Complete populations of virtual patients forin silicoclinical trials
Author(s) -
Stefano Sinisi,
Vadim Alimguzhin,
Toni Mancini,
Enrico Tronci,
Brigitte Leeners
Publication year - 2020
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btaa1026
Subject(s) - pairwise comparison , representativeness heuristic , computer science , population , granularity , machine learning , data mining , statistics , artificial intelligence , medicine , mathematics , environmental health , operating system
Model-based approaches to safety and efficacy assessment of pharmacological drugs, treatment strategies or medical devices (In Silico Clinical Trial, ISCT) aim to decrease time and cost for the needed experimentations, reduce animal and human testing, and enable precision medicine. Unfortunately, in presence of non-identifiable models (e.g. reaction networks), parameter estimation is not enough to generate complete populations of Virtual Patients (VPs), i.e. populations guaranteed to show the entire spectrum of model behaviours (phenotypes), thus ensuring representativeness of the trial.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom