PPT-Expectation Maximization Meets Sampling in Motif Finding

Zhizhuo Zhang Outline Review of Mixture Model and EM algorithm Importance Sampling Resampling EM Extending EM Integrate Other Features Result Review Motif Finding Mixture modeling Given a dataset . Simon Andrews. simon.andrews@babraham.ac.uk. @. simon_andrews. v. 1.0. 1. Rationale. 2. Gene A. Gene B. Gene C. Hit A. Hit B. Hit C. Prom A. Prom B. Prom C. GGATCC. GGATCC. GGATCC. Basic Questions. Does the sequence around my hits look unusual?. Machine Learning. Last Time. Expectation Maximization. Gaussian Mixture Models. Today. EM Proof. Jensen’s Inequality. Clustering sequential data. EM over . HMMs. EM in any Graphical Model. Gibbs Sampling. Mixture Models and Expectation Maximization. Machine Learning. Last Time. Review of Supervised Learning. Clustering. K-means. Soft K-means. Today. Gaussian Mixture Models. Expectation Maximization. The Problem. Machine . Learning . 10-601. , Fall . 2014. Bhavana. . Dalvi. Mishra. PhD student LTI, CMU. Slides are based . on materials . from . Prof. . Eric Xing, Prof. . . William Cohen and Prof. Andrew Ng. Stat2 120m. E-value 5.1e-202. Width 16. Sites 148 (200 peaks used). Jaspar. Stat1 motif. Stat1 120 . m. Jaspar. Stat1 motif. E-value 5.9e-319. Width 16. Sites 198 (200 peaks used). Irf1 (120 min). -value 3.3e-130. Maximum Likelihood (ML) Model. Introduction. . Alternative Splicing. Simulation Setup: . human genome data (UCSC hg18) . UCSC database - 66, 803 . isoforms. 19, 372 genes, Single error-free reads: 60M of length 100bp. Machine Learning. April 13, 2010. Last Time. Review of Supervised Learning. Clustering. K-means. Soft K-means. Today. A brief look at Homework 2. Gaussian Mixture Models. Expectation Maximization. The Problem. Rajhans Samdani. Joint work with. Ming-Wei Chang (. Microsoft Research. ) . and Dan Roth. University of Illinois at Urbana-Champaign. Page . 1. NAACL 2012,. Montreal. Weakly Supervised Learning in NLP. 1. Matt Gormley. Lecture . 24. November 21, 2016. School of Computer Science. Readings:. 10-601B Introduction to Machine Learning. Reminders. Final . Exam. in-. class. . Wed. ., . Dec. . 7. 2. Outline. Winter 2012. Daniel Weld. Slides adapted from Carlos . Guestrin. , Dan Klein & Luke . Zettlemoyer. Machine Learning. 2. Supervised Learning. Parametric. Reinforcement Learning. Unsupervised Learning. Out-of-School Youth. Roy Carr-Hill. Institute of Education, London. roy.carr_hill@yahoo.com. BRIEF. This is a very preliminary attempt to set out the difficulties of Tracking and Testing Out-of-School (henceforth . simon.andrews@babraham.ac.uk. @. simon_andrews. v. 1.0. 1. Rationale. 2. Gene A. Gene B. Gene C. Hit A. Hit B. Hit C. Prom A. Prom B. Prom C. GGATCC. GGATCC. GGATCC. Basic Questions. Does the sequence around my hits look unusual?. Motif 4. Motif 4. Motif 5. Motif 6. Supplementary . Figure . S2. . Amino acid sequence alignment of the five members of the Arabidopsis PAR1-family proteins (LAT1-5) shows a number of conserved regions and motifs throughout the protein sequence. Motifs were identified using Multiple . . The costs that an organization incurs even when there is little or no activity are . fixed costs. , or . overhead. .. Finding Marginal Cost. . Variable costs . are usually associated with labor and raw materials and change with the business’s rate of operation or output..