AIMC Topic: Unsupervised Machine Learning

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Anomaly Detection in Retinal OCT Images With Deep Learning-Based Knowledge Distillation.

Translational vision science & technology Mar 3, 2025
PURPOSE: The purpose of this study was to develop a robust and general purpose artificial intelligence (AI) system that allows the identification of retinal optical coherence tomography (OCT) volumes with pathomorphological manifestations not present...
Retinal Diseases Macula Lutea Unsupervised Machine Learning Tomography, Optical Coherence Humans Deep Learning Image Processing, Computer-Assisted
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Identification of Depression Subtypes in Parkinson's Disease Patients via Structural MRI Whole-Brain Radiomics: An Unsupervised Machine Learning Study.

CNS neuroscience & therapeutics Feb 1, 2025
OBJECTIVE: Current clinical evaluation may tend to lack precision in detecting depression in Parkinson's disease (DPD). Radiomics features have gradually shown potential as auxiliary diagnostic tools in identifying and distinguishing different subtyp...
Humans Brain Female Male Aged Magnetic Resonance Imaging Middle Aged Radiomics Parkinson Disease Depression Unsupervised Machine Learning
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CSI-GEP: A GPU-based unsupervised machine learning approach for recovering gene expression programs in atlas-scale single-cell RNA-seq data.

Cell genomics Jan 8, 2025
Exploratory analysis of single-cell RNA sequencing (scRNA-seq) typically relies on hard clustering over two-dimensional projections like uniform manifold approximation and projection (UMAP). However, such methods can severely distort the data and hav...
Unsupervised Machine Learning Single-Cell Gene Expression Analysis Mice Sequence Analysis, RNA Single-Cell Analysis RNA-Seq Humans Brain Animals Gene Expression Profiling
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Analyzing the impact of clay minerals on the reservoir quality of the Lower Goru Formation using Unsupervised Machine Learning.

PloS one Jan 1, 2025
The reservoir quality of the Lower Goru Formation is highly variable due to its heterogeneous nature influenced by sea level fluctuations during the Early Cretaceous period. This study applies an unsupervised machine learning workflow, integrating Pr...
Unsupervised Machine Learning Aluminum Silicates Clay Geologic Sediments Principal Component Analysis Minerals Pakistan
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Threshold-based exploitation of noisy label in black-box unsupervised domain adaptation.

PloS one Jan 1, 2025
How can we perform unsupervised domain adaptation when transferring a black-box source model to a target domain? Black-box Unsupervised Domain Adaptation focuses on transferring the labels derived from a pre-trained black-box source model to an unlab...
Humans Algorithms Models, Theoretical Unsupervised Machine Learning
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Analyzing Demographic Grocery Purchase Patterns in Kenyan Supermarkets Through Unsupervised Learning Techniques.

Inquiry : a journal of medical care organization, provision and financing Jan 1, 2025
Kenya is experiencing a significant increase in the prevalence of non-communicable diseases (NCDs) such as cardiovascular diseases, hypertension, Type 2 diabetes, and certain cancers (bowel, lung, prostate, and uterine). This case is not unique to Ke...
Humans Female Kenya Middle Aged Adult Supermarkets Unsupervised Machine Learning Male Consumer Behavior
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Unsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2025
Autism Spectrum Disorder (ASD) encompasses a range of developmental disabilities marked by differences in social functioning, cognition, and behavior. Both genetic and environmental factors are known to contribute to ASD, yet the exact etiological fa...
Child Computational Biology Unsupervised Machine Learning Female Male Principal Component Analysis Supervised Machine Learning Neuroimaging Humans Algorithms Biomarkers Autism Spectrum Disorder
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Inferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches.

Briefings in bioinformatics Nov 22, 2024
This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised deep learning-derived imaging phenotypes (UDIPs). We employed a combination of genome-wide association study (GWAS) data, single-nucleus RNA sequencin...
Multiomics Brain Neoplasms Glioblastoma Humans Deep Learning Unsupervised Machine Learning Phenotype Genome-Wide Association Study
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Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes.

Briefings in bioinformatics Nov 22, 2024
Identification of cancer subtypes is a critical step for developing precision medicine. Most cancer subtyping is based on the analysis of RNA sequencing (RNA-seq) data from patient cohorts using unsupervised machine learning methods such as hierarchi...
Cluster Analysis Osteosarcoma Bone Neoplasms Humans Gene Expression Profiling Prognosis Transcriptome Unsupervised Machine Learning Bayes Theorem
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Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning Analysis.

Brain and behavior Nov 1, 2024
BACKGROUND: The demand for fresh strategies to analyze intricate multidimensional data in neuroscience is increasingly evident. One of the most complex events during our neurodevelopment is adolescence, where our nervous system suffers constant chang...
Brain Female Male Humans Adolescent Unsupervised Machine Learning Cluster Analysis Electroencephalography Binge Drinking
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