Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Molecular profiling data (e.g., gene expression) has been used for clinical risk prediction and biomarker discovery. Hover, it is necessary to integrate other prior knowledge like biological pathways or gene interaction networks to improve the predictive ability and biological interpretability of biomarkers. Here,  first introduce a general regularized Logistic Regression (LR) framework with regularized term $\lambda \|\bm{w}\|_1 + \eta\bm{w}^T\bm{M}\bm{w}$, which can reduce to different penalties, including Lasso, elastic net, and network-regularized terms with different $\bm{M}$. This framework can be easily solved in a unified manner by a cyclic coordinate descent algorithm which can avoid inverse matrix operation and accelerate the computing speed. Hover, if those estimated $\bm{w}_i$ and $\bm{w}_j$ have opposite signs, then the traditional network-regularized penalty may not perform ll. To address it,  introduce a novel network-regularized sparse LR model with a new penalty $\lambda \|\bm{w}\|_1 + \eta|\bm{w}|^T\bm{M}|\bm{w}|$ to consider the difference beten the absolute values of the coefficients. And  develop two efficient algorithms to solve it. Finally,  test our methods and compare them with the related ones using simulated and real data to show their efficiency.
['Wenwen Min', 'Juan Liu', 'Shihua Zhang']
I am writing a research paper on a idea Network-regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery
[{"from": "human", "value": "I am writing a research paper on a idea Network-regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Molecular profiling data (e.g., gene expression) has been used for clinical risk prediction and biomarker discovery. Hover, it is necessary to integrate other prior knowledge like biological pathways or gene interaction networks to improve the predictive ability and biological interpretability of biomarkers. Here,  first introduce a general regularized Logistic Regression (LR) framework with regularized term $\\lambda \\|\\bm{w}\\|_1 + \\eta\\bm{w}^T\\bm{M}\\bm{w}$, which can reduce to different penalties, including Lasso, elastic net, and network-regularized terms with different $\\bm{M}$. This framework can be easily solved in a unified manner by a cyclic coordinate descent algorithm which can avoid inverse matrix operation and accelerate the computing speed. Hover, if those estimated $\\bm{w}_i$ and $\\bm{w}_j$ have opposite signs, then the traditional network-regularized penalty may not perform ll. To address it,  introduce a novel network-regularized sparse LR model with a new penalty $\\lambda \\|\\bm{w}\\|_1 + \\eta|\\bm{w}|^T\\bm{M}|\\bm{w}|$ to consider the difference beten the absolute values of the coefficients. And  develop two efficient algorithms to solve it. Finally,  test our methods and compare them with the related ones using simulated and real data to show their efficiency."}]