N 6 -Methyladenosine (m 6 A) refers to methylation modification of the adenosine nucleotide acid at the nitrogen-6 position. Many conventional computational methods for identifying N 6 -methyladenosine sites are limited by the small amount of data available. Taking advantage of the thousands of m 6 A sites detected by high-throughput sequencing, it is now possible to discover the characteristics of m 6 A sequences using deep learning techniques. To the best of our knowledge, our work is the first attempt to use word embedding and deep neural networks for m 6 A prediction from mRNA sequences. Using four deep neural networks, we developed a model inferred from a larger sequence shifting window that can predict m 6 A accurately and robustly. Four prediction schemes were built with various RNA sequence representations and optimized convolutional neural networks. The soft voting results from the four deep networks were shown to outperform all of the stateof-the-art methods. We evaluated these predictors mentioned above on a rigorous independent test data set and proved that our proposed method outperforms the state-of-the-art predictors. The training, independent, and cross-species testing data sets are much larger than in previous studies, which could help to avoid the problem of overfitting. Furthermore, an online prediction web server implementing the four proposed predictors has been built and is available at http://server. malab.cn/Gene2vec/.

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Cell-penetrating peptides (CPPs), have been proven as important drug-delivery vehicles, demonstrating the potential as therapeutic candidates. The past decade has witnessed a rapid growth in CPP-based research. Recently, many computational efforts have been made to develop machine-learning-based methods for identifying CPPs. Although much progress has been made, existing methods still suffer low feature representation capability that limits further performance improvement. In this study, we propose a novel predictor called CPPred-RF, in which we integrate multiple sequence-based feature descriptors to sufficiently explore distinct information embedded in CPPs, employ a well-established feature selection technique to improve the feature representation, and, for the first time, construct a two-layer prediction framework based on the random forest algorithm. The jackknife results on benchmark data sets show that the proposed CPPred-RF is at least competitive with the state-of-the-art predictors. Moreover, we establish the first online Web server in terms of predicting CPPs and their uptake efficiency simultaneously. It is freely available at http://server.malab.cn/CPPred-RF .

Protein methylation, an important post-translational modification, plays crucial roles in many cellular processes. The accurate prediction of protein methylation sites is fundamentally important for revealing the molecular mechanisms undergoing methylation. In recent years, computational prediction based on machine learning algorithms has emerged as a powerful and robust approach for identifying methylation sites, and much progress has been made in predictive performance improvement. However, the predictive performance of existing methods is not satisfactory in terms of overall accuracy. Motivated by this, we propose a novel random-forest-based predictor called MePred-RF, integrating several discriminative sequence-based feature descriptors and improving feature representation capability using a powerful feature selection technique. Importantly, unlike other methods based on multiple, complex information inputs, our proposed MePred-RF is based on sequence information alone. Comparative studies on benchmark datasets via vigorous jackknife tests indicate that our proposed MePred-RF method remarkably outperforms other state-of-the-art predictors, leading by a 4.5% average in terms of overall accuracy. A user-friendly webserver that implements the proposed method has been established for researchers' convenience, and is now freely available for public use through http://server.malab.cn/MePred---‑RF. We anticipate our research tool to be useful for the largescale prediction and analysis of protein methylation sites.

Many recent efforts have been made for the development of machine learning-based methods for fast and accurate phosphorylation site prediction. Currently, a majority of well-performing methods are based on hybrid information to build prediction models, such as evolutionary information, disorder information, and so on. Unfortunately, this type of methods suffers two major limitations: one is that it would not be much of help for protein phosphorylation site prediction in case of no obvious homology detected; the other is that computing such the complicated information is time-consuming, which probably limits the usage of predictors in practical applications. In this paper, we present a simple, fast, and powerful feature representation algorithm, which sufficiently explores the sequential information from multiple perspectives only based on primary sequences, and successfully captures the differences between true phosphorylation sites and hboxnon-phosphorylation sites. Using the proposed features, we propose a random forest-based predictor named PhosPred-RF in the prediction of protein phosphorylation sites from proteins. We evaluate and compare the proposed predictor with the state-of-the-art predictors on some benchmark data sets. The experimental results show that PhosPred-RF outperforms other existing predictors, demonstrating its potential to be a useful tool for protein phosphorylation site prediction. Currently, the proposed PhosPred-RF is freely accessible to the public through the user-friendly webserver http://server.malab.cn/PhosPred-RF.