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Disclosing incoherent sparse and low-rank patterns inside homologous GPCR tasks for better modelling of ligand bioactivities |
Jiansheng WU1,2( ), Chuangchuang LAN1,2, Xuelin YE3, Jiale DENG4, Wanqing HUANG5, Xueni YANG1, Yanxiang ZHU6, Haifeng HU5 |
1. School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2. Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
3. Department of Statistics, University of Warwick, Coventry CV47AL, United Kingdom
4. Modern Economics & Management College, Jiangxi University of Finance and Economics, Nanchang 330013, China
5. School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
6. Verimake Research, Nanjing Qujike Info-tech Co., Ltd., Nanjing 210088, China |
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Abstract There are many new and potential drug targets in G protein-coupled receptors (GPCRs) without sufficient ligand associations, and accurately predicting and interpreting ligand bioactivities is vital for screening and optimizing hit compounds targeting these GPCRs. To efficiently address the lack of labeled training samples, we proposed a multi-task regression learning with incoherent sparse and low-rank patterns (MTR-ISLR) to model ligand bioactivities and identify their key substructures associated with these GPCRs targets. That is, MTR-ISLR intends to enhance the performance and interpretability of models under a small size of available training data by introducing homologous GPCR tasks. Meanwhile, the low-rank constraint term encourages to catch the underlying relationship among homologous GPCR tasks for greater model generalization, and the entry-wise sparse regularization term ensures to recognize essential discriminative substructures from each task for explanative modeling. We examined MTR-ISLR on a set of 31 important human GPCRs datasets from 9 subfamilies, each with less than 400 ligand associations. The results show that MTR-ISLR reaches better performance when compared with traditional single-task learning, deep multi-task learning and multi-task learning with joint feature learning-based models on most cases, where MTR-ISLR obtains an average improvement of 7% in correlation coefficient (r2) and 12% in root mean square error (RMSE) against the runner-up predictors. The MTR-ISLR web server appends freely all source codes and data for academic usages. ①
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| Keywords
G protein-coupled receptors (GPCRs)
ligand bioactivities
multi-task learning
incoherent sparse and low-rank patterns
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Corresponding Author(s):
Jiansheng WU
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Just Accepted Date: 26 January 2021
Issue Date: 07 December 2021
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