磷酸化反應是真核生物中最重要的翻譯後修飾之一，並在細胞內的許多反應過程中有著至關重要的作用。關於激酶及其底物的研究對於理解細胞中的信號傳導網絡非常重要，並且有助於一些疾病開發新的治療方法，如癌症。由於相關的實驗需要耗費大量的時間與人力成本，因此磷酸化位點的預測變得很重要，多年來也發展出許多相關的研究以及工具，大抵上分為兩類 ─ 激酶特異性與非激酶特異性。激酶特異性的磷酸化位點預測需要同時輸入序列以及該序列的激酶，再預測序列中的絲氨酸 ( S )、蘇氨酸 ( T ) 及酪氨酸 ( Y ) 是否為磷酸化位點；非激酶特異性的磷酸化位點預測只需要輸入序列即可進行預測。隨著定序技術的發展，有很多序列還未確定的激酶或是某些激酶的已知序列過少，難以使用激酶特異性的方法來預測，因此非激酶特異性磷酸化位點的預測在此時就越漸重要。
在本研究中使用了兩大類特徵來進行非特異性磷酸化位點預測，分別是明確特徵與隱性特徵。明確特徵包括夏儂熵、相對熵、蛋白質二級結構的預測值、蛋白質非穩定結構預測值、溶劑可及區域、重疊性質、平均累計疏水性質、K 近鄰算法以及位置特異性矩陣。隱性特徵由卷積神經網絡以及循環神經網路產生。最後再將這兩大類特徵輸入XGBoost來進行預測。此方法在S / T / Y磷酸化位點上在測試資料集得到的 AUC 值分別為 0.8598 / 0.7547 / 0.6842，優於現行的其他方法。

Phosphorylation is one of the most important post-translational modification in Eukaryotes, and it plays a vital role of many reactions in cells. Because the related experiments require a lot of time and labor costs, the prediction of phosphorylation sites becomes more important. Many related research and tools have also been developed over the years, and they mostly divided into two categories – kinase-specific and non-kinase specific. The prediction of kinase-specific phosphorylation sites needs to input sequences and the kinase of sequences simultaneously, and then predict Serine, Threonine and Tyrosine is phosphorylation site or not. The prediction of non-kinase-specific phosphorylation sites only needs to input sequence. With the development of sequencing technology, the kinases of many sequences are unsure, and the corresponding sequences of some kinases are too few. So, the prediction of non-kinase-specific phosphorylation sites become more and more important.
In our research, we use explicit and implicit features to predict non-kinase-specific phosphorylation sites. Explicit features include shannon entropy, relative entropy, the prediction value of protein second structure, the prediction value of protein disorder, solvent accessible area, overlapping properties, averaged cumulative hydrophobicity, KNN and position-specific scoring matrix. Implicit features are generated by convolutional neural network and recurrent neural network. Finally, we input these features XGBoost to predict. The AUC of this method on S / T / Y phosphorylation sites are 0.8598 / 0.7547 / 0.6842 respectively, and it is better than other methods currently.

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