Machine Learning Methods For Protein Analyses
published: Oct. 5, 2009, recorded: September 2009, views: 321
Report a problem or upload filesIf 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.
Computational biologists, and biologists more generally, spend a lot of time trying to more fully characterize proteins. In this talk, I will describe several of our recent efforts to use machine learning methods to gain a better understanding of proteins. First, we tackle one of the oldest problems in computational biology, the recognition of distant evolutionary relationships among protein sequences. We show that by exploiting a global protein similarity network, coupled with a latent space embedding, we can detect remote protein homologs more accurately than state-of-the-art methods such as PSI-BLAST and HHPred. Second, we use machine learning methods to improve our ability to identify proteins in complex biological samples on the basis of shotgun proteomics data. I will describe two quite different approaches to this problem, one generative and one discriminative.
Download slides: mlsb09_noble_mlmfpa_01.v1.pdf (2.0 MB)
Download slides: mlsb09_noble_mlmfpa_01.ppt (6.3 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !