EPSRC Winter School in Mathematics for Data Modelling

Modelling Genomic Data

author: Mahesan Niranjan, University of Sheffield

Description

This presentation describes work on the modelling of genomic data.

You might be experiencing some problems with Your Video player.

*Slides*
0:00	Analysing Genomic Data
0:46	Classification, Regression & Online Problems
6:02	Sampling Methods: Bayesian Inference
7:03	Importance Sampling
7:07	Particle Filters
8:22	My Research Ambition for the Next Few Years
9:15	Basic Biology: Cell
9:35	Basic Biology: DNA
11:31	Basic Biology: Gene
11:47	Basic Biology: Protein - 1
12:05	Basic Biology: Protein - 2
12:06	Some Further Complexities - 1
13:17	Some Further Complexities - 2
14:36	Two Approaches to Pattern Recognition (Classification)
17:23	Curse of Dimensionality
19:08	Two Approaches to Pattern Recognition (Classification)
20:45	Curse of Dimensionality
20:53	Where CoD Doesn’t Hurt as Much
21:46	Support Vector Machines
21:52	- Nonlinear Kernel Functions
21:56	Classifier Performance - 1
22:42	Classifier Performance - 2
24:04	Classifier Performance - 3
26:52	Convex Hull of ROC Curves
28:23	PARCEL: Feature Subset Selection
29:40	Yeast Gene Classification
36:00	Now for Something Different … - 1
36:08	Basics: Transcriptome and Proteome - 1
38:09	Basics: Transcriptome and Proteome - 2
38:41	Two Views of the Same Dataset
39:15	Regulation
40:57	Data-Driven Models / Machine Learning
42:04	Model "Failure" Can Be Informative I
44:21	Model "Failure" Can Be Informative II
46:23	Data-Driven Models / Machine Learning
46:35	Predictor for Protein Concentrations
49:17	Improvement in Prediction (Small, but Exists)
49:44	- Questions
52:13	Some Encouraging Results?
54:33	- Questions
56:51	Precision: 2.4601 or 2.0?
61:01	Classification
61:34	Clustering - 1
63:50	Clustering - 2
65:12	Clustering - 3
67:09	Periodic Expression: Cell Cycle - 1
67:10	Periodic Expression: Cell Cycle - 2
67:11	Periodic Expression: Cell Cycle - 3
67:17	We May Be Able to Work with Lower Precision…So What?

Lecture rating

People found this lecture:
Worth seeing
because it is:
Valuable and informative
Well presented
Easily understandable
Acceptably recorded
You need to login to cast your vote.

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.