scholarly journals Improving the prediction of disulfide bonds in Eukaryotes with machine learning methods and protein subcellular localization

2011 ◽  
Vol 27 (16) ◽  
pp. 2224-2230 ◽  
Author(s):  
Castrense Savojardo ◽  
Piero Fariselli ◽  
Monther Alhamdoosh ◽  
Pier Luigi Martelli ◽  
Andrea Pierleoni ◽  
...  
Keyword(s):  
Machine Learning ◽  
Subcellular Localization ◽  
Disulfide Bonds ◽  
Protein Subcellular Localization ◽  
Learning Methods ◽  
Machine Learning Methods

Protein transport : bioinformatics methods for understanding protein subcellular localization

2018 ◽  
Author(s):  
◽  
Ning Zhang
Keyword(s):  
Machine Learning ◽  
Subcellular Localization ◽  
Protein Transport ◽  
Protein Localization ◽  
Mitochondrial Protein ◽  
Computational Prediction ◽  
Protein Subcellular Localization ◽  
Learning Methods ◽  
Web Resource ◽  
Machine Learning Methods

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Eukaryotic cells contain diverse subcellular organelles. These organelles form distinct functional cellular compartments where different biological processes and functions are carried out. The accurate translocation of a protein is crucial to establish and maintain cellular organization and function. Newly synthesized proteins are transported to different cellular components with the assistance of protein transport machineries and complex targeting signals. Mis-localization of proteins is often associated with metabolic disorders and diseases. Compared with experimental methods, computational prediction of protein localization, utilizing different machine learning methods, provides an efficient and effective way for studying the protein subcellular localization on the whole-proteome level. Here, we present in this dissertation the bioinformatics methods for studying protein subcellular localization. We reviewed the studies of protein subcellular transport and machine learning methods in bioinformatics, presented our work on mitochondrial protein targeting prediction in plants, summarized the ongoing development of a web-resource for protein subcellular localization, and discussed the future work and development.

scholarly journals In silico prediction of chemical subcellular localization via multi-classification methods

MedChemComm ◽  
2017 ◽  
Vol 8 (6) ◽  
pp. 1225-1234 ◽  
Author(s):  
Hongbin Yang ◽  
Xiao Li ◽  
Yingchun Cai ◽  
Qin Wang ◽  
Weihua Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Subcellular Localization ◽  
Small Molecules ◽  
In Silico ◽  
Classification Models ◽  
Classification Methods ◽  
In Silico Prediction ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Multi Classification

Multi-classification models were developed for prediction of subcellular localization of small molecules by machine learning methods.

Application of machine learning methods for automatic interpretation of open hole logging data

2020 ◽  
pp. 44-47
Author(s):  
M.A. Basyrov ◽  
◽  
A.V. Akinshin ◽  
I.R. Makhmutov ◽  
Yu.D. Kantemirov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Automatic Interpretation ◽  
Open Hole
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