Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization.

ABSTRACT: The accurate prediction of protein localization is a critical step in any functional genome annotation process. This paper proposes an improved strategy for protein subcellular localization prediction in plants based on multiple classifiers, to improve prediction results in terms of both accuracy and reliability. The prediction of plant protein subcellular localization is challenging because the underlying problem is not only a multiclass, but also a multilabel problem. Generally, plant proteins can be found in 10-14 locations/compartments. The number of proteins in some compartments (nucleus, cytoplasm, and mitochondria) is generally much greater than that in other compartments (vacuole, peroxisome, Golgi, and cell wall). Therefore, the problem of imbalanced data usually arises. Therefore

SUBMITTER: Wattanapornprom W 

PROVIDER: S-EPMC8066735 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature