Essential Protein Prediction Based on node2vec and XGBoost-Reference-Cited by-同舟云学术

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Essential Protein Prediction Based on node2vec and XGBoost

Published:2021-07-01 Issue:7 Volume:28 Page:687-700
ISSN:1557-8666
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Wang Nian¹,Zeng Min¹,Li Yiming¹,Wu Fang-xiang²³,Li Min¹

Affiliation:

1. School of Computer Science and Engineering, Central South University, Changsha, P.R. China.

2. Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada.

3. Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada.

Publisher

Mary Ann Liebert Inc

Subject

Computational Theory and Mathematics,Computational Mathematics,Genetics,Molecular Biology,Modeling and Simulation

Link

https://www.liebertpub.com/doi/pdf/10.1089/cmb.2020.0543

Reference45 articles.

1. Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

2. COMPARTMENTS: unification and visualization of protein subcellular localization evidence

3. Power and Centrality: A Family of Measures

4. Radar emitter classification for large data set based on weighted‐xgboost

5. OGEE: an online gene essentiality database

Cited by 18 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. ACDMBI: A deep learning model based on community division and multi-source biological information fusion predicts essential proteins;Computational Biology and Chemistry;2024-10

2. FRP-XGBoost: Identification of ferroptosis-related proteins based on multi-view features;International Journal of Biological Macromolecules;2024-03

3. SpaCCC: Large language model-based cell-cell communication inference for spatially resolved transcriptomic data;2024-02-23

4. Identification of Human Drug Targets for COVID-19 Based on Subcellular Localization Information, Gene Expression Data, and Node2vec;Lecture Notes in Networks and Systems;2024

5. MEM‐FET: Essential protein prediction using membership feature and machine learning approach;Proteins: Structure, Function, and Bioinformatics;2023-08-28

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