RNA-binding proteins (RBPs) play crucial roles in many biological processes, e.g. gene regulation. Computational identification of RBP binding sites on RNAs are urgently needed. In particular, RBPs bind to RNAs by recognizing sequence motifs. Thus, fast locating those motifs on RNA sequences is crucial and time-efficient for determining whether the RNAs interact with the RBPs or not. In this study, we present an attention based convolutional neural network, iDeepA, to predict RNA-protein binding sites from raw RNA sequences. We first encode RNA sequences into one-hot encoding. Next, we design a deep learning model with a convolutional neural network (CNN) and an attention mechanism, which automatically search for important positions, e.g. binding motifs, to learn discriminant high-level features for predicting RBP binding sites. We evaluate iDeepA on publicly gold-standard RBP binding sites derived from CLIP-seq data. The results demonstrate iDeepA achieves comparable performance with other state-of-the-art methods.
Responding to reports of comments made by the lawmakers, which suggested the EU would consider legally binding assurances on how the withdrawal agreement would operate, Selmayr tweeted: "On the EU side, nobody is considering this. Asked whether any assurance would help to get the Withdrawal Agreement through the Commons, the answers of MPs were inconclusive."
Reed starts each class by sharing this story, and teaching students how to mitigate pain and prevent injuries of their own. They urge students to spend a few minutes a day going through self-massaging techniques. Reed demonstrates how to massage their diaphragm muscle tissue near the edge of the binder and how to roll a tennis ball against their trapezius muscle using a corner of a wall. In another technique, Reed lies down on a roller and extends their arms toward the floor, opening their chest.
In general, each factor recognizes a family of "similar" sequences rather than a single unique sequence. Ultimately, the transcriptional state of a gene is determined by the cooperative interaction of several bomld factors. We have developed a method using Gibbs Sampling and tile Mininmm Description Length principle for automatically and reliably creating weight matrix models of binding sites from a database (TRANSFAC) of known binding site sequences. Determining the relationship between sequence and binding affinity for a particular factor is an important first step in predicting whether a given uncharacterized sequence is part of a promoter site or other control region. Here we describe the foundation for the methods we will use to develop weight ma rix nlodcls for transcription factor binding sites.