'Motif discovery' presentation slideshows

Learning Interaction Protocols through imitation A data mining approach

Artificial Intelligence, Adv (E) (2013). Do not distribute beyond this class. Learning Interaction Protocols through imitation A data mining approach. Yasser Mohammad Nishida Lab. Situated Modules. Used in many systems until now mainly with Robovie Situated modules are executed in serial.

804 views • 67 slides

DETECTION OF REGULATORY MOTIFS BASED ON COEXPRESSION AND PHYLOGENETIC FOOTPRINTING

DETECTION OF REGULATORY MOTIFS BASED ON COEXPRESSION AND PHYLOGENETIC FOOTPRINTING. PhD presentation Valerie Storms March 29 th , 2011 Promoters Prof. Dr. Ir. Kathleen Marchal Prof. Dr. Ir Bart De Moor. Overview. Introduction on transcriptional regulation

733 views • 62 slides

Biological Motif Discovery

Biological Motif Discovery. Concepts Motif Modeling and Motif Information EM and Gibbs Sampling Comparative Motif Prediction Applications Transcription Factor Binding Site Prediction Epitope Prediction Lab Practical DNA Motif Discovery with MEME and AlignAce

870 views • 58 slides

From Expression to Regulation: the online analysis of microarray data

From Expression to Regulation: the online analysis of microarray data. Gert Thijs K.U.Leuven, Belgium ESAT-SCD. K.U.Leuven. Founded in 1425 Situated in the center of Belgium Some numbers: 25.000 students 2.500 researchers 1.000 professors University Hospital with 1.500 beds. ESAT-SCD.

627 views • 54 slides

Motif discovery and Phylogenetic trees

Motif discovery and Phylogenetic trees. Motif Discovery Challenges. How to recognize a known regulatory motif in genome scale? Can we discover new motifs within upstream sequences of genes?. Scenario 1 : Binding motif is known. Building a PSSM for short motifs.

539 views • 48 slides

Expectation-Maximization for HMMs and Motif Discovery

Expectation-Maximization for HMMs and Motif Discovery. Yves Moreau. Overview. The general Expectation-Maxization algorithm EM interpretation of the Baum-Welch algorithm for the learning of HMMs MEME for motif discovery. The general EM algorithm. EM algorithm. Maximum likelihood estimation

709 views • 43 slides

CSCE555 Bioinformatics

CSCE555 Bioinformatics. Lecture 10 Motif Discovery Meeting: MW 4:00PM-5:15PM SWGN2A21 Instructor: Dr. Jianjun Hu Course page: http://www.scigen.org/csce555. HAPPY CHINESE NEW YEAR. University of South Carolina Department of Computer Science and Engineering 2008 www.cse.sc.edu. Outline.

434 views • 35 slides

Computational Approaches for Understanding Biological Significance of Microarray Data

Spotted Microarray Workshop. Computational Approaches for Understanding Biological Significance of Microarray Data. Liangjiang (LJ) Wang ljwang@ksu.edu KSU Bioinformatics Center, Biology Division June, 2005. Sample acquisition. RNA: purification, labeling. Data acquisition.

428 views • 31 slides

Geometric Crossovers for Supervised Motif Discovery

Geometric Crossovers for Supervised Motif Discovery . Rolv Seehuus NTNU. Motivation and Scope. Try out the applicability of the geometric framework, on a supervised motif discovery problem Compare its merits to a previously used operator. In practice, we test on a very easy problem

363 views • 25 slides

Motif Discovery in Protein Sequences using Messy De Bruijn Graph

Motif Discovery in Protein Sequences using Messy De Bruijn Graph. Mehmet Dalkilic and Rupali Patwardhan. Goal. The goal of this project is to develop an algorithm that can take advantage of the properties of De Bruijn graphs for discovering motifs in protein sequences. Outline of Presentation.

316 views • 22 slides

Finding Time Series Motifs on Disk-Resident Data

Finding Time Series Motifs on Disk-Resident Data. Abdullah Mueen, Dr. Eamonn Keogh UC Riverside Nima Bigdely-Shamlo Swartz Center for Computational Neuroscience, UCSD. Outline. Motivation Time Series Motif DAME : Disk-Aware Motif Enumeration Performance Evaluation

342 views • 21 slides

DISCOVERING LARGER NETWORK MOTIFS

DISCOVERING LARGER NETWORK MOTIFS. Wooyoung Kim and Li Chen 4/24/2009 CSC 8910 Analysis of Biological Network , Spring 2009 Dr. Yi Pan. OUTLINE. Project Topic Related Works Proposed Ideas Unsolved Problems. PROJECT TOPIC. Discovering Larger Network Motifs

211 views • 20 slides

The AMADEUS Motif Discovery Platform

334 views • 19 slides

Pattern Discovery and Recognition for Genetic Regulation

Pattern Discovery and Recognition for Genetic Regulation. Tim Bailey UQ Maths and IMB. Research Goals. We are studying algorithms for discovering regulatory elements in DNA. Our research includes:. Developing fast and accurate methods for computing the statistics of random alignments

266 views • 18 slides

Algorithms for Regulatory Motif Discovery

Algorithms for Regulatory Motif Discovery. Xiaohui Xie University of California, Irvine. Positional weight matrix representation. Lambda cI/cro binding sites. Sequence Logo. Probabilistic model for motif discovery.

287 views • 18 slides

239 views • 10 slides

The Keogh Lab

Data Mining and Structure Retrieval. Presented by Abdullah Mueen. The Keogh Lab. Overview of our work. Our Goal: Extract information from raw, noisy, massive, unstructured data. We develop algorithms for Classification Clustering Rule finding Motif discovery Discord discovery

222 views • 8 slides