Contrary to this, the EGBW feature evinced the poorest performance.The set of selected features greatly influenced the predictive performance for all three sites.Successively, we further analyzed which feature vectors are valuable to the prediction model based on optimization features selected by F score feature selection.Among the optimal set, and features of S, T and Y sites respectively are used to train the final.There are features constructed by PSPM and, features constructed by EGBW.Tables shows the detailed results of all methods tested on the PPA dataset.ELM dataset over fold crossvalidation and PPA dataset as an independent dataset for S, T, and Y phosphorylation sites.ROC curve is a simplified graphical tool that visualizes and assesses the performance of predictors as the tradeoff between true positive rate and false positive rate.ELM, our proposed deep learningbased method acquired the highest MCC and AUC values for all three types of phosphorylation sites, in comparison to seven stateoftheart methods using both fold crossvalidation and an independent dataset.To provide an intuitive view of performance by different methods, the predictive performance of each method S.To observe the difference between phosphorylation sites, a popular visualization algorithm, tdistributed stochastic neighbor embedding, was utilized to visualize the results, which arranges the highdimensional features into D space and normalizes the values from to. Our developed method obtained an excellent set of hyperparameters as revealed by the utilization of a training dataset over a fold crossvalidation test.The superior performance of the constructed bioinformatics tool for phosphorylation site identification is due to several reasons.First, the method employs efficient feature engineering extraction of common protein descriptors from protein phosphorylation.Third, as a result of the excellent network architecture, the method effectively learns vital protein features through a stackedLSTM layer abstraction.The abovedescribed characteristics of the model and the comparative analysis results reveal that our proposed method to be a useful learning approach for the largescale prediction of unannotated phosphorylation sites of proteins in particular and for drug design in general.Mach. Learn. Res. Lrrc is found to regulate pluripotency by affecting the phosphorylation of STAT through the JAKSTAT signaling pathway.INTRODUCTION cisregulatory elements are regions of noncoding DNA that regulate the expression of their target genes.Furthermore, a single CRE may regulate the expression of several genes at any one time or target different downstream genes in different cell types. Many genetic approaches, such as reporter assays and selftranscribing active regulatory region sequencing, were developed to address this.However, these methods relied heavily on the functional readout of the enhancer fragments outside their native genomic architecture, which led to inaccurate representations of their endogenous activity.Pluripotency is the ability of stem cells to differentiate into all other cell types that constitute the entire organism.In the past few decades, many studies have dened the essential genes involved in maintaining pluripotency.Nontargeting controls are labeled as blue and green dots.The remaining cisregulatory elements are marked in gray.The Z score was calculated with a reference to DNT.The bar chart shows mean SD of three biological replicates.To this end, the correlation was used to normalize the OCT immunouorescence signal derived from both the primary and secondary screens.