AIMC Topic: Unsupervised Machine Learning

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Heterogeneity in Blood Biomarker Trajectories After Mild TBI Revealed by Unsupervised Learning.

IEEE/ACM transactions on computational biology and bioinformatics Jun 3, 2022
Concussions, also known as mild traumatic brain injury (mTBI), are a growing health challenge. Approximately four million concussions are diagnosed annually in the United States. Concussion is a heterogeneous disorder in causation, symptoms, and outc...
Unsupervised Machine Learning Brain Concussion Humans Biomarkers
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Identification and epidemiological characterization of Type-2 diabetes sub-population using an unsupervised machine learning approach.

Nutrition & diabetes May 27, 2022
BACKGROUND: Studies on Type-2 Diabetes Mellitus (T2DM) have revealed heterogeneous sub-populations in terms of underlying pathologies. However, the identification of sub-populations in epidemiological datasets remains unexplored. We here focus on the...
Obesity Unsupervised Machine Learning Diabetes Mellitus, Type 2 India Humans Diet
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Classification of antiseizure drugs in cultured neuronal networks using multielectrode arrays and unsupervised learning.

Epilepsia May 10, 2022
OBJECTIVE: Antiseizure drugs (ASDs) modulate synaptic and ion channel function to prevent abnormal hypersynchronous or excitatory activity arising in neuronal networks, but the relationship between ASDs with respect to their impact on network activit...
Receptors, GABA Sodium Channel Blockers Neurons gamma-Aminobutyric Acid Unsupervised Machine Learning
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Evolution and dispersal of mitochondrial DNA haplogroup U5 in Northern Europe: insights from an unsupervised learning approach to phylogeography.

BMC genomics May 7, 2022
BACKGROUND: We combined an unsupervised learning methodology for analyzing mitogenome sequences with maximum likelihood (ML) phylogenetics to make detailed inferences about the evolution and diversification of mitochondrial DNA (mtDNA) haplogroup U5,...
Europe Evolution, Molecular Humans Phylogeny Haplotypes Phylogeography Genetics, Population Bayes Theorem DNA, Mitochondrial Unsupervised Machine Learning
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Capturing surface complementarity in proteins using unsupervised learning and robust curvature measure.

Proteins Apr 23, 2022
The structure of a protein plays a pivotal role in determining its function. Often, the protein surface's shape and curvature dictate its nature of interaction with other proteins and biomolecules. However, marked by corrugations and roughness, a pro...
Models, Molecular Ligands Proteins Unsupervised Machine Learning Algorithms
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An unsupervised machine learning approach to evaluate sports facilities condition in primary school.

PloS one Apr 20, 2022
Sports facilities have been acknowledged as one of the crucial environmental factors for children's physical education, physical fitness, and participation in physical activity. Finding a solution for the effective and objective evaluation of the con...
Unsupervised Machine Learning Humans Physical Fitness Exercise Schools Physical Education and Training Child
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An analysis framework for clustering algorithm selection with applications to spectroscopy.

PloS one Mar 31, 2022
Cluster analysis is a valuable unsupervised machine learning technique that is applied in a multitude of domains to identify similarities or clusters in unlabelled data. However, its performance is dependent of the characteristics of the data it is b...
Algorithms Cluster Analysis Unsupervised Machine Learning Machine Learning Spectrum Analysis
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Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems.

Biointerphases Mar 28, 2022
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormo...
Machine Learning Algorithms Unsupervised Machine Learning Multivariate Analysis Spectrometry, Mass, Secondary Ion
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Multimodal driver state modeling through unsupervised learning.

Accident; analysis and prevention Mar 24, 2022
Naturalistic driving data (NDD) can help understand drivers' reactions to each driving scenario and provide personalized context to driving behavior. However, NDD requires a high amount of manual labor to label certain driver's state and behavioral p...
Humans Unsupervised Machine Learning Bayes Theorem Accidents, Traffic Heart Rate Automobile Driving
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Bayesian machine learning analysis of single-molecule fluorescence colocalization images.

eLife Mar 23, 2022
Multi-wavelength single-molecule fluorescence colocalization (CoSMoS) methods allow elucidation of complex biochemical reaction mechanisms. However, analysis of CoSMoS data is intrinsically challenging because of low image signal-to-noise ratios, non...
Image Processing, Computer-Assisted Unsupervised Machine Learning Reproducibility of Results Bayes Theorem Fluorescence Microscopy, Fluorescence
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