Enrico Capobianco
The Jackson Laboratory, USA
Research Expertise
About
Publications
Protein networking: insights into global functional organization of proteomes
PROTEOMICS / Feb 01, 2008
Pieroni, E., de la Fuente van Bentem, S., Mancosu, G., Capobianco, E., Hirt, H., & de la Fuente, A. (2008). Protein networking: insights into global functional organization of proteomes. PROTEOMICS, 8(4), 799–816. Portico. https://doi.org/10.1002/pmic.200700767
Comorbidity: a multidimensional approach
Trends in Molecular Medicine / Sep 01, 2013
Capobianco, E., & Lio’, P. (2013). Comorbidity: a multidimensional approach. Trends in Molecular Medicine, 19(9), 515–521. https://doi.org/10.1016/j.molmed.2013.07.004
Distinct Transcriptomic Features are Associated with Transitional and Mature B-Cell Populations in the Mouse Spleen
Frontiers in Immunology / Feb 11, 2015
Kleiman, E., Salyakina, D., De Heusch, M., Hoek, K. L., Llanes, J. M., Castro, I., Wright, J. A., Clark, E. S., Dykxhoorn, D. M., Capobianco, E., Takeda, A., Renauld, J.-C., & Khan, W. N. (2015). Distinct Transcriptomic Features are Associated with Transitional and Mature B-Cell Populations in the Mouse Spleen. Frontiers in Immunology, 6. https://doi.org/10.3389/fimmu.2015.00030
Separate and Combined Effects of DNMT and HDAC Inhibitors in Treating Human Multi-Drug Resistant Osteosarcoma HosDXR150 Cell Line
PLoS ONE / Apr 22, 2014
Capobianco, E., Mora, A., La Sala, D., Roberti, A., Zaki, N., Badidi, E., Taranta, M., & Cinti, C. (2014). Separate and Combined Effects of DNMT and HDAC Inhibitors in Treating Human Multi-Drug Resistant Osteosarcoma HosDXR150 Cell Line. PLoS ONE, 9(4), e95596. https://doi.org/10.1371/journal.pone.0095596
Smart Cities, Big Data, and Communities: Reasoning From the Viewpoint of Attractors
IEEE Access / Jan 01, 2016
Ianuale, N., Schiavon, D., & Capobianco, E. (2016). Smart Cities, Big Data, and Communities: Reasoning From the Viewpoint of Attractors. IEEE Access, 4, 41–47. https://doi.org/10.1109/access.2015.2500733
Multiscale Analysis of Stock Index Return Volatility
Computational Economics / Apr 01, 2004
Capobianco, E. (2004). Multiscale Analysis of Stock Index Return Volatility. Computational Economics, 23(3), 219–237. https://doi.org/10.1023/b:csem.0000022834.86489.e5
Systems and precision medicine approaches to diabetes heterogeneity: a Big Data perspective
Clinical and Translational Medicine / Jul 25, 2017
Capobianco, E. (2017). Systems and precision medicine approaches to diabetes heterogeneity: a Big Data perspective. Clinical and Translational Medicine, 6(1). Portico. https://doi.org/10.1186/s40169-017-0155-4
The landscape of BRAF transcript and protein variants in human cancer
Molecular Cancer / Apr 28, 2017
Marranci, A., Jiang, Z., Vitiello, M., Guzzolino, E., Comelli, L., Sarti, S., Lubrano, S., Franchin, C., Echevarría-Vargas, I., Tuccoli, A., Mercatanti, A., Evangelista, M., Sportoletti, P., Cozza, G., Luzi, E., Capobianco, E., Villanueva, J., Arrigoni, G., Signore, G., … Poliseno, L. (2017). The landscape of BRAF transcript and protein variants in human cancer. Molecular Cancer, 16(1). https://doi.org/10.1186/s12943-017-0645-4
Hammerstein system represention of financial volatility processes
The European Physical Journal B - Condensed Matter / May 01, 2002
Capobianco, E. (2002). Hammerstein system represention of financial volatility processes. The European Physical Journal B - Condensed Matter, 27(2), 201–211. https://doi.org/10.1140/epjb/e20020154
Smart cities and urban networks: are smart networks what we need?
Journal of Management Analytics / Mar 26, 2015
Ianuale, N., Schiavon, D., & Capobianco, E. (2015). Smart cities and urban networks: are smart networks what we need? Journal of Management Analytics, 2(4), 285–294. https://doi.org/10.1080/23270012.2015.1023856
WAVELET TRANSFORMS FOR THE STATISTICAL ANALYSIS OF RETURNS GENERATING STOCHASTIC PROCESSES
International Journal of Theoretical and Applied Finance / Jun 01, 2001
CAPOBIANCO, E. (2001). WAVELET TRANSFORMS FOR THE STATISTICAL ANALYSIS OF RETURNS GENERATING STOCHASTIC PROCESSES. International Journal of Theoretical and Applied Finance, 04(03), 511–534. https://doi.org/10.1142/s0219024901001097
Comorbidity networks: beyond disease correlations
Journal of Complex Networks / Jan 07, 2015
Capobianco, E., & Liò, P. (2015). Comorbidity networks: beyond disease correlations. Journal of Complex Networks, 3(3), 319–332. https://doi.org/10.1093/comnet/cnu048
RNA-Seq Data: A Complexity Journey
Computational and Structural Biotechnology Journal / Sep 01, 2014
Capobianco, E. (2014). RNA-Seq Data: A Complexity Journey. Computational and Structural Biotechnology Journal, 11(19), 123–130. https://doi.org/10.1016/j.csbj.2014.09.004
Ten Challenges for Systems Medicine
Frontiers in Genetics / Jan 01, 2012
Capobianco, E. (2012). Ten Challenges for Systems Medicine. Frontiers in Genetics, 3. https://doi.org/10.3389/fgene.2012.00193
Multiresolution approximation for volatility processes
Quantitative Finance / Apr 01, 2002
Capobianco, E. (2002). Multiresolution approximation for volatility processes. Quantitative Finance, 2(2), 91–110. https://doi.org/10.1088/1469-7688/2/2/301
Independent Multiresolution Component Analysis and Matching Pursuit
Computational Statistics & Data Analysis / Mar 01, 2003
Capobianco, E. (2003). Independent Multiresolution Component Analysis and Matching Pursuit. Computational Statistics & Data Analysis, 42(3), 385–402. https://doi.org/10.1016/s0167-9473(02)00217-7
From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health
Journal of Personalized Medicine / Mar 02, 2020
Capobianco, E., & Dominietto, M. (2020). From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health. Journal of Personalized Medicine, 10(1), 15. https://doi.org/10.3390/jpm10010015
Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing
Jan 26, 2021
Gialluisi, A., Di Castelnuovo, A., Costanzo, S., Bonaccio, M., Persichillo, M., Magnacca, S., De Curtis, A., Cerletti, C., Donati, M. B., de Gaetano, G., Capobianco, E., & Iacoviello, L. (2021). Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing. https://doi.org/10.1101/2021.01.22.21250338
Integrative analysis of cancer imaging readouts by networks
Molecular Oncology / Sep 10, 2014
Dominietto, M., Tsinoremas, N., & Capobianco, E. (2014). Integrative analysis of cancer imaging readouts by networks. Molecular Oncology, 9(1), 1–16. Portico. https://doi.org/10.1016/j.molonc.2014.08.013
Sub-Modular Resolution Analysis by Network Mixture Models
Statistical Applications in Genetics and Molecular Biology / Jan 09, 2010
Marras, E., Travaglione, A., & Capobianco, E. (2010). Sub-Modular Resolution Analysis by Network Mixture Models. Statistical Applications in Genetics and Molecular Biology, 9(1). https://doi.org/10.2202/1544-6115.1523
Multitype Network-Guided Target Controllability in Phenotypically Characterized Osteosarcoma: Role of Tumor Microenvironment
Frontiers in Immunology / Jul 31, 2017
Sharma, A., Cinti, C., & Capobianco, E. (2017). Multitype Network-Guided Target Controllability in Phenotypically Characterized Osteosarcoma: Role of Tumor Microenvironment. Frontiers in Immunology, 8. https://doi.org/10.3389/fimmu.2017.00918
Data-driven clinical decision processes: it’s time
Journal of Translational Medicine / Feb 12, 2019
Capobianco, E. (2019). Data-driven clinical decision processes: it’s time. Journal of Translational Medicine, 17(1). https://doi.org/10.1186/s12967-019-1795-5
Identification of potential therapeutic targets in a model of neuropathic pain
Frontiers in Genetics / May 23, 2014
Raju, H. B., Englander, Z., Capobianco, E., Tsinoremas, N. F., & Lerch, J. K. (2014). Identification of potential therapeutic targets in a model of neuropathic pain. Frontiers in Genetics, 5. https://doi.org/10.3389/fgene.2014.00131
Semi-Parametric Estimation in Magnetic Resonance Spectroscopy: Automation of the Disentanglement Procedure
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society / Aug 01, 2007
Rabeson, H., Ratiney, H., van Ormondt, D., & Graveron-Demilly, D. (2007). Semi-Parametric Estimation in Magnetic Resonance Spectroscopy: Automation of the Disentanglement Procedure. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/iembs.2007.4352377
Empirical volatility analysis: feature detection and signal extraction with function dictionaries
Physica A: Statistical Mechanics and its Applications / Mar 01, 2003
Capobianco, E. (2003). Empirical volatility analysis: feature detection and signal extraction with function dictionaries. Physica A: Statistical Mechanics and Its Applications, 319, 495–518. https://doi.org/10.1016/s0378-4371(02)01369-9
Emerging Putative Associations between Non-Coding RNAs and Protein-Coding Genes in Neuropathic Pain: Added Value from Reusing Microarray Data
Frontiers in Neurology / Oct 18, 2016
Raju, H. B., Tsinoremas, N. F., & Capobianco, E. (2016). Emerging Putative Associations between Non-Coding RNAs and Protein-Coding Genes in Neuropathic Pain: Added Value from Reusing Microarray Data. Frontiers in Neurology, 7. https://doi.org/10.3389/fneur.2016.00168
Identification of BRAF 3′UTR Isoforms in Melanoma
Journal of Investigative Dermatology / Jun 01, 2015
Marranci, A., Tuccoli, A., Vitiello, M., Mercoledi, E., Sarti, S., Lubrano, S., Evangelista, M., Fogli, A., Valdes, C., Russo, F., Monte, M. D., Caligo, M. A., Pellegrini, M., Capobianco, E., Tsinoremas, N., & Poliseno, L. (2015). Identification of BRAF 3′UTR Isoforms in Melanoma. Journal of Investigative Dermatology, 135(6), 1694–1697. https://doi.org/10.1038/jid.2015.47
State-space stochastic volatility models: A review of estimation algorithms
Applied Stochastic Models and Data Analysis / Dec 01, 1996
Capobianco, E. (1996). State-space stochastic volatility models: A review of estimation algorithms. Applied Stochastic Models and Data Analysis, 12(4), 265–279. https://doi.org/10.1002/(sici)1099-0747(199612)12:4<265::aid-asm288>3.0.co;2-n
Vitamin D Modulation of Mitochondrial
Oxidative Metabolism and
mTOR Enforces Stress
Adaptations and Anticancer Responses
JBMR Plus / Dec 01, 2021
Quigley, M., Rieger, S., Capobianco, E., Wang, Z., Zhao, H., Hewison, M., & Lisse, T. S. (2021). Vitamin D Modulation of Mitochondrial Oxidative Metabolism and <scp>mTOR</scp> Enforces Stress Adaptations and Anticancer Responses. JBMR Plus, 6(1). Portico. https://doi.org/10.1002/jbm4.10572
Ensemble inference by integrative cancer networks
Frontiers in Genetics / Mar 31, 2014
Mora, A., Taranta, M., Zaki, N., Badidi, E., Cinti, C., & Capobianco, E. (2014). Ensemble inference by integrative cancer networks. Frontiers in Genetics, 5. https://doi.org/10.3389/fgene.2014.00059
Multiscale stochastic dynamics in finance
Physica A: Statistical Mechanics and its Applications / Dec 01, 2004
Capobianco, E. (2004). Multiscale stochastic dynamics in finance. Physica A: Statistical Mechanics and Its Applications, 344(1–2), 122–127. https://doi.org/10.1016/j.physa.2004.06.100
Targeting Cancer with Epi-Drugs: A Precision Medicine Perspective
Current Pharmaceutical Biotechnology / Jun 21, 2016
Gherardini, L., Sharma, A., Capobianco, E., & Cinti, C. (2016). Targeting Cancer with Epi-Drugs: A Precision Medicine Perspective. Current Pharmaceutical Biotechnology, 17(10), 856–865. https://doi.org/10.2174/1381612822666160527154757
On Digital Therapeutics
Frontiers in Digital Humanities / Nov 10, 2015
Capobianco, E. (2015). On Digital Therapeutics. Frontiers in Digital Humanities, 2. https://doi.org/10.3389/fdigh.2015.00006
Pathway landscapes and epigenetic regulation in breast cancer and melanoma cell lines
Theoretical Biology and Medical Modelling / May 01, 2014
El Baroudi, M., La Sala, D., Cinti, C., & Capobianco, E. (2014). Pathway landscapes and epigenetic regulation in breast cancer and melanoma cell lines. Theoretical Biology and Medical Modelling, 11(S1). https://doi.org/10.1186/1742-4682-11-s1-s8
In vivo quantitation of metabolites with an incomplete model function
Measurement Science and Technology / Sep 04, 2009
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 and Technology, 20(10), 104032. https://doi.org/10.1088/0957-0233/20/10/104032
Empowering Spot Detection in 2DE Images by Wavelet Denoising
In Silico Biology / Jan 01, 2009
Soggiu, A., Marullo, O., Roncada, P., & Capobianco, E. (2009). Empowering Spot Detection in 2DE Images by Wavelet Denoising. In Silico Biology, 9(3), 125–133. https://doi.org/10.3233/isb-2009-0393
Kernel methods and flexible inference for complex stochastic dynamics
Physica A: Statistical Mechanics and its Applications / Jul 01, 2008
Capobianco, E. (2008). Kernel methods and flexible inference for complex stochastic dynamics. Physica A: Statistical Mechanics and Its Applications, 387(16–17), 4077–4098. https://doi.org/10.1016/j.physa.2008.03.003
Time-domain semi-parametric estimation based on a metabolite basis set
NMR in Biomedicine / Jan 01, 2005
Ratiney, H., Sdika, M., Coenradie, Y., Cavassila, S., Ormondt, D. van, & Graveron-Demilly, D. (2005). Time-domain semi-parametric estimation based on a metabolite basis set. NMR in Biomedicine, 18(1), 1–13. https://doi.org/10.1002/nbm.895
Wavelets
Statistical Modeling by Wavelets / Apr 19, 1999
Wavelets. (1999). Wiley Series in Probability and Statistics, 43–99. Portico. https://doi.org/10.1002/9780470317020.ch3
Role of Complex Networks for Integrating Medical Images and Radiomic Features of Intracranial Ependymoma Patients in Response to Proton Radiotherapy
Frontiers in Medicine / Jan 17, 2020
Dominietto, M., Pica, A., Safai, S., Lomax, A. J., Weber, D. C., & Capobianco, E. (2020). Role of Complex Networks for Integrating Medical Images and Radiomic Features of Intracranial Ependymoma Patients in Response to Proton Radiotherapy. Frontiers in Medicine, 6. https://doi.org/10.3389/fmed.2019.00333
Dynamic Networks in Systems Medicine
Frontiers in Genetics / Jan 01, 2012
Capobianco, E. (2012). Dynamic Networks in Systems Medicine. Frontiers in Genetics, 3. https://doi.org/10.3389/fgene.2012.00185
Expected Impacts of Connected Multimodal Imaging in Precision Oncology
Frontiers in Pharmacology / Nov 29, 2016
Dominietto, M. D., & Capobianco, E. (2016). Expected Impacts of Connected Multimodal Imaging in Precision Oncology. Frontiers in Pharmacology, 7. https://doi.org/10.3389/fphar.2016.00451
Methods to Detect Transcribed Pseudogenes: RNA-Seq Discovery Allows Learning Through Features
Methods in Molecular Biology / Jan 01, 2014
Valdes, C., & Capobianco, E. (2014). Methods to Detect Transcribed Pseudogenes: RNA-Seq Discovery Allows Learning Through Features. Pseudogenes, 157–183. https://doi.org/10.1007/978-1-4939-0835-6_11
Model validation for gene selection and regulation maps
Functional & Integrative Genomics / Dec 07, 2007
Capobianco, E. (2007). Model validation for gene selection and regulation maps. Functional & Integrative Genomics, 8(2), 87–99. https://doi.org/10.1007/s10142-007-0066-3
MINING TIME-DEPENDENT GENE FEATURES
Journal of Bioinformatics and Computational Biology / Oct 01, 2005
CAPOBIANCO, E. (2005). MINING TIME-DEPENDENT GENE FEATURES. Journal of Bioinformatics and Computational Biology, 03(05), 1191–1205. https://doi.org/10.1142/s0219720005001454
Inferring modules from human protein interactome classes
BMC Systems Biology / Jul 23, 2010
Marras, E., Travaglione, A., Chaurasia, G., Futschik, M., & Capobianco, E. (2010). Inferring modules from human protein interactome classes. BMC Systems Biology, 4(1). https://doi.org/10.1186/1752-0509-4-102
Next Generation Networks: Featuring the Potential Role of Emerging Applications in Translational Oncology
Journal of Clinical Medicine / May 11, 2019
Capobianco, E. (2019). Next Generation Networks: Featuring the Potential Role of Emerging Applications in Translational Oncology. Journal of Clinical Medicine, 8(5), 664. https://doi.org/10.3390/jcm8050664
General Practitioners Records Are Epidemiological Predictors of Comorbidities: An Analytical Cross-Sectional 10-Year Retrospective Study
Journal of Clinical Medicine / Jul 27, 2018
Cavallo, P., Pagano, S., De Santis, M., & Capobianco, E. (2018). General Practitioners Records Are Epidemiological Predictors of Comorbidities: An Analytical Cross-Sectional 10-Year Retrospective Study. Journal of Clinical Medicine, 7(8), 184. https://doi.org/10.3390/jcm7080184
Epigenetically driven network cooperativity: meta-analysis in multi-drug resistant osteosarcoma
Journal of Complex Networks / Jul 09, 2015
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, 4(2), 296–317. https://doi.org/10.1093/comnet/cnv017
Inflammation blood and tissue factors of plaque growth in an experimental model evidenced by a systems approach
Frontiers in Genetics / Apr 07, 2014
Pelosi, G., Rocchiccioli, S., Cecchettini, A., Viglione, F., Puntoni, M., Parodi, O., Capobianco, E., & Trivella, M. G. (2014). Inflammation blood and tissue factors of plaque growth in an experimental model evidenced by a systems approach. Frontiers in Genetics, 5. https://doi.org/10.3389/fgene.2014.00070
Neural networks and statistical inference: seeking robust and efficient learning
Computational Statistics & Data Analysis / Jan 01, 2000
Capobianco, E. (2000). Neural networks and statistical inference: seeking robust and efficient learning. Computational Statistics & Data Analysis, 32(3–4), 443–454. https://doi.org/10.1016/s0167-9473(99)00089-4
Time-course gene profiling and networks in demethylated retinoblastoma cell line
Oncotarget / Jun 25, 2015
Malusa, F., Taranta, M., Zaki, N., Cinti, C., & Capobianco, E. (2015). Time-course gene profiling and networks in demethylated retinoblastoma cell line. Oncotarget, 6(27), 23688–23707. https://doi.org/10.18632/oncotarget.4644
Manifold Learning in Protein Interactomes
Journal of Computational Biology / Jan 01, 2011
Marras, E., Travaglione, A., & Capobianco, E. (2011). Manifold Learning in Protein Interactomes. Journal of Computational Biology, 18(1), 81–96. https://doi.org/10.1089/cmb.2009.0258
Lineshape estimation in in vivo MR Spectroscopy without using a reference signal
2008 IEEE International Workshop on Imaging Systems and Techniques / Sep 01, 2008
Popa, E., Graveron-Demilly, D., Capobianco, E., de Beer, R., & van Ormondt, D. (2008). Lineshape estimation in in vivo MR Spectroscopy without using a reference signal. 2008 IEEE International Workshop on Imaging Systems and Techniques. https://doi.org/10.1109/ist.2008.4659992
FUNCTIONAL APPROXIMATION IN MULTISCALE COMPLEX SYSTEMS
Advances in Complex Systems / Jun 01, 2003
CAPOBIANCO, E. (2003). FUNCTIONAL APPROXIMATION IN MULTISCALE COMPLEX SYSTEMS. Advances in Complex Systems, 06(02), 177–204. https://doi.org/10.1142/s0219525903000840
Editorial: Artificial Intelligence for Precision Medicine
Frontiers in Artificial Intelligence / Jan 21, 2022
Deng, J., Hartung, T., Capobianco, E., Chen, J. Y., & Emmert-Streib, F. (2022). Editorial: Artificial Intelligence for Precision Medicine. Frontiers in Artificial Intelligence, 4. https://doi.org/10.3389/frai.2021.834645
RNA-seq analysis reveals significant transcriptome changes in huntingtin-null human neuroblastoma cells
BMC Medical Genomics / Jul 02, 2021
Bensalel, J., Xu, H., Lu, M. L., Capobianco, E., & Wei, J. (2021). RNA-seq analysis reveals significant transcriptome changes in huntingtin-null human neuroblastoma cells. BMC Medical Genomics, 14(1). https://doi.org/10.1186/s12920-021-01022-w
Use of instrumental variables in electronic health record-driven models
Statistical Methods in Medical Research / Apr 07, 2016
Salmasi, L., & Capobianco, E. (2016). Use of instrumental variables in electronic health record-driven models. Statistical Methods in Medical Research, 27(2), 608–621. https://doi.org/10.1177/0962280216641154
Precision Oncology: The Promise of Big Data and the Legacy of Small Data
Frontiers in ICT / Aug 29, 2017
Capobianco, E. (2017). Precision Oncology: The Promise of Big Data and the Legacy of Small Data. Frontiers in ICT, 4. https://doi.org/10.3389/fict.2017.00022
Entropy embedding and fluctuation analysis in genomic manifolds
Communications in Nonlinear Science and Numerical Simulation / Jun 01, 2009
Capobianco, E. (2009). Entropy embedding and fluctuation analysis in genomic manifolds. Communications in Nonlinear Science and Numerical Simulation, 14(6), 2602–2618. https://doi.org/10.1016/j.cnsns.2008.09.015
Independent component analysis and resolution pursuit with wavelet and cosine packets
Neurocomputing / Oct 01, 2002
Capobianco, E. (2002). Independent component analysis and resolution pursuit with wavelet and cosine packets. Neurocomputing, 48(1–4), 779–806. https://doi.org/10.1016/s0925-2312(01)00673-7
A unifying view of stochastic approximation, Kalman filter and backpropagation
Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing
Capobianco, E. (n.d.). A unifying view of stochastic approximation, Kalman filter and backpropagation. Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing. https://doi.org/10.1109/nnsp.1995.514882
Radiomics at a Glance: A Few Lessons Learned from Learning Approaches
Cancers / Aug 29, 2020
Capobianco, E., & Deng, J. (2020). Radiomics at a Glance: A Few Lessons Learned from Learning Approaches. Cancers, 12(9), 2453. https://doi.org/10.3390/cancers12092453
Editorial: Trends in Digital Medicine
Frontiers in Medicine / Apr 03, 2020
Capobianco, E., Iacoviello, L., de Gaetano, G., & Donati, M. B. (2020). Editorial: Trends in Digital Medicine. Frontiers in Medicine, 7. https://doi.org/10.3389/fmed.2020.00116
Imprecise Data and Their Impact on Translational Research in Medicine
Frontiers in Medicine / Mar 19, 2020
Capobianco, E. (2020). Imprecise Data and Their Impact on Translational Research in Medicine. Frontiers in Medicine, 7. https://doi.org/10.3389/fmed.2020.00082
Significant EHR Feature-Driven T2D Inference: Predictive Machine Learning and Networks
Frontiers in Big Data / Sep 27, 2019
Preo, N., & Capobianco, E. (2019). Significant EHR Feature-Driven T2D Inference: Predictive Machine Learning and Networks. Frontiers in Big Data, 2. https://doi.org/10.3389/fdata.2019.00030
Immuno-Oncology Integrative Networks: Elucidating the Influences of Osteosarcoma Phenotypes
Cancer Informatics / Jan 01, 2017
Sharma, A., & Capobianco, E. (2017). Immuno-Oncology Integrative Networks: Elucidating the Influences of Osteosarcoma Phenotypes. Cancer Informatics, 16, 117693511772169. https://doi.org/10.1177/1176935117721691
Prognostic models in coronary artery disease: Cox and network approaches
Royal Society Open Science / Feb 01, 2015
Mora, A., Sicari, R., Cortigiani, L., Carpeggiani, C., Picano, E., & Capobianco, E. (2015). Prognostic models in coronary artery disease: Cox and network approaches. Royal Society Open Science, 2(2), 140270. https://doi.org/10.1098/rsos.140270
Advances in translational biomedicine from systems approaches
Frontiers in Genetics / Apr 19, 2017
Capobianco, E., & Lió, P. (2017). Advances in translational biomedicine from systems approaches. Frontiers in Genetics, 5. https://doi.org/10.3389/fgene.2014.00273
Warehousing re-annotated cancer genes for biomarker meta-analysis
Computer Methods and Programs in Biomedicine / Jul 01, 2013
Orsini, M., Travaglione, A., & Capobianco, E. (2013). Warehousing re-annotated cancer genes for biomarker meta-analysis. Computer Methods and Programs in Biomedicine, 111(1), 166–180. https://doi.org/10.1016/j.cmpb.2013.03.010
Multiscale Characterization of Signaling Network Dynamics through Features
Statistical Applications in Genetics and Molecular Biology / Jan 20, 2011
Capobianco, E., Marras, E., & Travaglione, A. (2011). Multiscale Characterization of Signaling Network Dynamics through Features. Statistical Applications in Genetics and Molecular Biology, 10(1). https://doi.org/10.2202/1544-6115.1657
On network entropy and bio-interactome applications
Journal of Computational Science / May 01, 2011
Capobianco, E. (2011). On network entropy and bio-interactome applications. Journal of Computational Science, 2(2), 144–152. https://doi.org/10.1016/j.jocs.2010.12.008
Value of digital biomarkers in precision medicine: implications in cancer, autoimmune diseases, and COVID-19
Expert Review of Precision Medicine and Drug Development / May 13, 2021
Capobianco, E., & Meroni, P. L. (2021). Value of digital biomarkers in precision medicine: implications in cancer, autoimmune diseases, and COVID-19. Expert Review of Precision Medicine and Drug Development, 6(4), 235–238. https://doi.org/10.1080/23808993.2021.1924055
Inference From Complex Networks: Role of Symmetry and Applicability to Images
Frontiers in Applied Mathematics and Statistics / Jul 09, 2020
Capobianco, E. (2020). Inference From Complex Networks: Role of Symmetry and Applicability to Images. Frontiers in Applied Mathematics and Statistics, 6. https://doi.org/10.3389/fams.2020.00023
Ensemble Modeling Approach Targeting Heterogeneous RNA-Seq data: Application to Melanoma Pseudogenes
Scientific Reports / Dec 11, 2017
Capobianco, E., Valdes, C., Sarti, S., Jiang, Z., Poliseno, L., & Tsinoremas, N. F. (2017). Ensemble Modeling Approach Targeting Heterogeneous RNA-Seq data: Application to Melanoma Pseudogenes. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-17337-7
Editorial: Physiology in Extreme Conditions: Adaptations and Unexpected Reactions
Frontiers in Physiology / Sep 29, 2017
Trivella, M. G., Capobianco, E., & L’Abbate, A. (2017). Editorial: Physiology in Extreme Conditions: Adaptations and Unexpected Reactions. Frontiers in Physiology, 8. https://doi.org/10.3389/fphys.2017.00748
Corrigendum: Distinct Transcriptomic Features Are Associated with Transitional and Mature B-Cell Populations in the Mouse Spleen
Frontiers in Immunology / Jul 04, 2016
Kleiman, E., Salyakina, D., De Heusch, M., Hoek, K. L., Llanes, J. M., Castro, I., Wright, J. A., Clark, E. S., Dykxhoorn, D. M., Capobianco, E., Takeda, A., McCormack, R. M., Podack, E. R., Renauld, J.-C., & Khan, W. N. (2016). Corrigendum: Distinct Transcriptomic Features Are Associated with Transitional and Mature B-Cell Populations in the Mouse Spleen. Frontiers in Immunology, 7. https://doi.org/10.3389/fimmu.2016.00267
A proteomic study of microgravity cardiac effects: feature maps of label-free LC-MALDI data for differential expression analysis
Molecular BioSystems / Jan 01, 2010
Rocchiccioli, S., Congiu, E., Boccardi, C., Citti, L., Callipo, L., Laganà, 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, 6(11), 2218. https://doi.org/10.1039/c0mb00065e
ALIASING IN GENE FEATURE DETECTION BY PROJECTIVE METHODS
Journal of Bioinformatics and Computational Biology / Aug 01, 2009
CAPOBIANCO, E. (2009). ALIASING IN GENE FEATURE DETECTION BY PROJECTIVE METHODS. Journal of Bioinformatics and Computational Biology, 07(04), 685–700. https://doi.org/10.1142/s0219720009004254
Mining protein–protein interaction networks: denoising effects
Journal of Statistical Mechanics: Theory and Experiment / Jan 05, 2009
Marras, E., & Capobianco, E. (2009). Mining protein–protein interaction networks: denoising effects. Journal of Statistical Mechanics: Theory and Experiment, 2009(01), P01006. https://doi.org/10.1088/1742-5468/2009/01/p01006
Statistical Embedding in Complex Biosystems
Journal of Integrative Bioinformatics / Dec 01, 2006
Capobianco, E. (2006). Statistical Embedding in Complex Biosystems. Journal of Integrative Bioinformatics, 3(2), 90–108. https://doi.org/10.1515/jib-2006-30
On support vector machines and sparse approximation for random processes
Neurocomputing / Jan 01, 2004
Capobianco, E. (2004). On support vector machines and sparse approximation for random processes. Neurocomputing, 56, 39–60. https://doi.org/10.1016/s0925-2312(03)00370-9
Semiparametric Artificial Neural Networks
Mathematics of Neural Networks / Jan 01, 1997
Capobianco, E. (1997). Semiparametric Artificial Neural Networks. Operations Research/Computer Science Interfaces Series, 140–145. https://doi.org/10.1007/978-1-4615-6099-9_21
Multivariate probability density estimation by wavelet methods: Strong consistency and rates for stationary time series
Stochastic Processes and their Applications / May 01, 1997
Masry, E. (1997). Multivariate probability density estimation by wavelet methods: Strong consistency and rates for stationary time series. Stochastic Processes and Their Applications, 67(2), 177–193. https://doi.org/10.1016/s0304-4149(96)00005-1
Overview of triple negative breast cancer prognostic signatures in the context of data science-driven clinico-genomics research
Annals of Translational Medicine / Dec 01, 2022
Capobianco, E. (2022). Overview of triple negative breast cancer prognostic signatures in the context of data science-driven clinico-genomics research. Annals of Translational Medicine, 10(24), 1300–1300. https://doi.org/10.21037/atm-22-5477
Characterization of huntingtin interactomes and their dynamic responses in living cells by proximity proteomics
Journal of Neurochemistry / Nov 27, 2022
Xu, H., Bensalel, J., Raju, S., Capobianco, E., Lu, M. L., & Wei, J. (2022). Characterization of huntingtin interactomes and their dynamic responses in living cells by proximity proteomics. Journal of Neurochemistry, 164(4), 512–528. Portico. https://doi.org/10.1111/jnc.15726
PTS is activated by ATF4 and promotes lung adenocarcinoma development via the Wnt pathway
Translational Lung Cancer Research / Sep 01, 2022
Ma, W., Wang, C., Li, R., Han, Z., Jiang, Y., Zhang, X., Divisi, D., Capobianco, E., Zhang, L., & Dong, W. (2022). PTS is activated by ATF4 and promotes lung adenocarcinoma development via the Wnt pathway. Translational Lung Cancer Research, 11(9), 1912–1925. https://doi.org/10.21037/tlcr-22-593
Impaired Restoration of Global Protein Synthesis Contributes to Increased Vulnerability to Acute ER Stress Recovery in Huntington’s Disease
Cellular and Molecular Neurobiology / Aug 04, 2021
Xu, H., Bensalel, J., Capobianco, E., Lu, M. L., & Wei, J. (2021). Impaired Restoration of Global Protein Synthesis Contributes to Increased Vulnerability to Acute ER Stress Recovery in Huntington’s Disease. Cellular and Molecular Neurobiology, 42(8), 2757–2771. https://doi.org/10.1007/s10571-021-01137-9
Network assessment of demethylation treatment in melanoma: Differential transcriptome-methylome and antigen profile signatures
PLOS ONE / Nov 28, 2018
Jiang, Z., Cinti, C., Taranta, M., Mattioli, E., Schena, E., Singh, S., Khurana, R., Lattanzi, G., Tsinoremas, N. F., & Capobianco, E. (2018). Network assessment of demethylation treatment in melanoma: Differential transcriptome-methylome and antigen profile signatures. PLOS ONE, 13(11), e0206686. https://doi.org/10.1371/journal.pone.0206686
Protein networks tomography
Systems Biomedicine / Jul 01, 2013
Capobianco, E. (2013). Protein networks tomography. Systems Biomedicine, 1(3), 161–178. https://doi.org/10.4161/sysb.25607
Advances in Human Protein Interactome Inference
Contributions to Statistics / Jan 01, 2008
Capobianco, E., & Marras, E. (2008). Advances in Human Protein Interactome Inference. Functional and Operatorial Statistics, 89–94. https://doi.org/10.1007/978-3-7908-2062-1_15
Independent Component Analysis
Analysis of Multivariate and High-Dimensional Data / Dec 02, 2013
Independent Component Analysis. (2013). Analysis of Multivariate and High-Dimensional Data, 305–348. https://doi.org/10.1017/cbo9781139025805.013
High-dimensional role of AI and machine learning in cancer research
British Journal of Cancer / Jan 10, 2022
Capobianco, E. (2022). High-dimensional role of AI and machine learning in cancer research. British Journal of Cancer, 126(4), 523–532. https://doi.org/10.1038/s41416-021-01689-z
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