AIMC Topic: Unsupervised Machine Learning

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An unsupervised learning approach to identify novel signatures of health and disease from multimodal data.

Genome medicine
BACKGROUND: Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. Integrated analysis of data from different modalities has the potential of uncovering novel biomarkers and disease sign...

An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection.

IEEE journal of biomedical and health informatics
The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches are mainly based on supervised learning with well-paired image-level or voxel-level labels. However...

Adaptive feature extraction in EEG-based motor imagery BCI: tracking mental fatigue.

Journal of neural engineering
OBJECTIVE: Electroencephalogram (EEG) signals are non-stationary. This could be due to internal fluctuation of brain states such as fatigue, frustration, etc. This necessitates the development of adaptive brain-computer interfaces (BCI) whose perform...

R/PY-SUMMA: An R/Python Package for Unsupervised Ensemble Learning for Binary Classification Problems in Bioinformatics.

Journal of computational biology : a journal of computational molecular cell biology
The increasing availability of complex data in biology and medicine has promoted the use of machine learning in classification tasks to address important problems in translational and fundamental science. Two important obstacles, however, may limit t...

Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records.

Journal of biomedical informatics
Machine learning has become ubiquitous and a key technology on mining electronic health records (EHRs) for facilitating clinical research and practice. Unsupervised machine learning, as opposed to supervised learning, has shown promise in identifying...

Unsupervised Machine Learning of Topics Documented by Nurses about Hospitalized Patients Prior to a Rapid-Response Event.

Applied clinical informatics
BACKGROUND: In the hospital setting, it is crucial to identify patients at risk for deterioration before it fully develops, so providers can respond rapidly to reverse the deterioration. Rapid response (RR) activation criteria include a subjective co...

Supervised and unsupervised algorithms for bioinformatics and data science.

Progress in biophysics and molecular biology
Bioinformatics refers to an ever evolving huge field of research based on millions of algorithms, designated to several data banks. Such algorithms are either supervised or unsupervised. In this article, a detailed overview of the supervised and unsu...

Deep Learning Approach for Epileptic Focus Localization.

IEEE transactions on biomedical circuits and systems
The task of epileptic focus localization receives great attention due to its role in an effective epileptic surgery. The clinicians highly depend on the intracranial EEG data to make a surgical decision related to epileptic subjects suffering from un...

CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data.

PLoS computational biology
Quantifying cell-type proportions and their corresponding gene expression profiles in tissue samples would enhance understanding of the contributions of individual cell types to the physiological states of the tissue. Current approaches that address ...

Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning.

PloS one
Due to the overlapping emission spectra of fluorophores, fluorescence microscopy images often have bleed-through problems, leading to a false positive detection. This problem is almost unavoidable when the samples are labeled with three or more fluor...