Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9754
Title: MLASSO-Hum: A LASSO-based interpretable human-protein subcellular localization predictor
Authors: Wan, S
Mak, MW 
Kung, SY
Issue Date: 2015
Source: Journal of theoretical biology, 2015, v. 382, p. 223-234
Abstract: Knowing the subcellular compartments of human proteins is essential to shed light on the mechanisms of a broad range of human diseases. In computational methods for protein subcellular localization, knowledge-based methods (especially gene ontology (GO) based methods) are known to perform better than sequence-based methods. However, existing GO-based predictors often lack interpretability and suffer from overfitting due to the high dimensionality of feature vectors. To address these problems, this paper proposes an interpretable multi-label predictor, namely mLASSO-Hum, which can yield sparse and interpretable solutions for large-scale prediction of human protein subcellular localization. By using the one-vs-rest LASSO-based classifiers, 87 out of more than 8000 GO terms are found to play more significant roles in determining the subcellular localization. Based on these 87 essential GO terms, we can decide not only where a protein resides within a cell, but also why it is located there. To further exploit information from the remaining GO terms, a method based on the GO hierarchical information derived from the depth distance of GO terms is proposed. Experimental results show that mLASSO-Hum performs significantly better than state-of-the-art predictors. We also found that in addition to the GO terms from the cellular component category, GO terms from the other two categories also play important roles in the final classification decisions.
Keywords: Depth-dependent information
Interpretable prediction
Multi-label classification
Protein subcellular localization
Sparse solutions
Publisher: Academic Press
Journal: Journal of Theoretical Biology 
ISSN: 0022-5193
DOI: 10.1016/j.jtbi.2015.06.042
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

17
Last Week
0
Last month
0
Citations as of Sep 4, 2020

WEB OF SCIENCETM
Citations

17
Last Week
0
Last month
0
Citations as of Sep 15, 2020

Page view(s)

132
Last Week
0
Last month
Citations as of Sep 16, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.