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Michela Carlotta Massi, PhD
Postdoc Researcher




AFFILIATIONS



PREVIOUS AFFILIATIONS

Center for Health Data Science (CHDS)

Human Technopole

Health Data Science Centre, Di Angelantonio Group

PhD in Data Analytics and Decision Sciences, Politecnico di Milano​

MOX Lab for Modeling and Scientific Computing

Dept. of Mathematics, Politecnico di Milano

My research focuses on developing effective methodologies to represent highly complex genomic and medical data,

to enhance and complement interpretable and robust statistical approaches to classification, regression and survival modelling

to personalize treatment decisions within the precision medicine framework.

RECENT ARTICLES

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Massi, M. C., Dominoni L., Ieva, F., Fiorito G. (2022) 

 

A Deep Survival EWAS approach estimating risk profile based on pre-diagnostic DNA methylation: an application to Breast Cancer time to diagnosis

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PLOS Computational Biology

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Massi, M. C., Ieva, F., Gasperoni, F., & Paganoni, A. M. (2021).

 

Feature Selection for Imbalanced Data with Deep Sparse Autoencoders Ensemble

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Statistical Analysis and Data Mining: the ASA Data Science Journal

Franco N.R., Massi M.C., Ieva F. et al. (2021)

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Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity

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Radiotherapy and Oncology

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Massi, M.C., Gasperoni, F., Ieva, F., Paganoni, A.m., Zunino, P., Manzoni, A., Franco, N.r., Et Al. (2020)

 

A deep learning approach validates genetic risk factors for late toxicity after prostate cancer radiotherapy in a REQUITE multinational cohort

 

Frontiers in Oncology

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Massi, M.C., Ieva, F. (2021)

 

Learning Signal Representations for EEG Cross-subject Channel Selection and Trial Classification 

 

IEEE International Workshop on Machine Learning for Signal Processing

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Manduchi, L., Marcinkeviks, R., Massi, M.C. et al. (2022)

 

A Deep Variational Approach to Clustering Survival Data

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10th International Conference on Learning Representations, ICLR 2022

RELEVANT PREPRINTS

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Massi, M. C., Franco, N.R. et al (2022) 

 

Learning High-Order Interactions for Polygenic Risk Scoring

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