Biomedical scientific literature is growing at a very rapid pace, which makes increasingly difficult for human experts to spot the most relevant results hidden in the papers. Automatized information extraction tools based on text mining techniques ar...
Outcome regressed on class labels identified by unsupervised clustering is custom in many applications. However, it is common to ignore the misclassification of class labels caused by the learning algorithm, which potentially leads to serious bias of...
Proceedings of the National Academy of Sciences of the United States of America
Jan 19, 2021
Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream...
BACKGROUND: Interpretation of medical images for the diagnosis and treatment of complex diseases from high-dimensional and heterogeneous data remains a key challenge in transforming healthcare. In the last few years, both supervised and unsupervised ...
Methylation profiling has become a mainstay in brain tumor diagnostics since the introduction of the first publicly available classification tool by the German Cancer Research Center in 2017. We demonstrate the capability of this system through an ex...
The international journal of neuropsychopharmacology
Nov 26, 2020
BACKGROUND: There is a lack of reliable biomarkers for major depressive disorder (MDD) in clinical practice. However, several studies have shown an association between alterations in microRNA levels and MDD, albeit none of them has taken advantage of...
Studies in health technology and informatics
Nov 23, 2020
The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent COVID-19 clusters in patients with chronic lower respiratory diseases (CLRD). Patients who underwent testing for SARS-CoV-2 were identified f...
Journal of the American Medical Informatics Association : JAMIA
Jul 1, 2020
OBJECTIVE: Unsupervised machine learning approaches hold promise for large-scale clinical data. However, the heterogeneity of clinical data raises new methodological challenges in feature selection, choosing a distance metric that captures biological...