Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: It is estimated that about 30% of the proteins in the human proteome are regulated by phosphorylation. In recent years, phosphorylation site prediction has been investigated in the field of bioinformatics. This has become necessary due to the challenges associated with experimental methods. Previously,  developed a random forest-based method, termed Random Forest-based Phosphosite predictor (RF-Phos 1.0), to predict phosphorylation sites in proteins given only the amino acid sequence of a protein as input. Here,  report an improved version of this method, termed RF-Phos 1.1 that employs additional sequence-driven features to identify putative sites of phosphorylation across many protein families. In side-by-side comparisons based on 10-fold cross validation analysis and an independent dataset, RF-Phos 1.1 performs comparably to or better than other existing phosphosite prediction methods, such as PhosphoSVM, GPS2.1 and Musite.
['Ahoi Jones', 'Hamid Ismail', 'Jung H. Kim', 'Robert H. Newman', 'B Kc Dukka']
I am writing a research paper on a idea RF-Phos: Random forest-based prediction of phosphorylation sites
[{"from": "human", "value": "I am writing a research paper on a idea RF-Phos: Random forest-based prediction of phosphorylation sites"}, {"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 estimated that about 30% of the proteins in the human proteome are regulated by phosphorylation. In recent years, phosphorylation site prediction has been investigated in the field of bioinformatics. This has become necessary due to the challenges associated with experimental methods. Previously,  developed a random forest-based method, termed Random Forest-based Phosphosite predictor (RF-Phos 1.0), to predict phosphorylation sites in proteins given only the amino acid sequence of a protein as input. Here,  report an improved version of this method, termed RF-Phos 1.1 that employs additional sequence-driven features to identify putative sites of phosphorylation across many protein families. In side-by-side comparisons based on 10-fold cross validation analysis and an independent dataset, RF-Phos 1.1 performs comparably to or better than other existing phosphosite prediction methods, such as PhosphoSVM, GPS2.1 and Musite."}]