an easy CASSPER interface
CASSPER is a deep learning tool for automating the time-consuming Particle Picking task in obtaining the 3D reconstruction of protein structures using CryoEM micrographs. This work was published in Nature Communications in 2020.
The updated pre-trained version of the CASSPER protein particle picking algorithm with the detailed manual is available here. This new version makes it easy to use CASSPER in just two lines of code in python.
DBNN is a Bayesian Classifier that uses the concept of Imposed Conditional Independence to reduce the computational overhead typically associated with Bayesian formalism. It has been widely used in various astronomy data challenges like star-galaxy classification, Quasar candidate identification, Gravitational Wave detector anomaly and glitches detection etc. The code in C++ is opensource and can be downloaded from here
The GitHub page also share the data used for creating the Quasar catalog, the catalog itself and the procedures to do similar classification problems using DBNN. The GIF animation to the right shows the 3D cube of about 6 million objects that include Quasars, similarly appearing Galaxies and stars from the colour space of the Sloan Digital Sky Survey