Nat BME: Definition and prediction of multiple diagnoses of neurodegenerative diseases.

Pathological protein aggregation is a major disease process of neurodegenerative disease.
over time, these protein aggregates may spread along large white matter fibers, causing dysfunction in distant regions.
their toxicity is thought to be mediated in part by the inflammatory system.
different neurodegenerative syndromes are characterized by the aggregation of specific proteins, and typical Alzheimer's disease includes amyloid-beta and related proteins.
Parkinson's disease involves alpha-synthetic nucleosides, and terabyte dementia may involve a transactive reaction dna binding protein 43 kDa (tdp-43).
numerous in vitro and animal studies have shown that these proteins interact to produce unique associated dysfunctions. There are many mechanisms
molecular pathology that cause cell dysfunction.
(i.e., vascular lesions, glial hyperplasia, nerve cell death, or signal dysfunction) and cell dysfunction may be more reflective of cognitive dysfunction than mere plaque burdens.
the ever-decreasing cost of computational hardware and advanced computational methods, such as machine learning, provides a tool for parsing heterogeneity by defining disease subtypes of new data algorithms.
: All the data were obtained through the Comprehensive Neurodegenerative Diseases Database, sponsored by the Center for Neurodegenerative Diseases at the University of Pennsylvania Hospital.
a team of specialist neuropathologists assessed the extent of 6 molecular pathological features (amyloid-beta, neuroplaque, palladium, alpha-syNucin, tdp-43 and unonine) and 3 cell pathology characteristics (vascular lesions, neuronal loss, and gliophilias) during the autopsy of 1659 patients.
all patients give informed consent under an agreement approved by the University of Pennsylvania's Institutional Review Board.
all patients are clinically diagnosed by their doctor prior to the autopsy.
use Luminex to determine the level of analysis in cerebrospinal fluid.
traditional definitions of neurodegenerative diseases usually account for only one or two proteins.
here, the authors seek to group patients in an unsupervised way.
in the n-p matrix P, p is 98 available pathological features, N is our sample of 895 experimental subjects.
a matrix was constructed in order to cluster the sequential semi-quantitative pathology score.
R its element Rij is equal to the P of multistring-related columns i and j, corresponding to the patient's pathological scoring vector.
to demonstrate the practical application of multivariate biomarker models in the body, data available in the body were used to predict whether patients met the criteria for specific neurodegenerative diseases, or disease clusters classified as specific data algorithms.
results: Unsupervised learning can be applied to similarity matrices based on any set of characteristics associated with diseases that overlap with pathophysiological patterns, such as in multifactorial diseases such as epilepsy, vascular disease, or cancer.
essential to this process is the compilation of large multi-modal and multi-site data sets covering a wide range of diagnostics, phenotypes and genotypes.
In addition to potential clinical applications for biomarker-based histopathological syndrome prediction, this work can also serve as a model for identifying data-driven cross-diagnostic disease subtypes in any field using unsupervised methods.
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