Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Nonlinear interactive effects of Single Nucleotide Polymorphisms (SNPs), namely, epistatic interactions, have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for their detection, most only focus on the detection of pairwise epistatic interactions. In this study, a Hypergraph Super- vised Search (HgSS) is developed based on the co-information measure for inferring multiple epistatic interactions with different orders at a substantially reduced time cost. The co-information measure is employed to exhaustively quantify the interaction effects of low order SNP combinations, as ll as the main effects of SNPs. Then, highly suspected SNP combinations and SNPs are used to construct a hypergraph. By deeply analyzing the hypergraph, some clues for better understanding the genetic architecture of complex diseases could be revealed. Experiments are performed on both simulation and real data sets. Results show that HgSS is promising in inferring multiple epistatic interactions with different orders.
['Junliang Shang', 'Yan Sun', 'Yun Fang', 'Shengjun Li', 'Jin-Xing Liu', 'Yuanke Zhang']
I am writing a research paper on a idea Hypergraph Supervised Search for Inferring Multiple Epistatic Interactions with Different Orders
[{"from": "human", "value": "I am writing a research paper on a idea Hypergraph Supervised Search for Inferring Multiple Epistatic Interactions with Different Orders"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Nonlinear interactive effects of Single Nucleotide Polymorphisms (SNPs), namely, epistatic interactions, have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for their detection, most only focus on the detection of pairwise epistatic interactions. In this study, a Hypergraph Super- vised Search (HgSS) is developed based on the co-information measure for inferring multiple epistatic interactions with different orders at a substantially reduced time cost. The co-information measure is employed to exhaustively quantify the interaction effects of low order SNP combinations, as ll as the main effects of SNPs. Then, highly suspected SNP combinations and SNPs are used to construct a hypergraph. By deeply analyzing the hypergraph, some clues for better understanding the genetic architecture of complex diseases could be revealed. Experiments are performed on both simulation and real data sets. Results show that HgSS is promising in inferring multiple epistatic interactions with different orders."}]