Hub Node Detection in Directed Acyclic Graphs

Slides:

Advertisements

Similar presentations

CS188: Computational Models of Human Behavior

Advertisements

Bayesian network for gene regulatory network construction

Gated Graphs and Causal Inference

Ricard V. Solè and Sergi Valverde Prepared by Amaç Herdağdelen

Scale Free Networks.

Statistical perturbation theory for spectral clustering Harrachov, 2007 A. Spence and Z. Stoyanov.

A Tutorial on Learning with Bayesian Networks

DREAM4 Puzzle – inferring network structure from microarray data Qiong Cheng.

1 Chapter 5 Belief Updating in Bayesian Networks Bayesian Networks and Decision Graphs Finn V. Jensen Qunyuan Zhang Division. of Statistical Genomics,

An Introduction to the EM Algorithm Naala Brewer and Kehinde Salau.

Emergence of Scaling in Random Networks Albert-Laszlo Barabsi & Reka Albert.

Michael Alves, Patrick Dugan, Robert Daniels, Carlos Vicuna

1 Some Comments on Sebastiani et al Nature Genetics 37(4)2005.

Huffman code and ID3 Prof. Sin-Min Lee Department of Computer Science.

Farnoush Banaei-Kashani and Cyrus Shahabi Criticality-based Analysis and Design of Unstructured P2P Networks as “ Complex Systems ” Mohammad Al-Rifai.

GS 540 week 6. HMM basics Given a sequence, and state parameters: – Each possible path through the states has a certain probability of emitting the sequence.

Non-Linear Problems General approach. Non-linear Optimization Many objective functions, tend to be non-linear. Design problems for which the objective.

Support Vector Machines and Kernel Methods

1 Data Persistence in Large-scale Sensor Networks with Decentralized Fountain Codes Yunfeng Lin, Ben Liang, Baochun Li INFOCOM 2007.

More routing protocols Alec Woo June 18 th, 2002.

1Causality & MDL Causal Models as Minimal Descriptions of Multivariate Systems Jan Lemeire June 15 th 2006.

Graph, Search Algorithms Ka-Lok Ng Department of Bioinformatics Asia University.

Network analysis and applications Sushmita Roy BMI/CS 576 Dec 2 nd, 2014.

. Applications and Summary. . Presented By Dan Geiger Journal Club of the Pharmacogenetics Group Meeting Technion.

Systematic Analysis of Interactome: A New Trend in Bioinformatics KOCSEA Technical Symposium 2010 Young-Rae Cho, Ph.D. Assistant Professor Department of.

Bayes Net Perspectives on Causation and Causal Inference

Large-scale organization of metabolic networks Jeong et al. CS 466 Saurabh Sinha.

Optimization Based Modeling of Social Network Yong-Yeol Ahn, Hawoong Jeong.

Summary of the Bayes Net Formalism David Danks Institute for Human & Machine Cognition.

6. Experimental Analysis Visible Boltzmann machine with higher-order potentials: Conditional random field (CRF): Exponential random graph model (ERGM):

Using Dijkstra’s Algorithm to Find a Shortest Path from a to z 1.

Learning Structure in Bayes Nets (Typically also learn CPTs here) Given the set of random variables (features), the space of all possible networks.

Chapter 10. Sampling Strategy for Building Decision Trees from Very Large Databases Comprising Many Continuous Attributes Jean-Hugues Chauchat and Ricco.

Lecture 8: 24/5/1435 Genetic Algorithms Lecturer/ Kawther Abas 363CS – Artificial Intelligence.

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

Siddhartha Shakya1 Estimation Of Distribution Algorithm based on Markov Random Fields Siddhartha Shakya School Of Computing The Robert Gordon.

Machine Learning, Decision Trees, Overfitting Machine Learning Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 14,

3. SMALL WORLDS The Watts-Strogatz model. Watts-Strogatz, Nature 1998 Small world: the average shortest path length in a real network is small Six degrees.

Module networks Sushmita Roy BMI/CS 576 Nov 18 th & 20th, 2014.

Coding Theory Efficient and Reliable Transfer of Information

Genome Biology and Biotechnology The next frontier: Systems biology Prof. M. Zabeau Department of Plant Systems Biology Flanders Interuniversity Institute.

Dependency networks Sushmita Roy BMI/CS 576 Nov 25 th, 2014.

341- INTRODUCTION TO BIOINFORMATICS Overview of the Course Material 1.

Sampling Theory and Some Important Sampling Distributions.

March 3, 2009 Network Analysis Valerie Cardenas Nicolson Assistant Adjunct Professor Department of Radiology and Biomedical Imaging.

1 CMSC 671 Fall 2001 Class #20 – Thursday, November 8.

Random Geometric Graph Model Model for ad hoc/sensor networks n nodes placed in d-dim space Connectivity threshold r Two nodes u,v connected iff ||u-v||

Sporadic model building for efficiency enhancement of the hierarchical BOA Genetic Programming and Evolvable Machines (2008) 9: Martin Pelikan, Kumara.

Computational methods for inferring cellular networks II Stat 877 Apr 17 th, 2014 Sushmita Roy.

Network applications Sushmita Roy BMI/CS 576 Dec 9 th, 2014.

Dependency Networks for Inference, Collaborative filtering, and Data Visualization Heckerman et al. Microsoft Research J. of Machine Learning Research.

Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Network Biology.

Genetic Algorithm(GA)

Presented By: Farid, Alidoust Vahid, Akbari 18 th May IAUT University – Faculty.

Structures of Networks

Sequential Algorithms for Generating Random Graphs

Assessing Hierarchical Modularity in Protein Interaction Networks

DNA Computing and Molecular Programming

Discrete Kernels.

Building and Analyzing Genome-Wide Gene Disruption Networks

Overview: Fault Diagnosis

Design of Hierarchical Classifiers for Efficient and Accurate Pattern Classification M N S S K Pavan Kumar Advisor : Dr. C. V. Jawahar.

Department of Computer Science University of York

Pattern Recognition and Image Analysis

Simulating Systems Genetics data (An update on SysGenSIM without technical details: those will be provided by Andrea.

Forest Learning from Data

Section 11.7 Probability.

Lecture 6 Shortest Path Problem.

Genetic Algorithm Soft Computing: use of inexact t solution to compute hard task problems. Soft computing tolerant of imprecision, uncertainty, partial.

Presentation transcript:

Hub Node Detection in Directed Acyclic Graphs 2008. 3. 11. Kim, Byoung-Hee I will talk about evolving DNA-encoded genetic programs in a test tube. We evaluate the potentials of this approach by solving a medical diagnosis problem on a simulated DNA computer. The individual genetic program represents a decision list of variable length and the whole population takes part in making probabilistic decisions.

Directions of Future Work on HBN Algorithm Extension from binary-tree type structure to n-ary tree Fast and effective learning by hub node detection Incorporate features of latent variable models Theoretical Analysis Information compression – efficiency, measurement Application & Empirical Analysis Biological networks – gene regulatory networks, metabolic networks Text Social networks

Preliminary Experiment for Detecting Hub Nodes … s5000 X1 X2 X100 sampling Estimation Structure Hub nodes Mutual information / Conditional MI 100 node scale-free/modular BN all nodes have binary states Conjectures D-separation vs conditionam mutual information d(x, y) > d(x, z) then I(x; y) < I(x; z) asymptotically (mutual information) the difference between I(X; Y) and I(X; Y|Z) increases as the degree of Z increases

Shortest path vs normalized mutual information Data: 5,000 samples out of 100 node networks (1 scale free and 1 modular) d(x, y) > d(x, z) then I(x; y) < I(x; z) asymptotically ????

Example 1: some short paths btw X65 and X86 72 0.000905 49 0.05604 84 7 0.08434 2 0.0693 0.0759 I(X65; X86) = 0.082375 31 28 1 I(X65; X86 | Z) =? 0.0859 0.0821 0.07947 26 17 0.0829 0.083

Example 2: some short paths btw X7 and X31 65 86 72 0.0177 0.023 0.0183 49 0.0224 84 7 0.0545 2 I(X7; X31) = 0.0194 0.00453 I(X7; X31 | Z) = ? 31 28 1 0.00703 0.00895 26 17 0.00754 0.0122

Experimetns to Verify Conjectures d(x, y) > d(x, z) then I(x, y) < I(x, z) asymptotically Shortest path vs normalized mutual information Data: 5,000 samples out of 100 node networks (1 scale free and 1 modular)

Mutual Information In probability theory and information theory, the mutual information, or transinformation, of two random variables is a quantity that measures the mutual dependency of the two variables

Updates Code for the conditional MI has a bug Couldn’t find any function for calculating MI/cMI in Matlab and R ‘empirical’ MI/cMI calculation: biased.. Need to reduce bias