AIMC Topic: Unsupervised Machine Learning

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Generative Adversarial Networks are special cases of Artificial Curiosity (1990) and also closely related to Predictability Minimization (1991).

Neural networks : the official journal of the International Neural Network Society Apr 13, 2020
I review unsupervised or self-supervised neural networks playing minimax games in game-theoretic settings: (i) Artificial Curiosity (AC, 1990) is based on two such networks. One network learns to generate a probability distribution over outputs, the ...
Neural Networks, Computer Artificial Intelligence Forecasting Humans Unsupervised Machine Learning Probability Goals
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Top-down machine learning approach for high-throughput single-molecule analysis.

eLife Apr 8, 2020
Single-molecule approaches provide enormous insight into the dynamics of biomolecules, but adequately sampling distributions of states and events often requires extensive sampling. Although emerging experimental techniques can generate such large dat...
Unsupervised Machine Learning Software High-Throughput Screening Assays Algorithms Fluorescent Dyes Single Molecule Imaging
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Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification.

Clinical epigenetics Apr 3, 2020
BACKGROUND: Machine learning is a sub-field of artificial intelligence, which utilises large data sets to make predictions for future events. Although most algorithms used in machine learning were developed as far back as the 1950s, the advent of big...
Humans Machine Learning Precision Medicine Epigenesis, Genetic Supervised Machine Learning Disease Diagnosis Epigenomics DNA Methylation Unsupervised Machine Learning
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Interpatient Similarities in Cardiac Function: A Platform for Personalized Cardiovascular Medicine.

JACC. Cardiovascular imaging Mar 18, 2020
OBJECTIVES: The authors applied unsupervised machine-learning techniques for integrating echocardiographic features of left ventricular (LV) structure and function into a patient similarity network that predicted major adverse cardiac event(s) (MACE)...
Image Interpretation, Computer-Assisted Pattern Recognition, Automated Humans Retrospective Studies Female Prospective Studies Prognosis Middle Aged Male Disease Progression Ventricular Function, Left Unsupervised Machine Learning Aged Patient Readmission Echocardiography Predictive Value of Tests Aged, 80 and over Ventricular Dysfunction, Left Registries Adult
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Unsupervised Machine Learning Reveals Novel Traumatic Brain Injury Patient Phenotypes with Distinct Acute Injury Profiles and Long-Term Outcomes.

Journal of neurotrauma Mar 11, 2020
The heterogeneity of traumatic brain injury (TBI) remains a core challenge for the success of interventional clinical trials. Data-driven approaches for patient stratification may help to identify TBI patient phenotypes during the acute injury period...
Brain Injuries, Traumatic Unsupervised Machine Learning Adult Male Middle Aged Adolescent Time Factors Treatment Outcome Phenotype Female Young Adult Humans
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Unsupervised Machine Learning in Pathology: The Next Frontier.

Surgical pathology clinics Mar 9, 2020
Applications of artificial intelligence and particularly deep learning to aid pathologists in carrying out laborious and qualitative tasks in histopathologic image analysis have now become ubiquitous. We introduce and illustrate how unsupervised mach...
Image Interpretation, Computer-Assisted Pathology, Clinical Deep Learning Humans Unsupervised Machine Learning Central Nervous System Diseases
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Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data.

Nature communications Mar 3, 2020
Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biolog...
Epigenome Humans K562 Cells Neural Networks, Computer Computer Graphics Data Analysis Unsupervised Machine Learning Genomics Gene Expression Chromatin Cluster Analysis Markov Chains Algorithms Reproducibility of Results
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LungRegNet: An unsupervised deformable image registration method for 4D-CT lung.

Medical physics Feb 26, 2020
PURPOSE: To develop an accurate and fast deformable image registration (DIR) method for four-dimensional computed tomography (4D-CT) lung images. Deep learning-based methods have the potential to quickly predict the deformation vector field (DVF) in ...
Unsupervised Machine Learning Four-Dimensional Computed Tomography Humans Lung Image Processing, Computer-Assisted
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Essential oils against bacterial isolates from cystic fibrosis patients by means of antimicrobial and unsupervised machine learning approaches.

Scientific reports Feb 14, 2020
Recurrent and chronic respiratory tract infections in cystic fibrosis (CF) patients result in progressive lung damage and represent the primary cause of morbidity and mortality. Staphylococcus aureus (S. aureus) is one of the earliest bacteria in CF ...
Biofilms Oils, Volatile Bacteria Humans Female Unsupervised Machine Learning Cystic Fibrosis Transmembrane Conductance Regulator Anti-Infective Agents Mutation Young Adult Cystic Fibrosis Male
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Assessing the Heterogeneity of Complaints Related to Tinnitus and Hyperacusis from an Unsupervised Machine Learning Approach: An Exploratory Study.

Audiology & neuro-otology Feb 14, 2020
INTRODUCTION: Subjective tinnitus (ST) and hyperacusis (HA) are common auditory symptoms that may become incapacitating in a subgroup of patients who thereby seek medical advice. Both conditions can result from many different mechanisms, and as a con...
Data Mining Unsupervised Machine Learning Hyperacusis Tinnitus Natural Language Processing Humans Internet Statistics as Topic Machine Learning Quality of Life
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