1. Yongjin Park, Stanley Shackney, and Russell Schwartz 2008.10.21 Accepted Computational Biology and Bioinformatics

2. Overview  Introduction  Method Uncorrected method Optimization method Sampling method Modular method  Result

3. Introduction

4. Methods  Uncorrected method  Optimization method  Sampling method  Modular method

5. Uncorrected method  Without any network-based correction  minimum spanning tree (MST) problem

6. Optimization method  regression equations’ form  Solving the following quadratic programming problem

7. Sampling method  Posterior probability of possible structures using Markov Chain Monte Carlo (MCMC) method

8. Sampling method(Cont.)  posterior probability of features can be estimated by Metropolis-Hastings algo.  the expression  likelihood

9. Modular method  Dirichlet process mixture  I ∗ −1 genes to K ∗ modules  probability of I ∗ -th gene belonging to one of the K ∗ currently known modules  the I ∗ -th gene could be the first member of a newly generated K ∗ +1-th module

10. Result  Data set Lung Cancer ○ Normal cell ○ Adenocarcinoma ○ Small cell ○ Large cell neuroendocrine carcinoid ○ Large cell

13.  Z score uncorrected = −7.5 optimization = −10.6 modular = −10.4 sampling = −11.7  False Negative