Author: Jim C. Huang etc. Lecturer: Dong Yue Director: Dr. Yufei Huang.

Slides:

Advertisements

Similar presentations

CHAPTER 8 More About Estimation. 8.1 Bayesian Estimation In this chapter we introduce the concepts related to estimation and begin this by considering.

Advertisements

D ISCOVERING REGULATORY AND SIGNALLING CIRCUITS IN MOLECULAR INTERACTION NETWORK Ideker Bioinformatics 2002 Presented by: Omrit Zemach April Seminar.

Improving miRNA Target Genes Prediction Rikky Wenang Purbojati.

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 10: The Bayesian way to fit models Geoffrey Hinton.

A Probabilistic Dynamical Model for Quantitative Inference of the Regulatory Mechanism of Transcription Guido Sanguinetti, Magnus Rattray and Neil D. Lawrence.

27/06/2005ISMB 2005 GenXHC: A Probabilistic Generative Model for Cross- hybridization Compensation in High-density Genome-wide Microarray Data Joint work.

04/02/2006RECOMB 2006 Detecting MicroRNA Targets by Linking Sequence, MicroRNA and Gene Expression Data Joint work with Quaid Morris (2) and Brendan Frey.

Statistical model for count data Speaker : Tzu-Chun Lo Advisor : Yao-Ting Huang.

Genome-wide prediction and characterization of interactions between transcription factors in S. cerevisiae Speaker: Chunhui Cai.

Detecting Differentially Expressed Genes Pengyu Hong 09/13/2005.

DNA Microarray Bioinformatics - #27612 Normalization and Statistical Analysis.

Regulatory Network (Part II) 11/05/07. Methods Linear –PCA (Raychaudhuri et al. 2000) –NIR (Gardner et al. 2003) Nonlinear –Bayesian network (Friedman.

CS 8751 ML & KDDEvaluating Hypotheses1 Sample error, true error Confidence intervals for observed hypothesis error Estimators Binomial distribution, Normal.

Gene Co-expression Network Analysis BMI 730 Kun Huang Department of Biomedical Informatics Ohio State University.

Machine Learning CMPT 726 Simon Fraser University CHAPTER 1: INTRODUCTION.

Similar Sequence Similar Function Charles Yan Spring 2006.

Data Mining CS 341, Spring 2007 Lecture 4: Data Mining Techniques (I)

Edpsy 511 Homework 1: Due 2/6.

Introduction to molecular networks Sushmita Roy BMI/CS 576 Nov 6 th, 2014.

Computer vision: models, learning and inference

Assigning Numbers to the Arrows Parameterizing a Gene Regulation Network by using Accurate Expression Kinetics.

Multivariate Data and Matrix Algebra Review BMTRY 726 Spring 2012.

Gaussian Processes for Transcription Factor Protein Inference Neil D. Lawrence, Guido Sanguinetti and Magnus Rattray.

Bayesian integration of biological prior knowledge into the reconstruction of gene regulatory networks Dirk Husmeier Adriano V. Werhli.

1 Bayesian methods for parameter estimation and data assimilation with crop models Part 2: Likelihood function and prior distribution David Makowski and.

Practical Issues in Microarray Data Analysis Mark Reimers National Cancer Institute Bethesda Maryland.

Maximum likelihood estimation of relative transcript abundances Advanced bioinformatics 2012.

MicroRNA Targets Prediction and Analysis. Small RNAs play important roles The Nobel Prize in Physiology or Medicine for 2006 Andrew Z. Fire and Craig.

1 A Presentation of ‘Bayesian Models for Gene Expression With DNA Microarray Data’ by Ibrahim, Chen, and Gray Presentation By Lara DePadilla.

Detection and Compensation of Cross- Hybridization in DNA Microarray Data Joint work with Quaid Morris (1), Tim Hughes (2) and Brendan Frey (1) (1)Probabilistic.

Bayesian inference review Objective –estimate unknown parameter  based on observations y. Result is given by probability distribution. Bayesian inference.

The Normal Probability Distribution

MicroRNA implication in urinary bladder cancer A. Zaravinos 1, J. Radojicic 3, G.I. Lambrou 2, D. Volanis 1,4, D. Delakas 4, E.N. Stathopoulos 3, D.A.

Inferring transcriptional and microRNA-mediated regulatory programs in glioblastma Setty, M., et al.

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 11: Bayesian learning continued Geoffrey Hinton.

Using Bayesian Networks to Analyze Whole-Genome Expression Data Nir Friedman Iftach Nachman Dana Pe’er Institute of Computer Science, The Hebrew University.

Mixture Models, Monte Carlo, Bayesian Updating and Dynamic Models Mike West Computing Science and Statistics, Vol. 24, pp , 1993.

MicroRNA regulation in Arabidopsis thaliana

CS5263 Bioinformatics Lecture 20 Practical issues in motif finding Final project.

PreDetector : Prokaryotic Regulatory Element Detector Samuel Hiard 1, Sébastien Rigali 2, Séverine Colson 2, Raphaël Marée 1 and Louis Wehenkel 1 1 Department.

Intel Confidential – Internal Only Co-clustering of biological networks and gene expression data Hanisch et al. This paper appears in: bioinformatics 2002.

Data Mining the Yeast Genome Expression and Sequence Data Alvis Brazma European Bioinformatics Institute.

Multi-Speaker Modeling with Shared Prior Distributions and Model Structures for Bayesian Speech Synthesis Kei Hashimoto, Yoshihiko Nankaku, and Keiichi.

Improving Intergenic miRNA Target Genes Prediction Rikky Wenang Purbojati.

Introduction to biological molecular networks

Guest lecture: Feature Selection Alan Qi Dec 2, 2004.

Markov Chain Monte Carlo for LDA C. Andrieu, N. D. Freitas, and A. Doucet, An Introduction to MCMC for Machine Learning, R. M. Neal, Probabilistic.

Bayes Theorem. Prior Probabilities On way to party, you ask “Has Karl already had too many beers?” Your prior probabilities are 20% yes, 80% no.

Project: Cellular microRNA profilings during the infections of the hepatitis C virus and the influenza A virus Yu Li Postdoc Fellow Katze Lab.

Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae Vu, T. T.,

MicroRNA Prediction with SCFG and MFE Structure Annotation Tim Shaw, Ying Zheng, and Bram Sebastian.

NCode TM miRNA Analysis Platform Identifies Differentially Expressed Novel miRNAs in Adenocarcinoma Using Clinical Human Samples Provided By BioServe.

Gaussian Process Networks Nir Friedman and Iftach Nachman UAI-2K.

PROTEIN INTERACTION NETWORK – INFERENCE TOOL DIVYA RAO CANDIDATE FOR MASTER OF SCIENCE IN BIOINFORMATICS ADVISOR: Dr. FILIPPO MENCZER CAPSTONE PROJECT.

Network Motifs See some examples of motifs and their functionality Discuss a study that showed how a miRNA also can be integrated into motifs Today’s plan.

Gene Set Enrichment Analysis. GSEA: Key Features Ranks all genes on array based on their differential expression Identifies gene sets whose member genes.

Bayesian Inference: Multiple Parameters

Inferring Regulatory Networks from Gene Expression Data BMI/CS 776 Mark Craven April 2002.

Down-regulated expression of family with sequence similarity 3, member B (FAM3B), in oral squamous cell carcinoma Masashi Shiiba, Shunsaku Ishige, Yasuhiro.

ANIMAL TARGET PREDICTION - TIPS

Chapter 7: Sampling Distributions

1 Department of Engineering, 2 Department of Mathematics,

1 Department of Engineering, 2 Department of Mathematics,

Characterization of microRNA transcriptome in tumor, adjacent, and normal tissues of lung squamous cell carcinoma Jun Wang, MD, PhD, Zhi Li, MD, PhD,

1 Department of Engineering, 2 Department of Mathematics,

Mathematical Foundations of BME Reza Shadmehr

Edwards Allen, Zhixin Xie, Adam M. Gustafson, James C. Carrington Cell

Volume 3, Issue 5, Pages e13 (November 2016)

Quantitative analyses using RNA-seq data

Brandon Ho, Anastasia Baryshnikova, Grant W. Brown Cell Systems

Presentation transcript:

Author: Jim C. Huang etc. Lecturer: Dong Yue Director: Dr. Yufei Huang

Outline The background of miRNA. The biology model A Bayesian model for mRNA regulation Algorithm evaluation Validating GenMiR++-predicted let-7b targets

What is miRNA? MicroRNAs (miRNA) are single-stranded RNA molecules of about nucleotides in length thought to regulate the expression of other genes.

The Process of miRNA

A movie to explain miRNA.

Biology Problem  Biologist can not do experiment for all mRNAs and miRNAs.  Some many targets been predicted by TargetScan which is a very popular miRNA target algorithm.  TargetScan use sequence level data, can we use so other kind of data?

Microarray data  Red(solid) curve means the expression of mRNA which was repressed by miRNA in specific tissue.  Blue(dashed) curve means the background distribution which is the normal expression mRNA.

Thinking the Model biologically for only one target  Looking at the miRNA target which is predicted by TargetScan. What is TargetScan.  2 If this miRNA is highly expressed in a given tissue?  3. Whether the expression of a targeted transcript is negatively shifted with respect to a background expression level.  If 2,3 is Yes, it is very likely a target in reality.

In situation of Multiple miRNAs  The down-regulation of target mRNAs can subject to the action of multiple miRNAs.  miRNA scores are given according to how much the miRNA expression profile contributed to explaining downregulation of the mRNA expression.

Process in general

Definition of Bayesian Model X and Z are the sets of expression profiles for mRNAs and miRNAs. C is the set of candidate miRNA targets. is a positive tissue scaling parameter which accounts for differences in hybridization conditions and normalization between the miRNA and mRNA expression data. Prior distribution

The goal of Bayesian Model Find the posterior probability:

Graphical model

A Bayesian model for mRNA regulation

Why we need an approximate method  Require integrating over the parameters  Sum over an exponential number of combinations of miRNA interactions per mRNA.

Variational Bayesian Learning of miRNA targets Observed variables v: X, Z, and C Unobserved variables u: S Model parameters η : Λ Γ The exact posterior is: The sorrogate distribution is: KL-divergence:

Variational Bayesian Learning of miRNA targets Define:  represents the probability that miRNA k targets mRNA g given the data.  represents the expected values of the regulatory weights.  represent the means and variances of the tissue scaling parameters.

Variational Bayesian Learning of miRNA targets

How to calculate the score

Algorithm evaluation What is fraction of targets detected? # of candidate interactions detected/ # of candidate interactions.

Validating GenMiR++-predicted let- 7b targets  Predict target for let-7b misregulation in retinoblastoma.  No neural tissue was represented in the expression data used to build GenMiR++.

Validating GenMiR++-predicted let- 7b targets  Use microarrays to profile 3 retinoblastoma samples and 1 healthy samples.  Let-7b was on average ~50- fold lower in abundance in retinoblastoma verss healthy retina.

Validating GenMiR++-predicted let- 7b targets

Reference [1] Jim C. Huang etc, Bayesian Inference of MicroRNA Targets from Sequence and Expression Data. J. Comput. Biol. 14, 550–563 (2007). [2] Jim C. Huang etc, Using Expression Profiling Data to Identify Human microRNA Targets, nature methods, VOL.4 NO.12, DEC 2007, p1045

Summarization The background of miRNA. The biology model A Bayesian model for mRNA regulation Algorithm evaluation Validating GenMiR++-predicted let-7b targets.

Questions Or Comments?