MEDICA Project

@article{PERNICE2023104546,

title = {A new computational workflow to guide personalized drug therapy},

author = {Simone Pernice and Alessandro Maglione and Dora Tortarolo and Roberta Sirovich and Marinella Clerico and Simona Rolla and Marco Beccuti and Francesca Cordero},

url = {https://www.sciencedirect.com/science/article/pii/S1532046423002678},

doi = {https://doi.org/10.1016/j.jbi.2023.104546},

issn = {1532-0464},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Journal of Biomedical Informatics},

volume = {148},

pages = {104546},

abstract = {Objective: 

Computational models are at the forefront of the pursuit of personalized medicine thanks to their descriptive and predictive abilities. In the presence of complex and heterogeneous data, patient stratification is a prerequisite for effective precision medicine, since disease development is often driven by individual variability and unpredictable environmental events. Herein, we present GreatNectorworkflow as a valuable tool for (i) the analysis and clustering of patient-derived longitudinal data, and (ii) the simulation of the resulting model of patient-specific disease dynamics. 

Methods: 

GreatNectoris designed by combining an analytic strategy composed of CONNECTOR, a data-driven framework for the inspection of longitudinal data, and an unsupervised methodology to stratify the subjects with GreatMod, a quantitative modeling framework based on the Petri Net formalism and its generalizations. 

Results: 

To illustrate GreatNectorcapabilities, we exploited longitudinal data of four immune cell populations collected from Multiple Sclerosis patients. Our main results report that the T-cell dynamics after alemtuzumab treatment separate non-responders versus responders patients, and the patients in the non-responders group are characterized by an increase of the Th17 concentration around 36 months. 

Conclusion: 

GreatNectoranalysis was able to stratify individual patients into three model meta-patients whose dynamics suggested insight into patient-tailored interventions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}