Literature DB >> 31957607

An Overview on Predicting Protein Subchloroplast Localization by using Machine Learning Methods.

Meng-Lu Liu1, Wei Su1, Zheng-Xing Guan1, Dan Zhang1, Wei Chen1, Li Liu2, Hui Ding1.   

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. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Entities:  

Keywords:  Protein; dataset; feature selection; machine learning method; protein sequence properties; subchloroplast localization

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Substances:

Year:  2020        PMID: 31957607     DOI: 10.2174/1389203721666200117153412

Source DB:  PubMed          Journal:  Curr Protein Pept Sci        ISSN: 1389-2037            Impact factor:   3.272


  7 in total

1.  Computational identification of N6-methyladenosine sites in multiple tissues of mammals.

Authors:  Fu-Ying Dao; Hao Lv; Yu-He Yang; Hasan Zulfiqar; Hui Gao; Hao Lin
Journal:  Comput Struct Biotechnol J       Date:  2020-04-30       Impact factor: 7.271

2.  Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network.

Authors:  Qingwen Li; Lei Xu; Qingyuan Li; Lichao Zhang
Journal:  Comput Math Methods Med       Date:  2020-10-18       Impact factor: 2.238

3.  A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD.

Authors:  Zhiyu Tao; Yanjuan Li; Zhixia Teng; Yuming Zhao
Journal:  Comput Math Methods Med       Date:  2020-10-19       Impact factor: 2.238

4.  iBLP: An XGBoost-Based Predictor for Identifying Bioluminescent Proteins.

Authors:  Dan Zhang; Hua-Dong Chen; Hasan Zulfiqar; Shi-Shi Yuan; Qin-Lai Huang; Zhao-Yue Zhang; Ke-Jun Deng
Journal:  Comput Math Methods Med       Date:  2021-01-07       Impact factor: 2.238

5.  Identifying Antioxidant Proteins by Using Amino Acid Composition and Protein-Protein Interactions.

Authors:  Yixiao Zhai; Yu Chen; Zhixia Teng; Yuming Zhao
Journal:  Front Cell Dev Biol       Date:  2020-10-29

6.  Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features.

Authors:  Xiao-Yang Jing; Feng-Min Li
Journal:  Comput Math Methods Med       Date:  2020-09-23       Impact factor: 2.238

7.  Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features.

Authors:  Feng-Min Li; Xiao-Wei Gao
Journal:  Biomed Res Int       Date:  2020-08-02       Impact factor: 3.411

  7 in total

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