scholarly journals Quo vadis artificial intelligence and personalized medicine?

2021 ◽  
Author(s):  
Antonio J Banegas-Luna ◽  
Miguel Carmena-Bargueño ◽  
Horacio Pérez-Sánchez
Keyword(s):  
Artificial Intelligence ◽  
Personalized Medicine ◽  
Quo Vadis ◽  
And Personalized Medicine

scholarly journals Health and Artificial Intelligence in the context of COVID-19 and beyond

2021 ◽  
Vol 7 (1) ◽  
pp. 33-49
Author(s):  
Susana Navas Navarro
Keyword(s):  
Artificial Intelligence ◽  
Personalized Medicine ◽  
Digital Health ◽  
Access Technology ◽  
Fourth Section ◽  
The Core ◽  
Concept Of Health ◽  
Preventive And Personalized Medicine ◽  
The Relationship ◽  
And Personalized Medicine

This article introduces the main relevant aspects of ehealth or “digital health.” In this regard, by way of an introduction, the concept of health is addressed. In the second section, the core of this article, different aspects regarding the relationship between health and technology (HealthTech) are highlighted. Next, the importance of technology in order to deliver a preventive and personalized medicine, tailor-made to each patient, is addressed. Then, in the fourth section, some discriminatory situations that may arise due to not being able to access technology are discussed. Finally, I make some remarks regarding the so-called “Internet of Bodies".

Artificial Intelligence-Driven Designer Drug Combinations: From Drug Development to Personalized Medicine

2018 ◽  
Vol 24 (1) ◽  
pp. 124-125
Author(s):  
Masturah Bte Mohd Abdul Rashid ◽  
Edward Kai-Hua Chow
Keyword(s):  
Artificial Intelligence ◽  
Personalized Medicine ◽  
Translational Medicine ◽  
Molecular Mechanisms ◽  
Disease Model ◽  
Drug Combinations ◽  
Designer Drug ◽  
Great Promise ◽  
Specific Patient ◽  
And Personalized Medicine

Artificial intelligence holds great promise in transforming how drugs are designed and patients are treated. In a study recently published in Science Translational Medicine, a unique artificial intelligence platform makes efficient use of small experimental datasets to design new drug combinations as well as identify the best drug combinations for specific patient samples. This quadratic phenotypic optimization platform (QPOP) does not rely on previous assumptions of molecular mechanisms of disease, but rather uses system-specific experimental data to determine the best drug combinations for a specific disease model or a patient sample. In this commentary, we explore how QPOP was applied toward multiple myeloma in the study. We also discuss how this study demonstrates the potential for applications of QPOP toward improving therapeutic regimen design and personalized medicine.

Artificial Intelligence for epigenetics: towards personalized medicine

2020 ◽  
Vol 27 ◽  
Author(s):  
Giulia De Riso ◽  
Sergio Cocozza
Keyword(s):  
Biological Sciences ◽  
Artificial Intelligence ◽  
Personalized Medicine ◽  
Molecular Mechanisms ◽  
Genomic Sequence ◽  
Health Care Interventions ◽  
Genome Wide ◽  
Genetic And Environmental Factors ◽  
Opening Up ◽  
Omic Data

: Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms, that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual. Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, highthroughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues. Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.

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