Machine Learning Powered Biological Network Analysis
Video
Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks. Learn more at www.createdatasol.com
The following presentation also shows a sneak peak of a new data analysis visualization software, DAVe: Data Analysis and Visualization engine. Check out some early features. DAVe is built in R and seeks to support a seamless environment for advanced data analysis and machine learning tasks and biological functional and network analysis.
As an aside, building the main site (in progress) was a fun opportunity to experiment with Jekyll, Ruby and embedding slick interactive canvas elements into websites. You can checkout all the code here https://github.com/dgrapov/CDS_jekyll_site.
slides: https://www.slideshare.net/dgrapov/machine-learning-powered-metabolomic-network-analysis
Complex Systems Biology Informed Data Analysis
Metabolomics and the greater sphere of ‘Omic analyses are a burgeoning set tools for investigation of environmental and organismal mechanisms and interactions. Carrying out data analyses within complex biological system contexts is rewarding but also difficult. The following presentation considers components involved in conducting multivariate data analysis, modeling and visualization within biological contexts.
slides: https://www.slideshare.net/dgrapov/complex-systems-biology-informed-data-analysis-and-machine-learning
Metabolomics and Beyond: Challenges and Strategies for Next-gen Omic Analyses
Recently I had the pleasure of giving lecture for the Metabolomics Society on Challenges and Strategies for Next-gen Omic Analyses. You can check out all of my slides and video of the lecture below.
2014 UC Davis Proteomics Workshop
Recently I had the pleasure of teaching data analysis at the 2014 UC Davis Proteomics Workshop. This included a hands on lab for making gene ontology enrichment networks. You can check out my lecture and tutorial below or download all the material.
Introduction
Tutorial
2014 UC Davis Proteomics Workshop Dmitry Grapov is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Creative Data Solutions 