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Unsupervised Machine Learning

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Exploring the Organization of Semantic Memory through Unsupervised Analysis of Event-related Potentials.

Journal of cognitive neuroscience
Modern multivariate methods have enabled the application of unsupervised techniques to analyze neurophysiological data without strict adherence to predefined experimental conditions. We demonstrate a multivariate method that leverages priming effects...

ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data.

BMC bioinformatics
BACKGROUND: With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-...

Foreground Detection by Competitive Learning for Varying Input Distributions.

International journal of neural systems
One of the most important challenges in computer vision applications is the background modeling, especially when the background is dynamic and the input distribution might not be stationary, i.e. the distribution of the input data could change with t...

Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration.

NeuroImage
The human brain is in constant flux, as distinct areas engage in transient communication to support basic behaviors as well as complex cognition. The collection of interactions between cortical and subcortical areas forms a functional brain network w...

Identification of candidate drugs using tensor-decomposition-based unsupervised feature extraction in integrated analysis of gene expression between diseases and DrugMatrix datasets.

Scientific reports
Identifying drug target genes in gene expression profiles is not straightforward. Because a drug targets proteins and not mRNAs, the mRNA expression of drug target genes is not always altered. In addition, the interaction between a drug and protein c...

Digging deep into Golgi phenotypic diversity with unsupervised machine learning.

Molecular biology of the cell
The synthesis of glycans and the sorting of proteins are critical functions of the Golgi apparatus and depend on its highly complex and compartmentalized architecture. High-content image analysis coupled to RNA interference screening offers opportuni...

Learning from Narrated Instruction Videos.

IEEE transactions on pattern analysis and machine intelligence
Automatic assistants could guide a person or a robot in performing new tasks, such as changing a car tire or repotting a plant. Creating such assistants, however, is non-trivial and requires understanding of visual and verbal content of a video. Towa...

Unsupervised Segmentation of 5D Hyperpolarized Carbon-13 MRI Data Using a Fuzzy Markov Random Field Model.

IEEE transactions on medical imaging
Hyperpolarized MRI with C-labelled compounds is an emerging clinical technique allowing in vivo metabolic processes to be characterized non-invasively. Accurate quantification of C data, both for clinical and research purposes, typically relies on th...

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

PloS one
Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural hete...

AV-1451 PET imaging of tau pathology in preclinical Alzheimer disease: Defining a summary measure.

NeuroImage
Utilizing [18F]-AV-1451 tau positron emission tomography (PET) as an Alzheimer disease (AD) biomarker will require identification of brain regions that are most important in detecting elevated tau pathology in preclinical AD. Here, we utilized an uns...