Probabilistic Modeling and Machine Learning in Structural and Systems Biology

The Challenge of Predicting Gene Function

author: Ross D. King, University of Wales

Description

The biological sciences are undergoing an explosion in the amount of available data. New data analysis methods are needed to deal with the data. A central problem in bioinformatics is the assignment of function to sequenced open reading frames (ORFs). The most common approach is based on inferred homology using a statistically based se- quence similarity (SIM) method e.g. PSI-BLAST.

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*Slides*
0:00	The Challenge of Predicting Gene Function
1:39	Gene Function Prediction
2:54	Data Mining Prediction
3:36	Predicting Gene Function in Yeast
4:44	We Want to Map From Sequence to Function Class
6:34	Classification Schemes 1
8:29	Classification Schemes 2
8:47	Classification Schemes 3
9:31	Sequence Data
10:19	Homology data
12:13	Predicted Secondary Structure Data
13:14	Expression Data
13:40	Phenotype Data
15:08	What are the Machine Learning Issues
17:38	Relational vs Propositional
18:28	Data Mining Prediction (DMP) pt 1
19:08	Warmr
20:44	PolyFARM
22:24	Propositionalisation
23:18	Dichotomic Search 1
23:43	Dichotomic Search 2
24:48	Data Mining Prediction (DMP) pt 2
25:03	C4.5
25:42	Data Mining Prediction (DMP) pt 2 (a)
26:01	Results pt 1
26:30	Accuracy Table
26:52	Expression Data Rule
27:30	Structure Rule
28:29	Alignment pt 1
28:34	Alignment pt 2
28:46	Homology Rule
29:16	Application of DMP to Bacterial Genomes
30:22	Example Rule (level 2 E. coli)
31:42	Experimental Conformation
32:14	“Wet” Biology Conformation
32:39	Confirmation of “Wet” Predictions
32:46	Extension to Arabidopsis Genome
34:32	Results pt 2
34:45	Gibberellin Biosynthesis Prediction pt 1
35:55	Gibberellin Biosynthesis Prediction pt 2
35:59	Leaf Number
36:26	Average Leaf Number at 21 Days Expt 4
36:59	Availability
37:44	ILP 2005 Challenge 1
38:08	ILP 2005 Challenge 2
38:31	Propositional Approach
38:48	Conclusions
39:52	Acknowledgements
40:47	PolyFARM (a)

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