Author Description

!Doctype html public ""-//w3c//dtd html 4.0 transitional//en"" meta http-equiv=content-type content=""text/html; charset=iso-8859-1"" meta content=""mshtml 6.00.6000.21371"" name=generator For bioinformaticians, computational biologists, and wet-lab biologists, the authors provide the latest machine learning approaches for protein subcellular localization prediction with a systemic scheme for improving predictors performance.