Tags: Machine Learning

Resources (1-6 of 6)

  1. Applying Machine Learning to Computational Chemistry: Can We Predict Molecular Properties Faster without Compromising Accuracy?

    14 Aug 2017 | Presentation Materials | Contributor(s): Hanjing Xu, Pradeep Kumar Gurunathan

    Non-covalent interactions are crucial in analyzing protein folding and structure, function of DNA and RNA, structures of molecular crystals and aggregates, and many other processes in the fields...

    http://nanohub.org/resources/26904

  2. Predicting Locations of Pollution Sources using Convolutional Neural Networks

    07 Aug 2017 | Presentation Materials | Contributor(s): Yiheng Chi, Guang Lin, Nickolas D Winovich

    Pollution is a severe problem today, and the main challenge in water pollution controls and eliminations is detecting and locating pollution sources. This research project aims to predict the...

    http://nanohub.org/resources/26948

  3. Model Selection Using Gaussian Mixture Models and Parallel Computing

    27 Jul 2016 | Tools | Contributor(s): Tian Qiu, Yiyi Chen, Georgios Karagiannis, Guang Lin

    Model Selection Using Gaussian Mixture Models

    http://nanohub.org/resources/msugmmpc

  4. Gaussian process regression in 1D

    04 Dec 2014 | Tools | Contributor(s): Ilias Bilionis, Alejandro Strachan, Benjamin P Haley, Martin Hunt, Rohit Kaushal Tripathy, Sam Reeve

    Use Gaussian processes to represent x-y data

    http://nanohub.org/resources/gptool

  5. Random Forest Model Objects for Pulmonary Toxicity Risk Assessment

    17 Apr 2013 | Downloads | Contributor(s): Jeremy M Gernand

    This download contains MATLAB treebagger or Random Forest (RF) model objects created via meta-analysis of nanoparticle rodent pulmonary toxicity experiments. The ReadMe.txt file contains object...

    http://nanohub.org/resources/17539

  6. Model Validation Document for "A Meta-Analysis of Carbon Nanotube Pulmonary Toxicity Studies – How Physical Dimensions and Impurities Affect the Toxicity of Carbon Nanotubes"

    19 Nov 2012 | Papers | Contributor(s): Jeremy M Gernand, Elizabeth Casman

    This document contains model learning statistics, and structure of the models utilized in the paper “A meta-analysis of carbon nanotube pulmonary toxicity studies – How physical dimensions and...

    http://nanohub.org/resources/15901