A comparison of automatic cell identification methods for single-cell RNA sequencing data | Genome Biology
Reseachers benchmarked 22 classification methods that automatically assign cell identities including single-cell-specific and general-purpose classifiers. The performance of the methods is evaluated using 27 publicly available single-cell RNA sequencing datasets of different sizes, technologies, species, and levels of complexity. The general-purpose support vector machine classifier has overall the best performance across the different experiments.
Predicting the genetic ancestry of 2.6 million New York City patients using clinical data | bioRxiv
Researchers present a novel algorithm for predicting genetic ancestry using only variables that are routinely captured in electronic health records (EHRs), such as self-reported race and ethnicity, and condition billing codes. Using patients that have both genetic and clinical information at Columbia University / New York-Presbyterian Irving Medical Center, they developed a pipeline that uses only clinical data to predict the genetic ancestry of all patients of which more than 80% identify as other or unknown.
The elements of algorithms | Chemistry World
Since Dmitri Mendeleev (and others) first sketched out the periodic relationships between the elements in the 1860s, it has been estimated that around a thousand different tables have appeared in print. But two recent papers have shown that it is now possible to use machine learning to rediscover the table empirically, from the way it is implicitly embedded within the milieu of chemistry.