Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Feb 3;24(1):38.
doi: 10.1186/s12859-023-05153-y.

SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction

Lingzhi Hu  1 , Chengzhou Fu  1   2 , Zhonglu Ren  1 , Yongming Cai  1   2 , Jin Yang  1   2 , Siwen Xu  1 , Wenhua Xu  1 , Deyu Tang  3   4   5
Affiliations

SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction

Lingzhi Hu et al. BMC Bioinformatics. .

Abstract

Background: The experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identifying drug-target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed.

Methods: In this study, we propose a framework called SSELM-neg for DTI prediction, in which we use a screening approach to choose high-quality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine.

Results: The results demonstrated that the proposed technique outperformed other state-of-the-art methods in 10-fold cross-validation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, G-protein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively.

Conclusion: The screening approach produced high-quality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other state-of-the-art methods.

Keywords: Class imbalance; Drug discovery; Drug–target interactions; Extreme learning machine; Spherical search.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Visual of the negative samples selected
Fig. 2
Fig. 2
Drug–target interaction prediction framework for SSELM-neg
Fig. 3
Fig. 3
Comparison of the AUC and AUPR results for the six methods
Fig. 4
Fig. 4
ROC results for interaction prediction for the dataset
See this image and copyright information in PMC

References

    1. Dudley JT, Deshpande T, Butte AJ. Exploiting drug–disease relationships for computational drug repositioning. Brief Bioinform. 2011;12(4):303–311. - PMC - PubMed
    1. Swamidass SJ. Mining small-molecule screens to repurpose drugs. Brief Bioinform. 2011;12(4):327–335. - PubMed
    1. Chen X, Yan CC, Zhang X, Zhang X, Dai F, Yin J, Zhang Y. Drug–target interaction prediction: databases, web servers and computational models. Brief Bioinform. 2016;17(4):696–712. - PubMed
    1. Chen X, Guan N-N, Sun Y-Z, Li J-Q, Qu J. Microrna-small molecule association identification: from experimental results to computational models. Brief Bioinform. 2020;21(1):47–61. - PubMed
    1. Hopkins AL. Predicting promiscuity. Nature. 2009;462(7270):167–168. - PubMed

LinkOut - more resources