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Unsupervised Machine Learning

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Decoding reappraisal and suppression from neural circuits: A combined supervised and unsupervised machine learning approach.

Cognitive, affective & behavioral neuroscience
Emotion regulation is a core construct of mental health and deficits in emotion regulation abilities lead to psychological disorders. Reappraisal and suppression are two widely studied emotion regulation strategies but, possibly due to methodological...

Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: application to surgical imaging.

International journal of computer assisted radiology and surgery
PURPOSE: Hyperspectral imaging has the potential to improve intraoperative decision making if tissue characterisation is performed in real-time and with high-resolution. Hyperspectral snapshot mosaic sensors offer a promising approach due to their fa...

An Unsupervised Machine Learning Approach to Evaluating the Association of Symptom Clusters With Adverse Outcomes Among Older Adults With Advanced Cancer: A Secondary Analysis of a Randomized Clinical Trial.

JAMA network open
IMPORTANCE: Older adults with advanced cancer who have high pretreatment symptom severity often experience adverse events during cancer treatments. Unsupervised machine learning may help stratify patients into different risk groups.

Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods.

Scientific reports
Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and related diseases. We applied an unsupervised machine learning method, PGMRA, to discover phenotype-genotype many-to-many relations between genotype and ...

Uncovering psychiatric phenotypes using unsupervised machine learning: A data-driven symptoms approach.

European psychiatry : the journal of the Association of European Psychiatrists
BACKGROUND: Current categorical classification systems of psychiatric diagnoses lead to heterogeneity of symptoms within disorders and common co-occurrence of disorders. We investigated the heterogeneous and overlapping nature of symptom endorsement ...

Alternative additives associated in the feeding of laying hens: performance, biometrics, bone traits, and economic evaluation-an unsupervised machine learning approach.

Tropical animal health and production
Given the current bans on the use of some growth promoting antibiotics in poultry nutrition, the need to use alternative additives which could replace traditional promoters in diets has arisen. The objective of this study was to evaluate the effect o...

Unsupervised learning reveals interpretable latent representations for translucency perception.

PLoS computational biology
Humans constantly assess the appearance of materials to plan actions, such as stepping on icy roads without slipping. Visual inference of materials is important but challenging because a given material can appear dramatically different in various sce...

An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation.

Journal of digital imaging
The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography-guided adaptive radiation therapy. A deep unsupervised learning mod...

Detecting genomic deletions from high-throughput sequence data with unsupervised learning.

BMC bioinformatics
BACKGROUND: Structural variation (SV), which ranges from 50 bp to [Formula: see text] 3 Mb in size, is an important type of genetic variations. Deletion is a type of SV in which a part of a chromosome or a sequence of DNA is lost during DNA replicati...

Applying Unsupervised Machine Learning Models to Identify Serve Performance Related Indicators in Women's Volleyball.

Research quarterly for exercise and sport
In volleyball, the effect of different factors on serve performance has usually been analyzed with traditional statistical techniques such as logistic regression or discriminant analysis. In this study, two of the main models used in unsupervised ma...