State-of-the-art RNA–protein interaction (RPI) prediction methods primarily rely on traditional machine learning and deep learning techniques. One such approach is RPIseq, which encodes 4-mer RNA and 3-mer pro
RPISeq (Muppirala et al., 2011) predicts whether a given pair of protein and RNA interacts with each other. RPISeq cannot be used to find binding regions or sites, but can be used to estimate the interaction probability of a protein–RNA pair. This paper describes a web server called PRI...
RPISeq – RNA-Protein Interaction PredictionRPISeq:: DESCRIPTIONRPISeq is a family of classifiers for predicting RNA-protein interactions using only sequence information. Advertisement::DEVELOPERRPISeq team:: SCREENSHOTSN/A:: REQUIREMENTSWeb Browser
throughputexperimentstoidentifyRNA-proteininteractionsarebeginningtoprovidevaluableinformationabout thecomplexityofRNA-proteininteractionnetworks,butareexpensiveandtimeconsuming.Hence,thereisa needforreliablecomputationalmethodsforpredictingRNA-proteininteractions. Results:WeproposeRPISeq,afamilyofclassifiersforpredictingRNA-prot...
RPISeq is based on the curated RNA-protein interactions obtained from PRIDB15, a database of RNA-protein structures extracted from PDB, and used RF and SVM as the classifiers. Recently, Sureshet al., have proposed RPI-Pred, an SVM based prediction method utilizing protein and RNA secondary ...
RPISeq classifiers can reliably predict RNA-protein interactions We compared the performance of RPISeq-SVM and RPISeq-RF classifiers to predict RPIs, using the benchmark datasets described above. Table 1 summarizes the prediction results obtained in 10-fold cross-validation experiments. On the RPI2241...
RPISeq RNA-protein interaction prediction CTF conjoint triad feature SVM support vector machine RF random forest SFPEL-LPI sequence-based feature projection ensemble learning for LPI PseDNC pseudo dinucleotide composition PseAAC pseudo amino acid composition LPI-BLS lncRNA–protein interactions-broad learning...
RNA-Protein Interaction Prediction (RPISeq) was used to calculate the interaction probabilities of snoRNAs with proteins of interest. The prediction was made using both the Random Forest (RF) and the Support Vector Machine (SVM) classifiers, using a stringent threshold of >0.6.Results. The time-...
RPISeq 数据库功能: Submit a protein sequence and an RNA sequence to predict the interaction probability. 实例分析:以蛋白AGO2和LncRNA H19为例 Interaction probabilities Prediction using RF classifier 0.55 Prediction using SVM classifier 0.54 两者数值均>0.5,表示预测会相互结合 ...
基于神经网络的LncRNA与蛋白质互作关系预测算法