Paper Info
Title
Deciphering the function of unknown cytosolic proteins using hyperparameter-tuned random forest.
Abstract
Knowing the roles of unknown proteins is vigorous to understand the cellular processes of parasite and the cause of disease progression. But it is highly challenging to decipher the function of an unknown protein. There are limited methods accessible for the prediction of an unknown protein function. We have used a hyper-tuned random forest that is a promising method of the classifier for reliable function predictions of an unknown protein. This method is tested for the prediction of some unknown cytosolic proteins of Leishmania donovani identified in our previous mass spectrometry-based proteomics study. L. donovani is a protozoan parasite that causes Visceral Leishmaniasis (VL: a fatal disease) in humans around the globe. Using Random Forest (RF) classifier, the obtained results in this study indicated that this method predicts the function of the unknown protein with higher precision and significance. We have employed this model to provide predictions 98% to know the role of the unknown proteins in cytoplasmic protein pool of L. donovani. This study reported the function of the unknown proteins of L. donovani which is imperative information for this parasite. They could be an important and promising target for new drug discovery vaccine candidate development. Further characterization and in-depth study of these unknown proteins may open a gate for successful therapy of fatal VL.
Year
DOI
Venue
2020
10.1007/s13721-019-0208-2
Network Modeling Analysis in Health Informatics and Bioinformatics
Keywords
DocType
Volume
Random forest, Function prediction, Unknown cytosolic protein, L. donovani , Drug target, Vaccine candidate
Journal
9
Issue
ISSN
Citations 
1
2192-6662
1
PageRank 
References 
Authors
0.35
0
2
Name
Order
Citations
PageRank
Pradeep Singh1175.62
Awanish Kumar211.71