An Optimal Time for Treatment-Predicting Circadian Time by Machine Learning and Mathematical Modelling.
chronotherapy in cancer
circadian network
circadian time prediction
computational methods for rhythmicity analysis
core-clock ODE models
harmonic regression
machine learning
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
23 Oct 2020
23 Oct 2020
Historique:
received:
01
09
2020
revised:
15
10
2020
accepted:
20
10
2020
entrez:
29
10
2020
pubmed:
30
10
2020
medline:
30
10
2020
Statut:
epublish
Résumé
Tailoring medical interventions to a particular patient and pathology has been termed personalized medicine. The outcome of cancer treatments is improved when the intervention is timed in accordance with the patient's internal time. Yet, one challenge of personalized medicine is how to consider the biological time of the patient. Prerequisite for this so-called chronotherapy is an accurate characterization of the internal circadian time of the patient. As an alternative to time-consuming measurements in a sleep-laboratory, recent studies in chronobiology predict circadian time by applying machine learning approaches and mathematical modelling to easier accessible observables such as gene expression. Embedding these results into the mathematical dynamics between clock and cancer in mammals, we review the precision of predictions and the potential usage with respect to cancer treatment and discuss whether the patient's internal time and circadian observables, may provide an additional indication for individualized treatment timing. Besides the health improvement, timing treatment may imply financial advantages, by ameliorating side effects of treatments, thus reducing costs. Summarizing the advances of recent years, this review brings together the current clinical standard for measuring biological time, the general assessment of circadian rhythmicity, the usage of rhythmic variables to predict biological time and models of circadian rhythmicity.
Identifiants
pubmed: 33114254
pii: cancers12113103
doi: 10.3390/cancers12113103
pmc: PMC7690897
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Subventions
Organisme : German Federal Ministry of Education and Research (BMBF)-eBio-CIRSPLICE
ID : FKZ031A316
Organisme : Dr. Rolf M. Schwiete Stiftung
ID : 07/2019
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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