Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Disease gene identification is a key step to understand the cellular mechanisms associated with a specific disease. Compared with biological experiments, computational predictions of disease genes are cheaper and more effortless. Many computational methods are used to detect causal genes for diseases on the protein-protein interaction (PPI) networks generated by the high-throughput technology. Hover, the accuracy of these methods need to be improved due to the false interactions in the PPI data. To deal with the challenge, other methods are proposed via the integration of biological information from different sources with the PPI networks. In this work, a new algorithm AIDG is developed to predict disease genes. First, the ighted PPI networks are built by incorporating the protein subcellular localization information into the human PPI networks. Next, all of disease candidate genes are scored in terms of a iteration function. Finally, they are ranked on descending order of their scores. The top candidates are considered as potential disease genes. The results from the leave-one-out crossing validation (LOOCV) show that AIDG outperforms other similar methods like DADA and ToppNet.
['Xiwei Tang', 'Xiaohua Hu', 'Xuejun Yang', 'Yuan Sun']
I am writing a research paper on a idea A algorithm for identifying disease genes by incorporating the subcellular localization information into the protein-protein interaction networks
[{"from": "human", "value": "I am writing a research paper on a idea A algorithm for identifying disease genes by incorporating the subcellular localization information into the protein-protein interaction networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Disease gene identification is a key step to understand the cellular mechanisms associated with a specific disease. Compared with biological experiments, computational predictions of disease genes are cheaper and more effortless. Many computational methods are used to detect causal genes for diseases on the protein-protein interaction (PPI) networks generated by the high-throughput technology. Hover, the accuracy of these methods need to be improved due to the false interactions in the PPI data. To deal with the challenge, other methods are proposed via the integration of biological information from different sources with the PPI networks. In this work, a new algorithm AIDG is developed to predict disease genes. First, the ighted PPI networks are built by incorporating the protein subcellular localization information into the human PPI networks. Next, all of disease candidate genes are scored in terms of a iteration function. Finally, they are ranked on descending order of their scores. The top candidates are considered as potential disease genes. The results from the leave-one-out crossing validation (LOOCV) show that AIDG outperforms other similar methods like DADA and ToppNet."}]