Not exact matches
The collaboration will involve creating
machine learning methods which can analyze mountains of
existing type 1 diabetes research in order to figure out risk factors and, hopefully, effective prevention measures.
Using
machine learning to analyze and model
existing crystal structures, the PLMF
method is able to predict the properties of new materials proposed by scientists and engineers.
We developed several computational and
machine learning methods to successfully identify behavioral patterns or signatures associated with different classes of reference drugs, from which to predict the class of novel compounds (Brunner et al., 2012, Alexandrov et al., 2015), and more recently developed
methods to allow us to compare animal models of AD and its progression, and to identify (in silico) novel compounds from our
existing database of thousands of novel and reference compounds with the potential to reverse the AD model behavioral profile.
This project seeks to use a blend modern causal inference,
machine learning methods, and classic statistical tools from survey sampling to extend
existing approaches for treatment effect variation into education contexts.
Machine learning techniques provide cost - effective alternatives to traditional
methods for extracting underlying relationships between information and data and for predicting future events by processing
existing information to train models.