AIMC Topic: Unsupervised Machine Learning

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Unsupervised multi-domain multimodal image-to-image translation with explicit domain-constrained disentanglement.

Neural networks : the official journal of the International Neural Network Society Jul 25, 2020
Image-to-image translation has drawn great attention during the past few years. It aims to translate an image in one domain to a target image in another domain. However, three big challenges remain in image-to-image translation: (1) the lack of large...
Image Processing, Computer-Assisted Unsupervised Machine Learning Pattern Recognition, Automated
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A hybrid unsupervised-Deep learning tandem for electrooculography time series analysis.

PloS one Jul 21, 2020
Medical data are often tricky to get mined for patterns even by the generally demonstrated successful modern methodologies of deep learning. This paper puts forward such a medical classification task, where patient registers of two of the categories ...
Time Factors Photic Stimulation Humans Cluster Analysis Electrooculography Neural Networks, Computer Unsupervised Machine Learning Saccades Algorithms Databases as Topic Deep Learning
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Cortical surface registration using unsupervised learning.

NeuroImage Jul 20, 2020
Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface propert...
Young Adult Adult Male Aging Deep Learning Humans Female Aged Aged, 80 and over Alzheimer Disease Neuroimaging Cerebral Cortex Cognitive Dysfunction Models, Theoretical Magnetic Resonance Imaging Unsupervised Machine Learning
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Exploration of the mechanism of traditional Chinese medicine by AI approach using unsupervised machine learning for cellular functional similarity of compounds in heterogeneous networks, XiaoErFuPi granules as an example.

Pharmacological research Jul 17, 2020
'Polypharmacology' is usually used to describe the network-wide effect of a single compound, but traditional Chinese medicine (TCM) has a polypharmacological effect naturally based on the 'multi-components, multi-targets and multi-pathways' principle...
Humans Animals Polypharmacology Dyspepsia Proteome Signal Transduction Medicine, Chinese Traditional Rats, Sprague-Dawley Cluster Analysis Unsupervised Machine Learning Structure-Activity Relationship Molecular Structure Drugs, Chinese Herbal Male Gene Regulatory Networks Gene Expression Profiling Transcriptome Systems Biology Protein Interaction Maps Duodenum Proteomics
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Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression.

Aging Jul 9, 2020
Tau pathology and brain atrophy are the closest correlate of cognitive decline in Alzheimer's disease (AD). Understanding heterogeneity and longitudinal progression of atrophy during the disease course will play a key role in understanding AD pathoge...
Female Aged Aged, 80 and over Male Bayes Theorem Disease Progression Amyloid beta-Peptides Longitudinal Studies Humans Alzheimer Disease Magnetic Resonance Imaging Brain Image Interpretation, Computer-Assisted Cluster Analysis Atrophy Unsupervised Machine Learning
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Bioinformatics analysis of the genes involved in the extension of prostate cancer to adjacent lymph nodes by supervised and unsupervised machine learning methods: The role of SPAG1 and PLEKHF2.

Genomics Jun 30, 2020
The present study aimed to identify the genes associated with the involvement of adjunct lymph nodes of patients with prostate cancer (PCa) and to provide valuable information for the identification of potential diagnostic biomarkers and pathological...
Humans Datasets as Topic Male Cluster Analysis Computational Biology Antigens, Surface Unsupervised Machine Learning Lymphatic Metastasis Vesicular Transport Proteins Prostatic Neoplasms GTP-Binding Proteins Supervised Machine Learning
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Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells.

eLife Jun 23, 2020
A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses. We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is uns...
Tumor Cells, Cultured Algorithms Pilot Projects Unsupervised Machine Learning Glioblastoma Humans
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Machine Learning of 12-Lead QRS Waveforms to Identify Cardiac Resynchronization Therapy Patients With Differential Outcomes.

Circulation. Arrhythmia and electrophysiology Jun 14, 2020
BACKGROUND: Cardiac resynchronization therapy (CRT) improves heart failure outcomes but has significant nonresponse rates, highlighting limitations in ECG selection criteria: QRS duration (QRSd) ≥150 ms and subjective labeling of left bundle branch b...
Risk Factors Aged Male Ventricular Function, Left Stroke Volume Heart-Assist Devices Middle Aged Heart Transplantation Risk Assessment Bundle-Branch Block Disease Progression Diagnosis, Computer-Assisted Humans Recovery of Function Retrospective Studies Predictive Value of Tests Female Signal Processing, Computer-Assisted Heart Failure Cardiac Resynchronization Therapy Time Factors Electrocardiography Treatment Outcome Unsupervised Machine Learning
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Defining heterogeneity of epicardial functional stenosis with low coronary flow reserve by unsupervised machine learning.

Heart and vessels Jun 6, 2020
Low CFR is associated with poor prognosis, whereas it is a heterogeneous condition according to the actual coronary flow, such as high resting or low hyperemic coronary flow, which should have different physiological traits and clinical implications....
Spain Blood Flow Velocity Cardiac Catheterization Humans Coronary Stenosis Prognosis Republic of Korea Fractional Flow Reserve, Myocardial Female Hyperemia Aged Male Unsupervised Machine Learning Middle Aged Predictive Value of Tests Severity of Illness Index Registries Diagnosis, Computer-Assisted Japan Cluster Analysis
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EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia.

International journal of neural systems May 28, 2020
The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography...
Psycholinguistics Connectome Speech Perception Language Tests Dyslexia Humans Neural Networks, Computer Child Unsupervised Machine Learning Electroencephalography Cerebral Cortex
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