AIMC Topic: Cluster Analysis

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Chaos versus noise as drivers of multistability in neural networks.

Chaos (Woodbury, N.Y.)
The multistable behavior of neural networks is actively being studied as a landmark of ongoing cerebral activity, reported in both functional Magnetic Resonance Imaging (fMRI) and electro- or magnetoencephalography recordings. This consists of a cont...

Classification of Cardiovascular Disease via A New SoftMax Model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Cardiovascular disease clinical diagnosis is an essentially problem of pattern recognition. In the traditional intelligent diagnosis, the evaluation of classification algorithm is based on the final accuracy of the disease diagnosis. In this paper, a...

Convolutional neural networks for classification of alignments of non-coding RNA sequences.

Bioinformatics (Oxford, England)
MOTIVATION: The convolutional neural network (CNN) has been applied to the classification problem of DNA sequences, with the additional purpose of motif discovery. The training of CNNs with distributed representations of four nucleotides has successf...

L1000FWD: fireworks visualization of drug-induced transcriptomic signatures.

Bioinformatics (Oxford, England)
MOTIVATION: As part of the NIH Library of Integrated Network-based Cellular Signatures program, hundreds of thousands of transcriptomic signatures were generated with the L1000 technology, profiling the response of human cell lines to over 20 000 sma...

Texture Analysis and Machine Learning for Detecting Myocardial Infarction in Noncontrast Low-Dose Computed Tomography: Unveiling the Invisible.

Investigative radiology
OBJECTIVES: The aim of this study was to test whether texture analysis and machine learning enable the detection of myocardial infarction (MI) on non-contrast-enhanced low radiation dose cardiac computed tomography (CCT) images.

Machine learning to detect signatures of disease in liquid biopsies - a user's guide.

Lab on a chip
New technologies that measure sparse molecular biomarkers from easily accessible bodily fluids (e.g. blood, urine, and saliva) are revolutionizing disease diagnostics and precision medicine. Microchip devices can measure more disease biomarkers with ...

Big-Data Analysis, Cluster Analysis, and Machine-Learning Approaches.

Advances in experimental medicine and biology
Medicine will experience many changes in the coming years because the so-called "medicine of the future" will be increasingly proactive, featuring four basic elements: predictive, personalized, preventive, and participatory. Drivers for these changes...

Impact of Imputing Missing Data in Bayesian Network Structure Learning for Obstructive Sleep Apnea Diagnosis.

Studies in health technology and informatics
Numerous diagnostic decisions are made every day by healthcare professionals. Bayesian networks can provide a useful aid to the process, but learning their structure from data generally requires the absence of missing data, a common problem in medica...