PPT-Inferring microbiome networks using graphical models

BY Jared Samilow Professor Shibu Yooseph What does that actually mean We have a matrix whose rows are samples and columns are taxa A sample is just a swab of a particular environment Taxa are in this problem just different species of microbes . Alan Ritter. Markov Networks. Undirected. graphical models. Cancer. Cough. Asthma. Smoking. Potential functions defined over cliques. Smoking. Cancer. . Ф. (S,C). False. False. 4.5. False. True. Alan Ritter. Problem: Non-IID Data. Most real-world data is not IID. (like coin flips). Multiple correlated variables. Examples:. Pixels in an image. Words in a document. Genes in a microarray. We saw one example of how to deal with this. Graphical Model Inference. View observed data and unobserved properties as . random variables. Graphical Models: compact graph-based encoding of probability distributions (high dimensional, with complex dependencies). (Markov Nets). (Slides from Sam . Roweis. ). Connection to MCMC:. . . MCMC requires sampling a node given its . markov. blanket. . Need to use P(. x|MB. (x)). . . For . Bayes. nets MB(x) contains more. Tamara L Berg. CSE 595 Words & Pictures. Announcements. HW3 . online tonight. Start thinking about project ideas . Project . proposals in class Oct 30 . . Come to office hours . Oct. 23-25 . to discuss . Automated Reasoning with Graphical models. Rina. Dechter. Bren school of ICS. University of California, Irvine. ICS 90 . November 2016. Agenda. My work in AI. How did I get to AI?. 2. ICS-90, 2016. Knowledge representation and Reasoning. Generalized covariance matrices and their inverses. Menglong Li. Ph.d. of Industrial Engineering. Dec 1. st. 2016. Outline. Recap: Gaussian graphical model. Extend to general graphical model. Model setting. Carolyn J. Anderson, Stanley Wasserman & Bradley Crouch (1999). 1. Predictive Models: Problems. Relationship specific social relation – explanatory variables. Response variable dichotomous/discrete (actor . Kenneth Frank, College of Education and Fisheries and Wildlife. Help from: Ann Krause, Ben Michael Pogodzinski, Bo Yan, Min Sun, I-Chen, Chong Min Kim. Cep 991B Fall 2018. To participate on zoom you will click on . B. . Aditya. . Prakash. http://www.cs.cmu.edu/~badityap. Carnegie Mellon University. MMS, SIAM AN, Minneapolis, July 10, 2012. Thanks !. Jeremy . Kepner. David . Bader. John . Gilbert. 2. Networks are everywhere!. Comparison of Strategies for Scalable Causal Discovery of Latent Variable Models from Mixed Data Vineet Raghu , Joseph D. Ramsey, Alison Morris, Dimitrios V. Manatakis, Peter Spirtes, Panos K. Chrysanthis, Clark Glymour, and Panayiotis V. Benos ). Prof. . Ralucca Gera, . Applied Mathematics Dept.. Naval Postgraduate School. Monterey, California. rgera@nps.edu. Excellence Through Knowledge. Learning Outcomes. I. dentify . network models and explain their structures. Ron . Collman. , MD - University of Pennsylvania. Dirk Dittmer, PhD - University of North Carolina. The Microbiome. The totality of microbial residents living in concert with the human body, along with their genes, functions, products. Part 1: Overview and Applications . Outline. Motivation for Probabilistic Graphical Models. Applications of Probabilistic Graphical Models. Graphical Model Representation. Probabilistic Modeling. 1. when trying to solve a real-world problem using mathematics, it is common to define a mathematical model of the world, e.g..