O'Reilly logo

Machine Learning for Protein Subcellular Localization Prediction by Man-Wai Mak, Shibiao Wan

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

6 Mining deeper on GO for protein subcellular localization

The methods described in the previous chapters use gene ontology (GO) information for single-label and multi-label protein subcellular localization prediction. Their performance is significantly better than the conventional methods that use non-GO features. However, these GO-based methods only use the occurrences of GO terms as features and disregard the relationships among the GO terms. This chapter describes some novel methods that mine deeper into the GO database for protein subcellular localization. The methods leverage not only the GO term occurrences, but also the interterm relationships. Some previous works related to semantic similarity are first presented. Then, a multi-label ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required