AIMC Topic: Unsupervised Machine Learning

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Integrating biological knowledge based on functional annotations for biclustering of gene expression data.

Computer methods and programs in biomedicine Mar 18, 2015
Gene expression data analysis is based on the assumption that co-expressed genes imply co-regulated genes. This assumption is being reformulated because the co-expression of a group of genes may be the result of an independent activation with respect...
Gene Ontology Algorithms Gene Expression Profiling Databases, Genetic Cluster Analysis Genes, Fungal Unsupervised Machine Learning Molecular Sequence Annotation Data Mining Knowledge Bases Yeasts
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Molecular classification of amyotrophic lateral sclerosis by unsupervised clustering of gene expression in motor cortex.

Neurobiology of disease Dec 10, 2014
Amyotrophic lateral sclerosis (ALS) is a rapidly progressive and ultimately fatal neurodegenerative disease, caused by the loss of motor neurons in the brain and spinal cord. Although 10% of ALS cases are familial (FALS), the majority are sporadic (S...
Motor Cortex White People Immunohistochemistry Oligonucleotide Array Sequence Analysis Cluster Analysis Gene Expression Profiling Humans Gene Expression Adult Real-Time Polymerase Chain Reaction Male Amyotrophic Lateral Sclerosis Female Unsupervised Machine Learning RNA, Messenger Middle Aged Aged
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Renal Transplant Survival Prediction From Unsupervised Deep Learning-Based Radiomics on Early Dynamic Contrast-Enhanced MRI.

Academic radiology Aug 1, 2025
RATIONALE AND OBJECTIVES: End-stage renal disease is characterized by an irreversible decline in kidney function. Despite a risk of chronic dysfunction of the transplanted kidney, renal transplantation is considered the most effective solution among ...
Kidney Transplantation Retrospective Studies Female Adult Humans Deep Learning Magnetic Resonance Imaging Graft Survival Unsupervised Machine Learning Contrast Media Radiomics Kidney Failure, Chronic Male Middle Aged
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Unsupervised Accuracy Estimation for Brain-Computer Interfaces Based on Selective Auditory Attention Decoding.

IEEE transactions on bio-medical engineering Aug 1, 2025
OBJECTIVE: Selective auditory attention decoding (AAD) algorithms process brain data such as electroencephalography to decode to which of multiple competing sound sources a person attends. Example use cases are neuro-steered hearing aids or communica...
Algorithms Female Young Adult Adult Humans Signal Processing, Computer-Assisted Attention Brain-Computer Interfaces Auditory Perception Male Unsupervised Machine Learning Electroencephalography
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Revisiting Abnormalities of Ventricular Depolarization: Redefining Phenotypes and Associated Outcomes Using Tree-Based Dimensionality Reduction.

Journal of the American Heart Association Jul 1, 2025
BACKGROUND: Abnormal ventricular depolarization, evident as a broad QRS complex on an ECG, is traditionally categorized into left bundle-branch block (LBBB) and right bundle-branch block or nonspecific intraventricular conduction delay. This categori...
Electrocardiography Action Potentials Unsupervised Machine Learning Heart Ventricles Aged Male Middle Aged Bundle-Branch Block Dimensionality Reduction Phenotype Female Humans
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A Weight-Aware-Based Multisource Unsupervised Domain Adaptation Method for Human Motion Intention Recognition.

IEEE transactions on cybernetics Jul 1, 2025
Accurate recognition of human motion intention (HMI) is beneficial for exoskeleton robots to improve the wearing comfort level and achieve natural human-robot interaction. A classifier trained on labeled source subjects (domains) performs poorly on u...
Pattern Recognition, Automated Movement Unsupervised Machine Learning Intention Humans Algorithms
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Supervised and unsupervised learning for lung perfusion data segmentation in electrical impedance tomography.

Biomedical physics & engineering express Jun 13, 2025
: Effective lung gas exchange relies on the balance between alveolar ventilation and perfusion, which can be disrupted in mechanically ventilated patients. Lung perfusion assessment using electrical impedance tomography (EIT) typically involves a sud...
Perfusion Electric Impedance Acute Lung Injury Tomography Support Vector Machine Unsupervised Machine Learning Supervised Machine Learning Lung Swine Image Processing, Computer-Assisted Algorithms Neural Networks, Computer Animals
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Unsupervised Adaptive Deep Learning Framework for Video Denoising in Light Scattering Imaging.

Analytical chemistry Jun 3, 2025
Light scattering is a powerful tool that has been widely applied in various scenarios, such as nanoparticle analysis, single-cell measurement, and blood flow monitoring. However, noise is always a concerning and challenging issue in light scattering ...
Unsupervised Machine Learning Light Signal-To-Noise Ratio Scattering, Radiation Image Processing, Computer-Assisted Nanoparticles Particle Size Humans Deep Learning
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Unsupervised discovery of clinical disease signatures using probabilistic independence.

Journal of biomedical informatics Jun 1, 2025
OBJECTIVE: This study uses probabilistic independence to disentangle patient-specific sources of disease and their signatures in Electronic Health Record (EHR) data.
Algorithms Electronic Health Records Humans Lung Neoplasms Probability Models, Statistical Unsupervised Machine Learning
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Unsupervised detection of sub-sequence anomalies in epilepsy EEG.

Computers in biology and medicine Jun 1, 2025
Seizures in electroencephalogram (EEG) data constitute a special case of sub-sequence anomalies in multivariate data with numerous challenges. These challenges include the irregular patterns exhibited even by the same individual, making seizures diff...
Humans Electroencephalography Epilepsy Algorithms Seizures Signal Processing, Computer-Assisted Unsupervised Machine Learning
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