Application of Spherical Evolution Extreme Learning Machine in Intelligent Prediction of Drug Target Interaction

HU Lingzhi; FU Chengzhou; CAI Yongming; YANG Jin; TANG Deyu

doi:10.6054/j.jscnun.2023012

Journal of South China Normal University (Natural Science Edition) > 2023 > 55(1): 121-128. > DOI: 10.6054/j.jscnun.2023012

HU Lingzhi, FU Chengzhou, CAI Yongming, YANG Jin, TANG Deyu. Application of Spherical Evolution Extreme Learning Machine in Intelligent Prediction of Drug Target Interaction[J]. Journal of South China Normal University (Natural Science Edition), 2023, 55(1): 121-128. DOI: 10.6054/j.jscnun.2023012

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Application of Spherical Evolution Extreme Learning Machine in Intelligent Prediction of Drug Target Interaction

1.
College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China
2.
Pazhou Lab, Guangzhou 510335, China

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Received Date: September 12, 2022
Available Online: April 11, 2023

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Abstract

Abstract

To solve the problems of time-consuming and costly wet experiments for drug development, machine learning has been applied to the prediction of drug-target interactions. At the same time, in order to solve the constraints of machine learning in building drug-target interaction models, it is subject to various problems such as class imbalance of classifiers and parameter optimization. The paper proposes a drug-target interaction prediction method (SEELM-DTI) based on a spherical evolution extreme learning machine, which mainly uses a screening method to select high confidence negative samples and a spherical evolution to optimize the parameters of the extreme learning machine. The researcn compared SEELM-DTI with SELF-BLM, NetLapRLS, WNN-GIP, SPLCMF, BLM-NII in a benchmark dataset and evaluated the metrics of AUC and AUPR.The experimental results showed that the SEELM-DTI outperformed other benchmark algorithms and solved the class imbalance and parameter optimization. Finally, the effectiveness of SEELM-DTI in predicting drug-target interactions was validated on several commonly used drug databases.
- drug-target interactions,
- drug discovery,
- extreme learning machine,
- spherical search,
- class imbalance

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References (25)

References

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Application of Spherical Evolution Extreme Learning Machine in Intelligent Prediction of Drug Target Interaction

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