Digital twins to personalize medicine

Paper

Abstract

Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.

Analysis

Paper intelligence

Summary

This paper proposes using Digital Twins, high-resolution computational models of individual patients, to process vast amounts of data for personalized medicine, specifically to identify optimal drug treatments.

Key findings

Digital Twins can integrate and process large datasets required for personalized medicine.
Digital Twins enable computational testing of thousands of drugs on patient models.
This approach aims to find the optimal drug for an individual patient.

Methods

Construction of high-resolution Digital Twins representing individual patients.
Computational treatment of patient Digital Twins with thousands of drugs.

Applications

Personalized medicine
Drug optimization
Patient-specific treatment planning

Confidence100%

Discovery

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Source record

Publication metadata

Authors: Bergþór Björnsson; Carl Borrebaeck; Nils Elander; Thomas Gasslander; Danuta R. Gawel; Mika Gustafsson; Rebecka Jörnsten; Eunjung Lee; Xinxiu Li; Sandra Lilja; David Martínez-Enguita; Andreas Matussek; Per Sandström; Samuel Schäfer; Margaretha Stenmarker; Xiao‐Feng Sun; Oleg Sysoev; Huan Zhang; Mikael Benson
Published: Dec 31, 2019
Journal or venue: Genome Medicine
DOI: 10.1186/s13073-019-0701-3
Paper type: journal article
Source: Openalex

DOI OpenAlex