Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: As various high-throughput experimental techniques are developed to characterize biological systems at the genome scale, vast protein function data have been collected. Hover, these functional tags are often imprecise and/or incomplete, resulting in unsatisfactory performances in function-related experiments and applications. Therefore, the refinement of protein functional annotation has been one of the fundamental challenges in post-genomic era. , the refinement problem is formulated as a decomposition of the available functional tag matrix X with noise and missing data into latent protein features U and function representations V. Specifically,  aim to minimize the sparse error beten original tag matrix X and the recovery matrix, and incorporate the protein-protein interactions and the function-function correlations to regularize two latent feature matrices U and V respectively. Finally, all these components are integrated into a convex optimization model, named graph regularized l1-norm PCA, and an efficient convergence iterative procedure is proposed for the model based on augmented lagrangian multiplier method.  conduct extensive experiments on yeast database and the results indicate that our new refinement method outperforms consistently other related state-of-the-art approaches by the average precision and average F1-score.
['Dengdi Sun', 'Huadong Liang', 'Meiling Ge', 'Zhuanlian Ding', 'Wanting Cai', 'Bin Luo']
I am writing a research paper on a idea Protein functional annotation refinement based on graph regularized ℓ 1 -norm PCA
[{"from": "human", "value": "I am writing a research paper on a idea Protein functional annotation refinement based on graph regularized \u2113 1 -norm PCA"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: As various high-throughput experimental techniques are developed to characterize biological systems at the genome scale, vast protein function data have been collected. Hover, these functional tags are often imprecise and/or incomplete, resulting in unsatisfactory performances in function-related experiments and applications. Therefore, the refinement of protein functional annotation has been one of the fundamental challenges in post-genomic era. , the refinement problem is formulated as a decomposition of the available functional tag matrix X with noise and missing data into latent protein features U and function representations V. Specifically,  aim to minimize the sparse error beten original tag matrix X and the recovery matrix, and incorporate the protein-protein interactions and the function-function correlations to regularize two latent feature matrices U and V respectively. Finally, all these components are integrated into a convex optimization model, named graph regularized l1-norm PCA, and an efficient convergence iterative procedure is proposed for the model based on augmented lagrangian multiplier method.  conduct extensive experiments on yeast database and the results indicate that our new refinement method outperforms consistently other related state-of-the-art approaches by the average precision and average F1-score."}]