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An approach based on deep convolutional neural networks. DeepBind can discover new patterns even when the locations of patterns within sequences are unknown—a task for which traditional neural networks require an exorbitant amount of training data. DeepBind addresses the above challenges: (i) It can be applied to both microarray and sequencing data; (ii) it can learn from millions of sequences through parallel implementation on a graphics processing unit (GPU); (iii) it generalizes well across technologies, even without correcting for technology-specific biases; (iv) it can tolerate a moderate degree of noise and mislabeled training data; and (v) it can train predictive models fully automatically, alleviating the need for careful and time-consuming hand-tuning.


A tool for genome-wide recommendation of RNA-protein interactions. RNAcommender is a recommender system capable of suggesting RNA targets to unexplored RNA binding proteins, by propagating the available interaction information, taking into account the protein domain composition and the RNA predicted secondary structure. RNAcommender can be a valid tool to assist researchers in identifying potential interacting candidates for the majority of RBPs with uncharacterised binding preferences.


Predicts protein-binding regions in mRNA. RBPbinding is a support vector machine (SVM) that uses sequence profiles constructed from log-odds scores of mono and di-nucleotides and nucleotide compositions. The software showed a high performance in testing on many human RNA sequences. It was evaluated in several ways, including standard 10-fold cross validation on six datasets with different ratios of positive to negative instances, LOPO cross validation, and independent testing with six datasets of different ratios of positive to negative instances.


A deep learning based framework to fuse heterogeneous data for predicting RNA-protein interaction sites. iDeep can not only learn the hidden feature patterns from individual source of data, but also extracted the shared representation across them. In addition, the convolutional neural network in iDeep can automatically identify binding motifs. To validate this method over other methods, experiments were performed on large-scale CLIP-seq datasets. The comprehensive results indicated the huge advantage of iDeep, which performs much better than the state-of-the-art methods.