Learning to translate sequence and structure to function: identifying DNA binding and membrane binding proteins.

ABSTRACT: A protein's function depends in a large part on interactions with other molecules. With an increasing number of protein structures becoming available every year, a corresponding structural annotation approach identifying such interactions grows more expedient. At the same time, machine learning has gained popularity in bioinformatics providing robust annotation of genes and proteins without sequence homology. Here we have developed a general machine learning protocol to identify proteins that bind DNA and membrane. In general, there is no theory or even rule of thumb to pick the best machine learning algorithm. Thus, a systematic comparison of several classification algorithms known to perform well is investigated. Indeed, the boosted tree classifier is found to give the best performance,

SUBMITTER: Langlois RE 

PROVIDER: S-EPMC2706547 | biostudies-literature | 2007 Jun

REPOSITORIES: biostudies-literature