im6A-TS-CNN: Identifying the N6-Methyladenine Site in Multiple Tissues by Using the Convolutional Neural Network

Abstract

N 6 -methyladenosine (m 6 A) is the most abundant post-transcriptional modification and involves a series of important biological processes. Therefore, accurate detection of the m 6 A site is very important for revealing its biological functions and impacts on diseases. Although both experimental and computational methods have been proposed for identifying m 6 A sites, few of them are able to detect m 6 A sites in different tissues. With the consideration of the spatial specificity of m 6 A modification, it is necessary to develop methods able to detect the m 6 A site in different tissues. In this work, by using the convolutional neural network (CNN), we proposed a new method, called im6A-TS-CNN, that can identify m 6 A sites in brain, liver, kidney, heart, and testis of Homo sapiens , Mus musculus , and Rattus norvegicus . In im6A-TS-CNN, the samples were encoded by using the one-hot encoding scheme. The results from both a 5-fold cross-validation test and independent dataset test demonstrate that im6A-TS-CNN is better than the existing method for the same purpose. The command-line version of im6A-TS-CNN is available at https://github.com/liukeweiaway/DeepM6A_cnn .

predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance

“…Existing phosphoglycerylation site, particularly, the most recent predictor assessed their model using 10-fold cross-validation. However, some researchers [ 54 – 57 ] highlighted the necessity of independent test for assessing prediction model in addition to k-fold (e.g. k = 5,10) cross-validation.…”

A Novel Early-Stage Lung Adenocarcinoma Prognostic Model Based on Feature Selection With Orthogonal Regression

“…In conclusion, the proposed FSOR method can deliver better prediction performance for the early-stage prognosis and has the potential to improve therapy strategy, but with few predictor consideration and computation burden. The future work should focus on integrating multi-omics and multi-scale profiling information (Tang et al, 2017 ), together with proposing novel analytical approaches (Liu et al, 2020 ; Qi et al, 2020 ), thus to optimize therapy targets and boost precision medicine.…”

M6A-BiNP: predicting N6-methyladenosine sites based on bidirectional position-specific propensities of polynucleotides and pointwise joint mutual information

“…The second type is the single-base resolution data including three species of human, mouse and rat, which were generated from two single-base resolution m 6 A sequencing techniques miCLIP or m 6 A-REF-seq. The three species datasets with different tissues based on m 6 A-REF-seq technique are downloaded from Dao's study in [42], and the dataset of human species based on miCLIP technique is obtained from Xing's study in [31]. The dataset of human species from Xing's study is denoted as Human51.…”

“…Therefore, many m 6 A site predictive models have been proposed in recent years based on various feature representation methods of sequence and traditional machine learning algorithms or deep learning framework . The latest several predictors, such as Gene2Vec [38], DeepPromise [39], WHISTLE [40], im6A-TS-CNN [42], iRNA-m6A [43] and HSM6AP [44] etc., were developed to identify and predict the m 6 A sites with the golden standard datasets at the single-base resolution level.…”