Alzheimer's disease
In this Seminar, we highlight the main developments in the field of Alzheimer's disease. The
most recent data indicate that, by 2050, the prevalence of dementia will double in Europe …
most recent data indicate that, by 2050, the prevalence of dementia will double in Europe …
Artificial intelligence for Alzheimer's disease: promise or challenge?
Decades of experimental and clinical research have contributed to unraveling many
mechanisms in the pathogenesis of Alzheimer's disease (AD), but the puzzle is still …
mechanisms in the pathogenesis of Alzheimer's disease (AD), but the puzzle is still …
Integrative metabolomics‐genomics approach reveals key metabolic pathways and regulators of Alzheimer's disease
Metabolites, the biochemical products of the cellular process, can be used to measure
alterations in biochemical pathways related to the pathogenesis of Alzheimer's disease …
alterations in biochemical pathways related to the pathogenesis of Alzheimer's disease …
Machine learning and novel biomarkers for the diagnosis of Alzheimer's disease
Background: Alzheimer's disease (AD) is a complex and severe neurodegenerative disease
that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on …
that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on …
An integrative multi-omics approach reveals new central nervous system pathway alterations in Alzheimer's disease
Background Multiple pathophysiological processes have been described in Alzheimer's
disease (AD). Their inter-individual variations, complex interrelations, and relevance for …
disease (AD). Their inter-individual variations, complex interrelations, and relevance for …
Prediction model of dementia risk based on XGBoost using derived variable extraction and hyper parameter optimization
With the development of healthcare technologies, the elderly population has grown and
therefore populating ageing has emerged as a social issue. It is a cause of rise in patients …
therefore populating ageing has emerged as a social issue. It is a cause of rise in patients …
Deep learning meets metabolomics: a methodological perspective
P Sen, S Lamichhane, VB Mathema… - Briefings in …, 2021 - academic.oup.com
Deep learning (DL), an emerging area of investigation in the fields of machine learning and
artificial intelligence, has markedly advanced over the past years. DL techniques are being …
artificial intelligence, has markedly advanced over the past years. DL techniques are being …
Computational models for clinical applications in personalized medicine—guidelines and recommendations for data integration and model validation
CB Collin, T Gebhardt, M Golebiewski… - Journal of personalized …, 2022 - mdpi.com
The future development of personalized medicine depends on a vast exchange of data from
different sources, as well as harmonized integrative analysis of large-scale clinical health …
different sources, as well as harmonized integrative analysis of large-scale clinical health …
AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications
LM Petrick, N Shomron - Cell Reports Physical Science, 2022 - cell.com
Metabolomics describes a high-throughput approach for measuring a repertoire of
metabolites and small molecules in biological samples. One utility of untargeted …
metabolites and small molecules in biological samples. One utility of untargeted …
Early detection of Alzheimer's disease with blood plasma proteins using support vector machines
The successful development of amyloid-based biomarkers and tests for Alzheimer's disease
(AD) represents an important milestone in AD diagnosis. However, two major limitations …
(AD) represents an important milestone in AD diagnosis. However, two major limitations …