Preservation of Statistically Significant patterns in Multiresolution 0-1 Data

author: Prem Raj Adhikari, Department of Computer Science and Engineering, Aalto University
published: Oct. 14, 2010,   recorded: September 2010,   views: 91
Categories

Slides

Related Open Educational Resources

Related content

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.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

Measurements in biology are made with high throughput and high resolution techniques often resulting in data in multiple resolutions. Currently, available standard algorithms can only handle data in one resolution. Generative models such as mixture models are often used to model such data. However, significance of the patterns generated by generative models has so far received inadequate attention. This paper analyses the statistical significance of the patterns preserved in sampling between different resolutions and when sampling from a generative model. Furthermore, we study the effect of noise on the likelihood with respect to the changing resolutions and sample size. Finite mixture of multivariate Bernoulli distribution is used to model amplification patterns in cancer in multiple resolutions. Statistically significant itemsets are identified in original data and data sampled from the generative models using randomization and their relationships are studied. The results showed that statistically significant itemsets are effectively preserved by mixture models. The preservation is more accurate in coarse resolution compared to the finer resolution. Furthermore, the effect of noise on data on higher resolution and with smaller number of sample size is higher than the data in lower resolution and with higher number of sample size.

See Also:

Download slides icon Download slides: prib2010_adhikari_pssp_01.pdf (316.3┬áKB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: