Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: It is of urgency to effectively identify differentially expressed genes from RNA-Seq data. ,  propose a novel method, semi-supervised feature extraction, to analyze RNA-Seq data. Our scheme is shown as follows. Firstly,  construct a graph Laplacian matrix and refine it by using labeled samples. Secondly,  find semi-supervised optimal maps by solving a gen- eralized eigenvalue problem. Thirdly,  solve an optimal problem via joint L2,1-norm constraint to obtain a projection matrix. Finally,  identify differ- entially expressed genes based on the projection matrix. The results on real RNA-Seq data sets demonstrate the feasibility and effectiveness of our method.
['Jin-Xing Liu', 'Yong Xu', 'Ying-Lian Gao', 'Dong Wang', 'Chun-Hou Zheng', 'Junliang Shang']
I am writing a research paper on a idea Semi-supervised Feature Extraction for RNA-Seq Data Analysis
[{"from": "human", "value": "I am writing a research paper on a idea Semi-supervised Feature Extraction for RNA-Seq Data Analysis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: It is of urgency to effectively identify differentially expressed genes from RNA-Seq data. ,  propose a novel method, semi-supervised feature extraction, to analyze RNA-Seq data. Our scheme is shown as follows. Firstly,  construct a graph Laplacian matrix and refine it by using labeled samples. Secondly,  find semi-supervised optimal maps by solving a gen- eralized eigenvalue problem. Thirdly,  solve an optimal problem via joint L2,1-norm constraint to obtain a projection matrix. Finally,  identify differ- entially expressed genes based on the projection matrix. The results on real RNA-Seq data sets demonstrate the feasibility and effectiveness of our method."}]