AIMC Topic: Unsupervised Machine Learning

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Phenotypes of sickle cell intensive care admissions: an unsupervised machine learning approach in a single-center retrospective cohort.

Annals of hematology
Sickle cell disease (SCD) is associated with multiple known complications and increased mortality. This study aims to further understand the profile of intensive care unit (ICU) admissions of SCD patients. In this single-center retrospective cohort (...

Informed Chemical Classification of Organophosphorus Compounds via Unsupervised Machine Learning of X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy.

The journal of physical chemistry. A
We analyze an ensemble of organophosphorus compounds to form an unbiased characterization of the information encoded in their X-ray absorption near-edge structure (XANES) and valence-to-core X-ray emission spectra (VtC-XES). Data-driven emergence of ...

Unsupervised learning methods for efficient geographic clustering and identification of disease disparities with applications to county-level colorectal cancer incidence in California.

Health care management science
Many public health policymaking questions involve data subsets representing application-specific attributes and geographic location. We develop and evaluate standard and tailored techniques for clustering via unsupervised learning (UL) algorithms on ...

Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm.

Scanning
Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural network denoising SRS photos: hyperspectral resolution enhancement and denoising one hyperspectral picture is enough to teach unsupervised method. An intuitive chemic...

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

IEEE/ACM transactions on computational biology and bioinformatics
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...

Identification and epidemiological characterization of Type-2 diabetes sub-population using an unsupervised machine learning approach.

Nutrition & diabetes
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...

Classification of antiseizure drugs in cultured neuronal networks using multielectrode arrays and unsupervised learning.

Epilepsia
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...

Evolution and dispersal of mitochondrial DNA haplogroup U5 in Northern Europe: insights from an unsupervised learning approach to phylogeography.

BMC genomics
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,...

Capturing surface complementarity in proteins using unsupervised learning and robust curvature measure.

Proteins
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...