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Unsupervised Machine Learning

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Bayesian machine learning analysis of single-molecule fluorescence colocalization images.

eLife
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...

Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease.

Scientific reports
Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging. To discov...

Recreating the Motion Trajectory of a System of Articulated Rigid Bodies on the Basis of Incomplete Measurement Information and Unsupervised Learning.

Sensors (Basel, Switzerland)
Re-creating the movement of an object consisting of articulated rigid bodies is an issue that concerns both mechanical and biomechanical systems. In the case of biomechanical systems, movement re-storation allows, among other things, introducing chan...

Few-Shot Learning for Deformable Medical Image Registration With Perception-Correspondence Decoupling and Reverse Teaching.

IEEE journal of biomedical and health informatics
Deformable medical image registration estimates corresponding deformation to align the regions of interest (ROIs) of two images to a same spatial coordinate system. However, recent unsupervised registration models only have correspondence ability wit...

Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry.

International journal of molecular sciences
The availability of computers has brought novel prospects in drug design. Neural networks (NN) were an early tool that cheminformatics tested for converting data into drugs. However, the initial interest faded for almost two decades. The recent succe...

Feature engineering solution with structured query language analytic functions in detecting electricity frauds using machine learning.

Scientific reports
Detecting fraud related to electricity consumption is usually a difficult challenge as the input datasets are sometimes unreliable due to missing and inconsistent records, faults, misinterpretation of meter reading remarks, status, etc. In this paper...

Unsupervised learning of haptic material properties.

eLife
When touching the surface of an object, its spatial structure translates into a vibration on the skin. The perceptual system evolved to translate this pattern into a representation that allows to distinguish between different materials. Here, we show...

Feature Identification With a Heuristic Algorithm and an Unsupervised Machine Learning Algorithm for Prior Knowledge of Gait Events.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The purpose of this study was to compare a heuristic feature identification algorithm with output from the Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) utilizing minimally sampled (≤ 100 Hz) human locomotion data for identification of...

Unsupervised learning of brain state dynamics during emotion imagination using high-density EEG.

NeuroImage
This study applies adaptive mixture independent component analysis (AMICA) to learn a set of ICA models, each optimized by fitting a distributional model for each identified component process while maximizing component process independence within som...

Deep Unfolding for Non-Negative Matrix Factorization with Application to Mutational Signature Analysis.

Journal of computational biology : a journal of computational molecular cell biology
Non-negative matrix factorization (NMF) is a fundamental matrix decomposition technique that is used primarily for dimensionality reduction and is increasing in popularity in the biological domain. Although finding a unique NMF is generally not possi...