An active learning approach for clustering single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) data has been widely used to profile cellular heterogeneities with a high-resolution picture. Clustering analysis is a crucial step of scRNA-seq data analysis because it provides a chance to identify and uncover undiscovered cell types. Most methods for clustering scRNA-seq data use an unsupervised learning strategy.

Improved Risk Prediction Following Surgery Using Machine Learning Algorithms.

Background: Machine learning is used to analyze big data, often for the purposes of prediction. Analyzing a patient's healthcare utilization pattern may provide more precise estimates of risk for adverse events (AE) or death. We sought to characterize healthcare utilization prior to surgery using machine learning for the purposes of risk prediction.