Title:An Overview on Predicting Protein Subchloroplast Localization by using Machine Learning Methods
VOLUME: 21 ISSUE: 12
Author(s):Meng-Lu Liu , Wei Su , Zheng-Xing Guan , Dan Zhang , Wei Chen *, Li Liu * and Hui Ding *
Affiliation:Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054
Keywords:Protein, subchloroplast localization, machine learning method, protein sequence properties, feature selection, dataset.
Abstract:The chloroplast is a type of subcellular organelle of green plants and eukaryotic algae,
which plays an important role in the photosynthesis process. Since the function of a protein correlates
with its location, knowing its subchloroplast localization is helpful for elucidating its functions. However,
due to a large number of chloroplast proteins, it is costly and time-consuming to design biological
experiments to recognize subchloroplast localizations of these proteins. To address this problem, during
the past ten years, twelve computational prediction methods have been developed to predict protein
subchloroplast localization. This review summarizes the research progress in this area. We hope the
review could provide important guide for further computational study on protein subchloroplast localization.
