Probabilistic Modeling and Machine Learning in Structural and Systems Biology

RNA Structure Prediction Including Pseudoknots Based on Stochastic Multiple Context-Free Grammar

author: Yuki Kato, Nara Institute of Science and Technology

Description

Several grammars have been proposed for modeling RNA pseudoknotted structure. In this paper, we focus on multiple contextfree grammars (MCFGs), which are natural extension of context-free grammars and can represent pseudoknots, and extend a specific subclass of MCFGs to a probabilistic model called SMCFG.

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*Slides*
0:00	RNA Structure Prediction Including PseudoknotsBased on Stochastic Multiple Context-Free Grammar
0:28	NAIST
0:33	Table of Contents
1:08	RNA Secondary Structure: Stem-Loop
1:56	Modeling RNA Secondary Structure by Context-Free Grammar (CFG)
2:49	RNA Secondary Structure: Pseudoknot
3:22	Early Studies
4:23	Early Studies (cont.)
4:50	Motivation
5:34	What’s New in the Present Work
6:14	Early Studies and Present Work
6:38	Table of Contents
6:44	Relation between SMCFG and Major Probabilistic Models
7:47	From HMM to SCFG
8:30	Stochastic Multiple Context-Free Grammar (SMCFG)
9:37	Functions of SMCFG
10:03	Rules of SMCFG
10:43	Derivation Trees in SMCFG
11:24	Modeling Pseudoknot by SMCFG
12:35	SMCFG for RNA Pseudoknot Modeling
13:18	SMCFG Gs
13:53	Table of Contents
14:02	Algorithms for SMCFG
14:53	CYK Algorithm
15:36	CYK Algorithm (cont.)
16:05	Algorithm [CYK]
16:37	Algorithm [CYK] (cont.)
17:02	Algorithm [CYK] (cont.)
17:39	Complexity of CYK Algorithm
18:10	Table of Contents
18:18	Experimental Method
19:00	Data Sets for Experiments
19:22	Corona_pk_3 in Rfam ver. 7.0
19:48	HDV_ribozyme in Rfam ver. 7.0
19:57	Tombus_3_IV in Rfam ver. 7.0
20:20	Evaluation for Prediction Results
20:57	Experimental Results
21:22	Experimental Results (cont.)
21:31	Pair Stochastic Tree Adjoining Grammar (PSTAG)[MSS05]
22:09	Comparison with PSTAG
22:34	Summary

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