Human drug-pathway association prediction based on network consistency projection. (April 2022)

• Record Type: Journal Article
• Authors: Ghulam, Ali; Lei, Xiujuan; Zhang, Yuchen; Wu, Zhenqiang
• Is Part Of: Computational biology and chemistry. Volume 97(2022)
• Publication Date: 2022-04
• Subjects: Drug-pathway associations -- Drug-pathway targets -- Jaccard similarity -- Network consistency; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2022.107624
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: We present a novel computational method for drug-pathway association prediction based on known drug-pathway associations. The association between a drug and a pathway needs to be examined to not only explain the cause and enable the identification, therapy, and diagnosis of a human disease. Though, biological studies and clinical trials require substantial time and resources to identify drug-pathway associations. Considerable research attention has been devoted to many scientists have developed computer models to predict the future interactions of drug–pathway organizations. We proposed a novel computing approach known as the Network Consistency Projection for Human Drug-Pathway Association (NCPHDPA). This method was based on the drug pathway target wherein biologically related drugs appear to interact with pathway targets in identical diseases and vice versa. We computed the pathway-pathway-interaction similarity of drugs sharing similarities on the basis of pairwise Jaccard similarity and then computed the drug-drug–interaction similarity of drugs sharing similar drug targets based on Jaccard similarity. The system was combined because of the cosine similarity drug network, the pathway cosine resemblance network, and the interaction network for recognized drug-pathway. NCPHDPA was a parameter less solution and did not require negative tests. Notably, NCPHDPA could be used to predict drugs without any known related pathway. Test results showed that our proposed NCPHDPA method with LOOCV achieved a high ROC of AUC = 0.7479, and with10-fold CV obtained ROC of AUC = 0.7566. The Result of ROC (AUC) comparison of NCPHDPA with other methods, such as SIMCCDA LOOCV (AUC = 0.7364), LOMDA LOOCV (AUC = 0.6729) and DMTHNDM LOOCV (AUC = 0.50.00) obtained. The robust predictive capability of the NCPHDPA was demonstrated in three case studies on drugs involved in pathways, cancer pathways, and hepatocellular carcinoma. Few attempts have been made to compared with other methods, our proposed NCPHDPA method had reliable predictive performance. The results yielded some interesting findings as that interaction of these proteins can cause a change in its associated pathway, leading to the onset of cancer. Graphical Abstract: ga1 Highlights: Human Drug-Pathway Association Prediction Based on Network Consistency Projection. A novel computational method for drug-pathway association prediction based on known drug-pathway associations. This method was based on the drug pathway target wherein biologically related drugs appear to interact with pathway targets in identical diseases and vice versa. We computed the pathway-pathway-interaction similarity of drugs sharing similarities on the basis of pairwise Jaccard similarity. The docking approach is an essential portion of drug designing.