Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many methods have been considered for gene selection and analysis of gene expression data. Nonetheless, there still exists the considerable space for improving the explicitness and reliability of gene selection. To this end, this paper proposes a novel method named robust graph regularized non-negative matrix factorization for characteristic gene selection using gene expression data, which mainly contains two aspects: Firstly, enforcing    ${L_{21}}$      -norm minimization on error function which is robust to outliers and noises in data points. Secondly, it considers that the samples lie in low-dimensional manifold which embeds in a high-dimensional ambient space, and reveals the data geometric structure embedded in the original data. To demonstrate the validity of the proposed method,  apply it to gene expression data sets involving various human normal and tumor tissue samples and the results demonstrate that the method is effective and feasible.
['Dong Wang', 'Jin-Xing Liu', 'Ying-Lian Gao', 'Chun-Hou Zheng', 'Yong Xu']
I am writing a research paper on a idea Characteristic Gene Selection Based on Robust Graph Regularized Non-Negative Matrix Factorization
[{"from": "human", "value": "I am writing a research paper on a idea Characteristic Gene Selection Based on Robust Graph Regularized Non-Negative Matrix Factorization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many methods have been considered for gene selection and analysis of gene expression data. Nonetheless, there still exists the considerable space for improving the explicitness and reliability of gene selection. To this end, this paper proposes a novel method named robust graph regularized non-negative matrix factorization for characteristic gene selection using gene expression data, which mainly contains two aspects: Firstly, enforcing    ${L_{21}}$      -norm minimization on error function which is robust to outliers and noises in data points. Secondly, it considers that the samples lie in low-dimensional manifold which embeds in a high-dimensional ambient space, and reveals the data geometric structure embedded in the original data. To demonstrate the validity of the proposed method,  apply it to gene expression data sets involving various human normal and tumor tissue samples and the results demonstrate that the method is effective and feasible."}]