most of the existing deep learning-based methods for RBP binding site prediction are still limited to those RBPs with some known binding targets, and they cannot make predictions for those RBPs without any verified binding targets. To address this challenge,...
However, deep learning algorithm is still not applied to RBPs prediction. In Deep-RBPPred, we apply a deep convolutional neural network instead of SVM. Since CNN-based method requires to input a fixed length feature vector, two solutions are handled to meet this requirement. The first solution...
notebook. On/off-target rates were then calculated by dividing, for each sample, the edit count on reads mapped to the reporter construct (on-target) by the edit count on reads mapped to the genome (off-target). This ensured, for example, that enzymes that have high on-target but also...
identify the majority of target RNAs. Additionally, in the RNAcommender dataset, at least 74.7% of RNAs are found to bind with at least two proteins. Therefore, to efficiently explore RNA-RBP interactions, the predictive model needs to recommend several RBPs simultaneously. Due to the capability...
MFNN has an advantage in the prediction accuracy. Finally, the predicted interaction pairs are matched to the known interactions in the other databases. Results of the experiments show that MFNN is an effective model for analyzing the circRNA-RBP interactions. The better performance of MFNN is ...
RBPs recognize their RNA target via specific binding sites on the RNA. Predicting the binding sites of RBPs is known to be a major challenge. We present a new webserver, RBPmap, freely accessible through the website http://rbpmap.technion.ac.il/ for accurate prediction and mapping of ...
There is growing evidence that it is essential to predict the interactions between circRNAs and RBP binding sites for diagnosing diseases and providing a potential target to treat diseases. In this paper, we propose the DFCRBP to identify the binding sites of circRNA-RBPs. The DFCRBP is ...
Here we propose a two-stage deep transfer learning-based framework, termed RBP-TSTL, for accurate prediction of RBPs. In the first stage, the knowledge from the self-supervised pre-trained model was utilized for feature embeddings to represent the protein sequence, while in the second stage, ...
EIF2S2 Is a Candidate Therapeutic Target for GI Cancers Because EIF2S2 expression was found to be significantly associated with poor patient survival and increased tumor growth in GI cancers, it may be a good candidate therapeutic target. Thus, we next assessed the potential effect of EIF2S2 ...
tuberculosis, makes RbpA a potential anti-tuberculosis drug target (11, 15). In the last few years, significant progress has been made toward characterization of RbpA, and this work contributes a structure and new interaction partners to shed light on the function and mechanism of action. ...