laplacian regularized least squares
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2022 ◽  
Vol 12 ◽  
Author(s):  
Chu-Qiao Gao ◽  
Yuan-Ke Zhou ◽  
Xiao-Hong Xin ◽  
Hui Min ◽  
Pu-Feng Du

Drug repositioning provides a promising and efficient strategy to discover potential associations between drugs and diseases. Many systematic computational drug-repositioning methods have been introduced, which are based on various similarities of drugs and diseases. In this work, we proposed a new computational model, DDA-SKF (drug–disease associations prediction using similarity kernels fusion), which can predict novel drug indications by utilizing similarity kernel fusion (SKF) and Laplacian regularized least squares (LapRLS) algorithms. DDA-SKF integrated multiple similarities of drugs and diseases. The prediction performances of DDA-SKF are better, or at least comparable, to all state-of-the-art methods. The DDA-SKF can work without sufficient similarity information between drug indications. This allows us to predict new purpose for orphan drugs. The source code and benchmarking datasets are deposited in a GitHub repository (https://github.com/GCQ2119216031/DDA-SKF).


2020 ◽  
Vol 11 ◽  
Author(s):  
Yuan-Ke Zhou ◽  
Jie Hu ◽  
Zi-Ang Shen ◽  
Wen-Ya Zhang ◽  
Pu-Feng Du

Long non-coding RNAs (lncRNAs) play an important role in serval biological activities, including transcription, splicing, translation, and some other cellular regulation processes. lncRNAs perform their biological functions by interacting with various proteins. The studies on lncRNA-protein interactions are of great value to the understanding of lncRNA functional mechanisms. In this paper, we proposed a novel model to predict potential lncRNA-protein interactions using the SKF (similarity kernel fusion) and LapRLS (Laplacian regularized least squares) algorithms. We named this method the LPI-SKF. Various similarities of both lncRNAs and proteins were integrated into the LPI-SKF. LPI-SKF can be applied in predicting potential interactions involving novel proteins or lncRNAs. We obtained an AUROC (area under receiver operating curve) of 0.909 in a 5-fold cross-validation, which outperforms other state-of-the-art methods. A total of 19 out of the top 20 ranked interaction predictions were verified by existing data, which implied that the LPI-SKF had great potential in discovering unknown lncRNA-protein interactions accurately. All data and codes of this work can be downloaded from a GitHub repository (https://github.com/zyk2118216069/LPI-SKF).


2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Fan Wang ◽  
Zhi-An Huang ◽  
Xing Chen ◽  
Zexuan Zhu ◽  
Zhenkun Wen ◽  
...  

2016 ◽  
Vol 45 ◽  
pp. 1-7 ◽  
Author(s):  
Haitao Gan ◽  
Zhizeng Luo ◽  
Yao Sun ◽  
Xugang Xi ◽  
Nong Sang ◽  
...  

PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0139676 ◽  
Author(s):  
Ao Li ◽  
Xiaoyi Xu ◽  
He Zhang ◽  
Minghui Wang

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