications) (Minnesota),
  • MSRI (Mathematical Sciences Research Institute) (Berkeley),
  • IPAM (Institute for Pure & Applied Mathematics) (UCLA),
  • CAS (Chinese Academy of Sciences) in China,
  • ICTP (International Centre for Theorectical Physics) in Italy,
  • Fiocruz (Fundacao Oswaldo Cruz) in Brazil (visiting professor 2008-2010 within the Program, Capes – FIOCRUZ), and the
  • Institut des Hautes Études Scientifiques (IHES) in France (Visiting scientist 2010)





In Press:

  • Capobianco E and Lio’ P. (2015) Electronic Health Systems: Golden Mine for Precision Medicine?  Current bottlenecks and future opportunities associated with Big Data. Journal of Precision Medicine.
  • Ianuale N, Schiavon D, Capobianco, E (2015). Smart Cities, Big Data and Communities: Reasoning from the viewpoint of attractors. IEEE Access – Smart Cities.


Recently Published

  1. Capobianco. (2015) On digital therapeutics. Frontiers Digital Humanities.  published: 10 November 2015 doi: 10.3389/fdigh.2015.00006
  2. Capobianco E. Cancer Hallmarks through the Network Lens. Cancer Cell & Microenvironment. 2015; 2: e943.
  3. Malusa F, Zaki N, Badidi E, Cinti C, Capobianco E (2015). Time-course Gene Expression Profiling and Networks Dynamics in Demethylated Retinoblastoma cell line. Oncotarget. 2015 Jun 25.  [Epub ahead of print].
  4. Mora A., Taranta M., Zaki N., Cinti C., Capobianco E. (2015). Epigenetically-driven Network Cooperativity: Meta-analysis in Multi-Drug Resistant Osteosarcoma. Journal of Complex Networks. doi: 10.1093/comnet/cnv017  First published online: July 9, 2015
  5. A.Marranci, A.Tuccoli, E. Mercoledi, M. Vitiello, C. Valdes, F. Russo, M. Dal Monte, M. Pellegrini, E. Capobianco, N.Tsinoremas, L.Poliseno. Identification of BRAF 3’UTR Isoforms in Melanoma.Journal of Investigative Dermatology. 2015 Feb 16. doi: 10.1038/jid.2015.47.
  6. Mora A, Sicari R, Cortigiani L, Carpeggiani C, Picano E, Capobianco E. 2015 Prognostic models in coronary artery disease: Cox and network approaches.  Soc. Open Sci. 2: 140270.
  7. Capobianco E. 2015 Computational Multiplexing Imaging: a Network Approach. Suppl. J Nucl Med, 56, 3.
  8. Kleiman E, Salyakina D, De Heusch M, Hoek KL, Llanes JM, Castro I, Wright JA, Clark ES, Dykxhoorn DM, Capobianco E, Takeda A, Renauld J-C and Khan WN (2015) Distinct transcriptomic features are associated with transitional and mature B-cell populations in the mouse spleen.  Front. Immunol. 6:30. doi: 10.3389/fimmu.2015.00030
  9. Capobianco and P. Lio’. Comorbidity Networks: Beyond Correlation. Journal of Complex Networks. jcomplexnetw doi: 10.1093/comnet/cnu048. First published online: January 7, 2015.
  10. M.Dominietto, N. Tsinoremas and E. Capobianco (2015) Integrative Analysis of Cancer Imaging Readouts by Networks. Molecular Oncology, 9(1), 1-16.
  11. Capobianco E. and Trivella M.G. (2014). Systems Medicine Road Map: Shaping Data Complexity towards Cultural Diversity. Pan European Networks: Science and Technology, pg 128-9.
  12. Capobianco E. and Lio’ P. (Eds.) Comprehensive Systems Biomedicine. E-Book,  Special Topic, Frontiers in Genetics, 2014.
  13. Capobianco E. (2014) RNA-Seq Data: a Complexity Journey. Computational and Structural Biotechnology Journal, 11(19), 123-130.
  14. Capobianco E. and Trivella M.G. (2014). Networks: Elucidating Experimental Data by Differential Protein-Protein Interactions. Journal of Computational Systems Biology 1(1): 101.
  15. Capobianco E and Lio’ P. Advances in Translational Biomedicine from Systems Approaches. Editorial article for the Special Topic: Comprehensive Systems Biomedicine.  Frontiers Genetics, 14 August 2014 | doi: 10.3389/fgene.2014.00273.
  16. Capobianco, E. and Tsinoremas, N. (2014). Health or Disease – Why does Dark Matter matter more? Journal of Investigative Genomics 1(1), 00002.
  17. H.R. Raju, Z.A. Englander, E. Capobianco, N. Tsinoremas, J.K. Lerch. (2014) Identification of therapeutic targets in a model of neuropathic pain.  Frontiers in Genetics, doi: 10.3389/fgene.2014.00131
  18. Capobianco, E., Mora, A., La Sala D., Roberti A., Zaki N., Taranta, M. and Cinti C. (2014) Separate and combined effects of of de-methylating and de-acetylating agents in the treatment of human multi-drug resistant osteosarcoma cell lines.  PlOS ONE 9(4), e95596.
  19. Mora A, Taranta M, Zaki N, Badidi E, Cinti C and Capobianco E (2014) Ensemble inference by integrative cancer networks. Frontiers Genetics 5:59. doi: 10.3389/fgene.2014.00059
  20. Pelosi G, Rocchiccioli S, Cecchettini A, Viglione F, Puntoni M, Parodi O, Capobianco E and Trivella MG (2014) Inflammation blood and tissue factors of plaque growth in an experimental model evidenced by a systems approach. Frontiers Genetics 5:70. doi: 10.3389/fgene.2014.00070.
  21. El Baroudi, M., La Sala, D., Cinti, C., and Capobianco, E. (2104) Pathway landscape and regulatory networks of epigenetic regulation in breast cancer and melanoma. Theoretical Biology and Medical Modelling, 11(Suppl 1):S8.
  22. Valdes, C. and Capobianco, E. (2014) Methods to detect transcribed pseudogenes: RNA-Seq discovery allows learning through features. In: Pseudogenes. Functions and Protocols (L. Poliseno Ed.). Methods in Molecular Biology, 1167. Springer Protocols, Humana Press.
  23. Capobianco, E. (2013) Protein Networks Tomography – Targeting Cancer and Associated Morbidities. Systems Biomedicine, 1(3), 1-17.
  24. Capobianco, E. and Lio’, P. (2013) Comorbidity: a multidimensional approach. Trends in Molecular Medicine, Cell Press, 19(9) September 2013, 515–21.
  25. Orsini, M., Travaglione, A. and Capobianco, E. (2103) Warehousing re-annotated cancer genes for Biomarker meta-analysis. Computer Methods and Programs in Biomedicine, III, 166-180.
  26. Orsini, M. and Capobianco, E. (2103) MiRWare: A MiRNA-mRNA warehouse for inference on cancer regulation. Microrna. .2(2), pp. 148-155(8).
  27. Orsini, M. Travaglione, A. Capobianco, E. (2013) Cancer Biomarkers: Integratively Re-annotated Classification. Gene 530(2):257-65.


Older Publications

  1. Capobianco, E. (2012) Multiscale fragPIN Modularity. ISRN Genomics, 2012, Art 307608.
  2. Capobianco, E. (2012) Constrained Network Modularity. ISRN Biomathematics, Art. 192031.
  3. Capobianco, E. (2012). Ten challenges for Systems Medicine. Frontiers in Genetics (sect Statistical Genetics and Methodology), 3, Art 193.
  4. Capobianco, E. (2012) Dynamic Networks in Systems Medicine. Frontiers in Genetics (sect Bioinformatics and Computational Biology), 3, Art 185
  5. Capobianco,E., Travaglione, A., Marras, E. (2012). Modularity Configurations in Biological Networks with Embedded Dynamics Invited Book chapter in ‘Statistical and Machine Learning Approaches for Network Analysis’. M. Dehmer and SC Basak (Eds.), Wiley, NJ USA, pg 109-129.
  6. Marras, E. Travaglione, A. and Capobianco, E. (2011). Multiscale Characterization of Signaling Network Dynamics through Features. Statistical Applications in Genetics and Molecular Biology.  Vol. 10: Iss. 1, Article 53, 2011
  7. Capobianco, E. (2011). On network entropy in bio-interactome applications. Journal of Computational Science, 2 (2011) 144-152.
  8. Marras, E., Travaglione, A., and Capobianco, E. (2011). Protein Interactomic Manifold Learning. Journal of Computational Biology, 18(1) – 81-96.
  9. Travaglione, A., Marras, E., Capobianco, E. (2011). Dynamic Modularization Assessment in Affine Protein Interaction Networks. BiosimBiostatistics, Bioinformatics, and Biomathematics, 2(3) 2011 137-156.
  10. Rocchiccioli, S., Congiu, E., Boccardi, C., Citti, L., Callipo, L., Lagana’, A., Capobianco, E. (2010). A proteomic study of microgravity cardiac effects: feature maps of label-free LC-MALDI data for differential expression analysis.  Molecular Biosystems, DOI:10.1039/C0MB00065E.
  11. Marras, E., Travaglione, A., Chaurasia, G., Futschik, M., and Capobianco, E. (2010). Inferring Modularity from Human Protein Interactome Classes.  BMC Systems Biology, 4:102. ‘Highly accessed’
  12. Marras, E., Travaglione, A., and Capobianco, E. (2010). Sub-Modular Resolution Analysis by Network Mixture Models. Statistical Applications in Genetics and Molecular Biology, 9(1), Art 19.
  13. Marullo, O., Soggiu, A., Capobianco, E. (2010). Two-dimensional Electrophoresis Gel Images Scan for decomposition and depletion analysis. Advances in Adaptive Data Analysis, Vol 2(3), 359-371
  14. Orsini, M. and Capobianco, E. (2010). Coupling genomic expression with copy number variation in brain cancer regulation studies. Biosim – Biostatistics, Bioinformatics, and Biomathematics, 1(1), 2010, 55.
  15. Capobianco, E. (2010). Gene Feature Interference Deconvolution.  Mathematical Biosciences, 227, 136-146.
  16. Soggiu, A., Marullo, O., Roncada, P., and Capobianco, E. (2009). Backing up 2DGE Depletion with Source Separation and Multiscale Decomposition Methods.  Biophysical Reviews and Letters, vol. 4(4), 319-330, 2009.
  17. Popa, E., Capobianco, E., de Beer, R., van Ormondt, D., Graveron-Demilly, D. (2009). In Vivo quantitation of metabolites with an incomplete model function. Measurement Science Technology, IOP Press, 20, 2009, 104032 (Special Issue Imaging Systems and Techniques).
  18. Capobianco, E. (2009). Aliasing in gene feature detection by projective methods. Journal of Bioinformatics and Computational Biology, 2009, 7(4), 685-700.
  19. Soggiu, A., Marullo, O., Roncada, P., Capobianco, E. (2009). Empowering spot detection in 2DE images by wavelet denoising. In Silico Biology, 9 0011, 2009.
  20. Capobianco, E. (2009). Entropy Embedding and Fluctuation Analysis in Genomic Manifolds. Communications in Nonlinear Science and Numerical Simulation 14, 2602-18.
  21. Marras, E. and Capobianco, E. (2009). Mining Protein-Protein Interaction Networks: Denoising Effects. Journal of Statistical Mechanics: Theory and Experiments P01006.
  22. Capobianco, E. (2008) Kernel Methods and Flexible Inference for Complex Stochastic Dynamics. Physica A: Statistical Mechanics and Applications, 387(16-17), 4077-98.
  23. Marras, E. and Capobianco, E. (2008) Advances in Human Protein Interactome Inference.  Functional and Operational Statistics, S. Dabo-Niang and F. Ferraty Eds., Physica-  Verlag, Heidelberg 89-94.
  24. Capobianco, E. (2008). Cascade Systems Denoising and Greedy Calibrated Approximation. Journal of Interdisciplinary Mathematics, 11(3): 429-442.
  25. Capobianco, E. (2008). Dimensionality Reduction and Greedy Learning of Convoluted Stochastic Dynamics. Nonlinear Analysis, Real World Appl Series B, 9(5): 1928-41.
  26. Pieroni, E., de la Fuente van Bentem, S., Mancosu, G., Capobianco, E., Hirt, H., and de la Fuente, A. (2008). Protein networking: insights into global functional organization of proteomes. Proteomics, 8(4):799-816.
  27. Capobianco, E. (2008). Model validation for gene selection and regulation maps. Functional and Integrative Genomics, 8(2):87-99.
  28. Capobianco, E. (2006). Statistical Embedding in Complex Biosystems. Journal of Integrative Bioinformatics, 3(2), Hofestadt, Topel (Eds.), Shaker-Verlag, pp. 117-35.
  29. Capobianco, E. (2005). Mining Time-dependent Gene Features.  Journal of Bioinformatics and Computational Biology, 3(5), 1191-1205.
  30. Capobianco, E. (2005). Robustness versus Redundancy in Biological Systems.  Fluctuation and Noise Letters, 5(3), L375-L385.
  31. Capobianco, E. (2004). On Support Vector Machines and Sparse Approximation for Random Processes. Neurocomputing, 56, 39-60.
  32. Capobianco, E. (2003). Functional Approximation in Multi-scale Complex Systems. Advances in Complex Systems, 6(2), 177-204.
  33. Capobianco, E. (2003). Computationally efficient atomic representations for non-stationary stochastic processes. International Journal on Wavelets, Multiresolution and Information Processing, 1(3), 325-351.
  34. Capobianco, E. (2003). Independent Multi-resolution Component Analysis and Matching Pursuit. Computational Statistics and Data Analysis, 42(3), 385-402.



  1. El Baroudi, M., La Sala, D., Cinti, C., Capobianco E. (2013) DAC-driven Integrative Network Regulation and Pathway Coordination in Breast Cancer, IWBBIO 2013 (International Work-Conference on Bioinformatics and Biomedical Engineering), Granada, March 18-20, pg 413
  2. Travaglione, A., Marras,E.,  Capobianco, E. (2010). Time-scale analysis from fragmented protein interactome networks. Proceedings Computational Systems Biology (WCSB 2010).
  3. Popa, E. Capobianco, E. de Beer, R., van Ormondt, D., Graveron-Demilly, D.  (2008) Lineshape estimation in in vivo MRS without using a reference signal
  4. IEEE-IST (Imaging Systems and Techniques) Proceedings, 315-320.
  5. Marras, E. and Capobianco, E. (2009). Inference by mixture models in protein interactome networks. WCSB 2009 Proceedings, TICSP series # 48, 119-122.
  6. Network Mixture Models: an Introduction and Application to Protein Interactomes.  Marras, E. and Capobianco, E. (2009). Institute Mathematics and its Applications – IMA, preprint series 2230.
  7. Marras, E. and Capobianco, E. (2008) Exploring Protein Interactome Features.  WCSB 2008 Proceedings, TICSP Series  n. 41, 105-108.
  8. Rabeson, H., Ratiney, H., Cudalbu, C., Cavassila, S., Capobianco, E., de Beer, R.,  van Ormondt, D., and Graveron-Demilly, D. (2006). Signal Disentanglement in In Vivo MR Spectroscopy: by semi-parametric processing or by measurement? ProRISC, IEEE Benelux, Veldhoven, The Netherlands.
  9. Ratiney, H., Capobianco, E., de Beer, R., van Ormondt, D., and Graveron-Demilly.D., (2006). Error Bounds for Semi-Parametric Estimation in MRS.  Proc. Int. Soc. Mag. Res. Med., 14th Scient. Meet., Seattle, WA (US) 3237,  6-2.
  10. Ratiney, H., Capobianco, E., Sdika, M., Rabeson, H., Cudalbu, C., Cavassila, S., de Beer, R., van Ormondt, D., and Graveron-Demilly, D. (2005). Semiparametric Estimation in In-vivo MR Spectroscopy. ProRISC, IEEE Benelux, Veldhoven, The Netherlands, 658-667.
  11. Capobianco, E. (2005). Robustness versus Redundancy in Biological Systems (short version). 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.
  12. Capobianco, E. (2005). Denoising and Dimensionality Reduction of Genomic Data. 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.


Other Publications in my Name

  1. Electronic Journal of Theoretical Physics, 4(15), 165-180, 2007.
  2. SPIE Proc., Complex dynamics and Fluctuations in Biomedical Photonics IV, 6436,2007.
  3. Journal of Numerical Analysis, Industrial and Applied Mathematics (JNAIAM), 1(2) 147-174, 2007.
  4. Electronic Journal of Theoretical Physics, 3 (11), 85-109, 2006.
  5. Physica A, 340 (1-3), 340-346, 2004
  6. Nonlinear Phenomena in Complex Systems. 7(4), 314-331, 2004.
  7. Fractals, 12(2), 179-195, 2004
  8. Physica A, 344, 122-127, 2004.
  9. Physica A, 319, 495-518, 2003.
  10. IEEE Proc. PhysCon 2003, Physics & Control, pp. 222-227. S.Petersburg, RU, Aug 20-22, 2003.
  11. SPIE Proc., ICA, Wavelets NN, 5102, pp. 360-370. Orlando, Fl, April 21-25, 2003.
  12. The European Physical Journal B, 27(2), 201-212, 2002.
  13. Neurocomputing 48(4), 779-806, 2002.
  14. EURASIP Journal on Applied Signal Processing, 2, 1-7, 2001.
  15. ENUMATH (4th European Numerical Mathematics and Advanced Applications) Proc. (Springer). Ischia, IT, July 23-28, pp. 391-408, 2001.
  16. Nonlinear Estimation and Classification, pp. 273-285. Proc. (Springer-Verlag) of Workshop at MSRI, Berkeley, March 19-29, 2001.
  17. International Journal of Theoretical and Applied Finance, 4(3), 511-534, 2001.
  18. Artificial Neural Nets and Genetic Algorithms, pp. 426-430 (Springer-Verlag). Proc. of International Conf. in Prague (Czech Rep.), 2001.
  19. Computational Statistics and Data Analysis, 32, 443-454, 2000.
  20. Methodology and Computing in Applied Probability, I(4), 423-443, 1999.
  21. Badanja Operacyjne I Decyzje, 2, 15-25, 1999.
  22. Badanja Operacyjne I Decyzje, 1, 21-33, 1999.
  23. Methodology and Computing in Applied Probability, I(4), 423-443, 1999.
  24. Stochastic Modelling and Applications, I(1), 44-59, 1998.
  25. Chapter in: Mathematics of Neural Networks; Models, Algorithms and Applications, S.W.Ellacott, J.C.Mason and I.J.Anderson (Eds) Kluwer Ac. Pub.,  pp. 140-147, 1997.
  26. Computing Science and Statistics, (Interface Foundation of North America) 29 (2), pp. 95-101, 1997. Proc. 2nd IASC World Conf., Pasadena, CA (US), 02/19-22/1997
  27. Neural Networks World: International Journal on Neural and Mass Parallel  Computing and Information Systems, 7 (4-5), 439-450, 1997.
  28. International Journal of Modelling and Simulation, 17, 137-142, 1997.
  29. Applied Stochastic Models and Data Analysis, 12 (4), 265-279, 1996.
  30. CISS’96, Proc. Conf. Inf. Sciences and Statistics, Princeton Un, 1037-42, 1996.
  31. NNSP’95, Neural Networks for Signal Processing Proc. IEEE, MIT Press, 87-94, 1995.
  32. Complex Systems, 9, 477-490, 1995.
Zhijie Jiang


Phone: 305.243.1641
Office: Room 600.23, Gables One Tower

Dr. Zhijie Jiang received his PhD in Evolutionary Biology from Louisiana State University in 2006, and then spent one year working on genome annotation projects at Scripps, Florida (The Scripps Research Institute). His main projects at CCS can be divided into two categories: The first is genome assembling using next generation sequencing (NGS) data and genome annotation, his expertise includes both prokaryotic and eukaryotic genomes, run on two different NGS platforms; the second focus is gene expression analysis, working with microarray data as well as NGS data. This latter focus allows him to maintain an active interest in gene regulatory networks and promoter prediction.

Dr. Jiang is working in collaboration with the (John P.) Hussman Institute for Human Genomics (pictured below), the Sylvester Comprehensive Cancer Center, and UM’s Department of Biology.

Hussman Institute for Human Genomics

Hemalatha Raju with Sebastian the Ibis

Phone: 305.243.1066
Office: Room 600.18, Gables One Tower

Hemalatha Raju, MS, is a PhD student in Human Genetics and Genomics at the University of Miami. She received her BS in Physics and a master in Biophysics from the University of Chennai, India.

Her research interests include analyzing next-generation sequencing data using different NGS tools (Bowtie, BWA (Burrows-Wheeler Aligner), TopHat, and Cufflinks), designing pipeline to identify and analyze non-coding RNA in the transcriptome, classify the functional non-coding RNAs. Prior to joining the graduate program, she worked in molecular projects involving the study of magnetic nanoparticles in the eye for drug delivery and cell therapy.

University of Chennai, India

Daria Salyakina, Center for Computational Science, University of Miami

Phone: 305.243.1641
Office: Room 600.28, Gables One Tower

Daria Salyakina, PhD is an Assistant Scientist at the Center for Computational Science. She obtained her Master’s degree in Genetics from Tomsk State University in Russia (pictured below), and gained her PhD in Statistical Genetics from the Technical University Munich in Germany. Dr. Salyakina’s areas of expertise include Genetic Epidemiology, Human Genetics, Bioinformatics, and Biostatistics. She has the most experience in the area of identification of genomic and environmental risk factors attributing to various complex disorders. Her present research is focused on the investigating the genetic mechanisms of oncogenesis in viral-associated cancers.


Tomsk State University, Russia


Sevika Singh


Phone:  305.243.1606
Office:  Room 600.03, Gables One Tower

Sevika is a computer scientist and artist who is a student in the UM School of Communication M.S. program in Interactive Media. She is building data visualization tools for the RegenBase Project.

Camilo Valdes University of Miami Center for Computational Science

Phone: 305.243.1641
Office: Room 600.29, Gables One Tower

Camilo earned two B.S. degrees in Biology and in Computer Science from Florida International University, and is currently enrolled in a Masters program in Bioinformatics at FIU. He is a senior analyst for gene expression analysis from microarray datasets. He has experience with several microarray platforms, and has worked with data from different species. His expertise includes performing differential expression analyses of regulated genes using various analysis algorithms, along with pathways analysis and gene-set enrichment analysis. Camilo is also a software architect and is the lead developer for the iBIS portal and other bioinformatics tools.

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