Data-driven computing provides empirical facts that help turn data into knowledge. Early-stage neurodegenerative diseases still pose challenges in daily clinical practice, and data-driven computing helps explore data to gain insightful understanding of brain diseases and overcome challenges in clinical practice.
Biosignature based methods for diagnosis and treatment of neurodegenerative diseases are mostly clinical and include neuropsychological testing and serological testing, e.g., blood counts, thyroid-stimulating hormone, vitamin B-12, folic acid, and MRI. Molecular traits, such as Amyloid Beta and Phosphorylated Tau, are associated with neurodegenerative diseases such as Alzheimer's disease. Advances in data-driven computational modeling initiatives aim to establish reliable and robust empirical causality to behavior through comprehensive biosignature-based analyzes. Thus, there is an urgent need to develop methods, computational tools, and software to improve our understanding of biological data, such as artificial intelligent methods that incorporate a large, high-dimensional dataset, are fully annotated and validated against the literature, and have high explanatory accuracy in neurodegenerative diseases.
This Research Topic aims to explore reliable biosignatures for diagnosis and treatment through advances in data-driven computation involving wet and dry laboratory techniques, including in vivo in vitro, large data sets, genome-wide association, brain imaging, inhibitor, alternative medicine, nutrient(s) or fixed mechanisms, and state of the art in neurodegenerative diseases.
Topics of interest include:
- Advances in laboratory experiments using data-driven computation in neurodegeneration;
- Innovations in molecular imaging through data-driven approaches;
- Innovations in large data sets proposing data-driven validated mechanisms in neurodegenerative diseases;
- Data-driven studies for pathological analysis of neurodegenerative diseases;
- Genetic data-driven computation and clinical biosignature-based development tools for neurodegenerative diseases or neurocutaneous syndromes;
- Data-driven computational technologies, such as Artificial Intelligence or Data Mining, used in neurodegenerative diseases.
Data-driven computing provides empirical facts that help turn data into knowledge. Early-stage neurodegenerative diseases still pose challenges in daily clinical practice, and data-driven computing helps explore data to gain insightful understanding of brain diseases and overcome challenges in clinical practice.
Biosignature based methods for diagnosis and treatment of neurodegenerative diseases are mostly clinical and include neuropsychological testing and serological testing, e.g., blood counts, thyroid-stimulating hormone, vitamin B-12, folic acid, and MRI. Molecular traits, such as Amyloid Beta and Phosphorylated Tau, are associated with neurodegenerative diseases such as Alzheimer's disease. Advances in data-driven computational modeling initiatives aim to establish reliable and robust empirical causality to behavior through comprehensive biosignature-based analyzes. Thus, there is an urgent need to develop methods, computational tools, and software to improve our understanding of biological data, such as artificial intelligent methods that incorporate a large, high-dimensional dataset, are fully annotated and validated against the literature, and have high explanatory accuracy in neurodegenerative diseases.
This Research Topic aims to explore reliable biosignatures for diagnosis and treatment through advances in data-driven computation involving wet and dry laboratory techniques, including in vivo in vitro, large data sets, genome-wide association, brain imaging, inhibitor, alternative medicine, nutrient(s) or fixed mechanisms, and state of the art in neurodegenerative diseases.
Topics of interest include:
- Advances in laboratory experiments using data-driven computation in neurodegeneration;
- Innovations in molecular imaging through data-driven approaches;
- Innovations in large data sets proposing data-driven validated mechanisms in neurodegenerative diseases;
- Data-driven studies for pathological analysis of neurodegenerative diseases;
- Genetic data-driven computation and clinical biosignature-based development tools for neurodegenerative diseases or neurocutaneous syndromes;
- Data-driven computational technologies, such as Artificial Intelligence or Data Mining, used in neurodegenerative diseases.