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DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19.
Xie, Guobo; Xu, Haojie; Li, Jianming; Gu, Guosheng; Sun, Yuping; Lin, Zhiyi; Zhu, Yinting; Wang, Weiming; Wang, Youfu; Shao, Jiang.
  • Xie G; School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China.
  • Xu H; School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China.
  • Li J; School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China.
  • Gu G; School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China.
  • Sun Y; School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China.
  • Lin Z; School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China.
  • Zhu Y; School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China.
  • Wang W; School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China.
  • Wang Y; Huaneng Qinghai Power Generation Co., Ltd. New Energy Branch, Xining 810000, China.
  • Shao J; School of Architecture & Design, China University of Mining and Technology, Xuzhou 221116, China.
Comput Chem Eng ; 166: 107947, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1966455
ABSTRACT
Given that the usual process of developing a new vaccine or drug for COVID-19 demands significant time and funds, drug repositioning has emerged as a promising therapeutic strategy. We propose a method named DRPADC to predict novel drug-disease associations effectively from the original sparse drug-disease association adjacency matrix. Specifically, DRPADC processes the original association matrix with the WKNKN algorithm to reduce its sparsity. Furthermore, multiple types of similarity information are fused by a CKA-MKL algorithm. Finally, a compressed sensing algorithm is used to predict the potential drug-disease (virus) association scores. Experimental results show that DRPADC has superior performance than several competitive methods in terms of AUC values and case studies. DRPADC achieved the AUC value of 0.941, 0.955 and 0.876 in Fdataset, Cdataset and HDVD dataset, respectively. In addition, the conducted case studies of COVID-19 show that DRPADC can predict drug candidates accurately.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Topics: Vaccines Language: English Journal: Comput Chem Eng Year: 2022 Document Type: Article Affiliation country: J.compchemeng.2022.107947

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Topics: Vaccines Language: English Journal: Comput Chem Eng Year: 2022 Document Type: Article Affiliation country: J.compchemeng.2022.107947