Détail du document
Identifiant

oai:arXiv.org:2312.02248

Sujet
Quantitative Biology - Quantitativ... Computer Science - Machine Learnin...
Auteur
Krix, Sophia Wilczynski, Ella Falgàs, Neus Sánchez-Valle, Raquel Yoles, Eti Nevo, Uri Baruch, Kuti Fröhlich, Holger
Catégorie

Computer Science

Année

2023

Date de référencement

13/12/2023

Mots clés
biomarkers machine approaches system methods learning alzheimer modeling disease
Métrique

Résumé

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics.

Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage.

Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease.

In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system.

With the help of machine learning algorithms and mechanistic modeling approaches, such as agent-based modeling, an in-depth analysis of the simulation of cell dynamics is possible as well as of high-dimensional omics resources indicative of pathway signaling changes.

Here, we give a background on advances in research on brain-immune system cross-talk in Alzheimer's disease and review recent machine learning and mechanistic modeling approaches which leverage modern omics technologies for blood-based immune system-related biomarker discovery.

Krix, Sophia,Wilczynski, Ella,Falgàs, Neus,Sánchez-Valle, Raquel,Yoles, Eti,Nevo, Uri,Baruch, Kuti,Fröhlich, Holger, 2023, Towards early diagnosis of Alzheimer's disease: Advances in immune-related blood biomarkers and computational modeling approaches

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