Patrizia Boracchi

Publication Activity (10 Years)

Years Active: 2002-2018
Publications (10 Years): 1

Top Topics

Piecewise Linear

Clinical Guidelines

Artificial Neural Networks

Top Venues

Publications

Marco Fornili, Patrizia Boracchi, Federico Ambrogi, Elia Biganzoli
Modeling the covariates effects on the hazard function by piecewise exponential artificial neural networks: an application to a controlled clinical trial on renal carcinoma. BMC Bioinform. (7) (2018)
Giuseppe Marano, Patrizia Boracchi, Elia M. Biganzoli
Estimation of a Piecewise Exponential Model by Bayesian P-splines Techniques for Prognostic Assessment and Prediction. CIBB (2014)
Marco Fornili, Federico Ambrogi, Patrizia Boracchi, Elia Biganzoli
Piecewise Exponential Artificial Neural Networks (PEANN) for Modeling Hazard Function with Right Censored Data. CIBB (2013)
Daniele Soria, Jonathan M. Garibaldi, Federico Ambrogi, Andrew R. Green, Des Powe, Emad A. Rakha, R. Douglas Macmillan, Roger W. Blamey, Graham R. Ball, Paulo J. G. Lisboa, Terence A. Etchells, Patrizia Boracchi, Elia Biganzoli, Ian O. Ellis
A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients. Comput. Biol. Medicine 40 (3) (2010)
Niccoló Bassani, Federico Ambrogi, Danila Coradini, Patrizia Boracchi, Elia Biganzoli
Complementary use of cluster analysis and biplots to discover and validate patterns of gene expression in microarray data. IJCNN (2010)
Ilaria Ardoino, Federico Ambrogi, Chris Bajdik, Paulo J. G. Lisboa, Elia Biganzoli, Patrizia Boracchi
Flexible parametric modelling of the hazard function in breast cancer studies. IJCNN (2010)
Nicola Lama, Patrizia Boracchi, Elia Biganzoli
Exploration of distributional models for a novel intensity-dependent normalization procedure in censored gene expression data. Comput. Stat. Data Anal. 53 (5) (2009)
Federico Ambrogi, Elia Biganzoli, Patrizia Boracchi
Estimating crude cumulative incidences through multinomial logit regression on discrete cause-specific hazards. Comput. Stat. Data Anal. 53 (7) (2009)
Elia Biganzoli, Federico Ambrogi, Patrizia Boracchi
Partial logistic artificial neural networks (PLANN) for flexible modeling of censored survival data. IJCNN (2009)
Daniele Soria, Jonathan M. Garibaldi, Federico Ambrogi, Patrizia Boracchi, Elena Raimondi, Elia Biganzoli
Cancer profiles by affinity propagation. Int. J. Knowl. Eng. Soft Data Paradigms 1 (3) (2009)
Paulo J. G. Lisboa, Terence A. Etchells, Ian H. Jarman, Corneliu T. C. Arsene, Min S. Hane Aung, Antonio Eleuteri, Azzam Fouad George Taktak, Federico Ambrogi, Patrizia Boracchi, Elia Biganzoli
Partial Logistic Artificial Neural Network for Competing Risks Regularized With Automatic Relevance Determination. IEEE Trans. Neural Networks 20 (9) (2009)
Federico Ambrogi, Elena Raimondi, Daniele Soria, Patrizia Boracchi, Elia Biganzoli
Cancer Profiles by Affinity Propagation. ICMLA (2008)
Federico Ambrogi, Nicola Lama, Patrizia Boracchi, Elia Biganzoli
Selection of artificial neural network models for survival analysis with Genetic Algorithms. Comput. Stat. Data Anal. 52 (1) (2007)
Azzam F. Taktak, Laura Antolini, Min S. Hane Aung, Patrizia Boracchi, Ian Campbell, Bertil E. Damato, Emmanuel C. Ifeachor, Nicola Lama, Paulo J. G. Lisboa, Christian Setzkorn, Viktoriya Stalbovskaya, Elia Biganzoli
Double-blind evaluation and benchmarking of survival models in a multi-centre study. Comput. Biol. Medicine 37 (8) (2007)
Elia Biganzoli, Patrizia Boracchi, Federico Ambrogi, Ettore Marubini
Artificial neural network for the joint modelling of discrete cause-specific hazards. Artif. Intell. Medicine 37 (2) (2006)
Federico Ambrogi, Elia Biganzoli, Patrizia Boracchi
Multiple correspondence analysis in S-PLUS. Comput. Methods Programs Biomed. 79 (2) (2005)
Patrizia Boracchi, Elia Biganzoli, Ettore Marubini
Joint modelling of cause-specific hazard functions with cubic splines: an application to a large series of breast cancer patients. Comput. Stat. Data Anal. 42 (1-2) (2003)
Elia Biganzoli, Patrizia Boracchi, Ettore Marubini
A general framework for neural network models on censored survival data. Neural Networks 15 (1) (2002)