machine learning

By Nick Eagles We’ve recently been interested in exploring the (largely python-based) tools others have published to process spatial transcriptomics data for various end goals. A common goal is to integrate data from platforms like Visium, which provides some information about how gene expression is spatially organized, with other approaches with potentially better spatial resolution or gene throughput. In particular, we came across a paper by Biancalani, Scalia et al.

By Arta Seyedian Medical Cost Personal Datasets Insurance Forecast by using Linear Regression Link to Kaggle Page Link to GitHub Source Around the end of October 2020, I attended the Open Data Science Conference primarily for the workshops and training sessions that were offered. The first workshop I attended was a demonstration by Jared Lander on how to implement machine learning methods in R using a new package named tidymodels.