Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Identification of novel drug targets is a critical step in drug development. Many recent studies have produced multiple types of data, which provides an opportunity to mine the relationships among them to predict drug targets. In this study,  present a novel integrative approach that combines ontology reasoning with network-assisted gene ranking to predict new drug targets.  utilized colorectal cancer (CRC) as a proof-of-concept use case to illustrate the approach. Starting from FDA-approved CRC drugs and the relationships among disease, drug, gene, pathway, and SNP in an ontology representing PharmGKB data,  inferred 113 potential CRC drug targets.  further prioritized these genes based on their relationships with CRC disease genes in the context of human protein‐protein interaction networks. Thus, among the 113 potential drug targets, 15 re selected as the promising drug targets, including some genes that are supported by previous studies. Among them, EGFR, TOP1 and VEGFA are known targets of FDAapproved drugs. Additionally, CCND1 (cyclin D1), and PTGS2 (prostaglandin-endoperoxide synthase 2) have reported to be relevant to CRC or as potential drug targets based on the literature search. These results indicate that our approach is promising for drug target prediction for CRC treatment, which might be useful for other cancer therapeutics.
['Cui Tao', 'Jingchun Sun', 'W. Jim Zheng', 'Junjie Chen', 'Hua Xu']
I am writing a research paper on a idea Colorectal cancer drug target prediction using ontology-based inference and network analysis.
[{"from": "human", "value": "I am writing a research paper on a idea Colorectal cancer drug target prediction using ontology-based inference and network analysis."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Identification of novel drug targets is a critical step in drug development. Many recent studies have produced multiple types of data, which provides an opportunity to mine the relationships among them to predict drug targets. In this study,  present a novel integrative approach that combines ontology reasoning with network-assisted gene ranking to predict new drug targets.  utilized colorectal cancer (CRC) as a proof-of-concept use case to illustrate the approach. Starting from FDA-approved CRC drugs and the relationships among disease, drug, gene, pathway, and SNP in an ontology representing PharmGKB data,  inferred 113 potential CRC drug targets.  further prioritized these genes based on their relationships with CRC disease genes in the context of human protein\u2010protein interaction networks. Thus, among the 113 potential drug targets, 15 re selected as the promising drug targets, including some genes that are supported by previous studies. Among them, EGFR, TOP1 and VEGFA are known targets of FDAapproved drugs. Additionally, CCND1 (cyclin D1), and PTGS2 (prostaglandin-endoperoxide synthase 2) have reported to be relevant to CRC or as potential drug targets based on the literature search. These results indicate that our approach is promising for drug target prediction for CRC treatment, which might be useful for other cancer therapeutics."}]