Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites

Wei, Leyi; Su, Ran; Wang, Bing; Li, Xiuting; Zou, Quan; Gao, Xing

doi:10.1016/j.neucom.2018.04.082

Cited by 137 publications

(74 citation statements)

References 30 publications

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“…The combination of big data and larger sliding windows together with deep learning techniques appeared to solve the m 6 A overfitting problem. It has also become common to represent sequences with word embedding algorithms, instead of the sparse one-hot encoding (Dai et al 2017;Min et al 2017;Wei et al 2018c).…”

Section: Introductionmentioning

confidence: 99%

Gene2vec: gene subsequence embedding for prediction of mammalian N⁶-methyladenosine sites from mRNA

Zou

Xing

Wei

et al. 2018

RNA

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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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Section: Introductionmentioning

confidence: 99%

Gene2vec: gene subsequence embedding for prediction of mammalian N⁶-methyladenosine sites from mRNA

Zou

Xing

Wei

et al. 2018

RNA

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449

214

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“…This article explores the use of deep learning to efficiently process and combine the sequence‐based and PPI‐based approaches. While deep learning was shown to improve predictive performance in several related prediction problems, it was used only once in the context of combining these two types of information to predict protein functions in the DeepGO model . We design and comparatively test a novel deep learning model called DeepFunc.…”

Section: Introductionmentioning

confidence: 99%

DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions

et al. 2019

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Annotation of protein functions plays an important role in understanding lifeat the molecular level. High-throughput sequencing produces massive numbers of raw proteins sequences and only about 1% of them have been manually annotated with functions. Experimental annotations of functions are expensive, time-consuming and do not keep up with the rapid growth of the sequence numbers. This motivates the development of computational approaches that predict protein functions. A novel deep learning framework, DeepFunc, is proposed which accurately predicts protein functions from protein sequence-and network-derived information. More precisely, DeepFunc uses a long and sparse binary vector to encode information concerning domains, families, and motifs collected from the InterPro tool that is associated with the input protein sequence. This vector is processed with two neural layers to obtain a low-dimensional vector which is combined with topological information extracted from protein-protein interactions (PPIs) and functional linkages. The combined information is processed by a deep neural network that predicts protein functions. DeepFunc is empirically and comparatively tested on a benchmark testing dataset and the Critical Assessment of protein Function Annotation algorithms (CAFA) 3 dataset. The experimental results demonstrate that DeepFunc outperforms current methods on the testing dataset and that it secures the highest F max = 0.54 and AUC = 0.94 on the CAFA3 dataset.

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“…More recently, Xiang et al (2016a) proposed a new predictor called “RNAMethyPre”, using compositional information and position-specific information to build predictive models for the prediction of m 6 A sites on both human and mouse. Additionally, in our previous study, we proposed to use deep learning algorithm to generate high-latent features to improve the predictive performance ( Wei et al, 2018d ). However, we found that most of existing predictors are species-specific.…”

Section: Introductionmentioning

confidence: 99%

M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species

Qiang

Chen

et al. 2018

Front. Genet.

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As one of the well-studied RNA methylation modifications, N6-methyladenosine (m6A) plays important roles in various biological progresses, such as RNA splicing and degradation, etc. Identification of m6A sites is fundamentally important for better understanding of their functional mechanisms. Recently, machine learning based prediction methods have emerged as an effective approach for fast and accurate identification of m6A sites. In this paper, we proposed “M6AMRFS”, a new machine learning based predictor for the identification of m6A sites. In this predictor, we exploited a new feature representation algorithm to encode RNA sequences with two feature descriptors (dinucleotide binary encoding and Local position-specific dinucleotide frequency), and used the F-score algorithm combined with SFS (Sequential Forward Search) to enhance the feature representation ability. To predict m6A sites, we employed the eXtreme Gradient Boosting (XGBoost) algorithm to build a predictive model. Benchmarking results showed that the proposed predictor is competitive with the state-of-the art predictors. Importantly, robust predictions for multiple species by our predictor demonstrate that our predictive models have strong generalization ability. To the best of our knowledge, M6AMRFS is the first tool that can be used for the identification of m6A sites in multiple species. To facilitate the use of our predictor, we have established a user-friendly webserver with the implementation of M6AMRFS, which is currently available in http://server.malab.cn/M6AMRFS/. We anticipate that it will be a useful tool for the relevant research of m6A sites.

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Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites

Cited by 137 publications

References 30 publications

Gene2vec: gene subsequence embedding for prediction of mammalian N⁶-methyladenosine sites from mRNA

Gene2vec: gene subsequence embedding for prediction of mammalian N⁶-methyladenosine sites from mRNA

DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions

M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species

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Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites

Cited by 137 publications

References 30 publications

Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA

Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA

DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions

M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species

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Product

Resources

About

Gene2vec: gene subsequence embedding for prediction of mammalian N⁶-methyladenosine sites from mRNA

Gene2vec: gene subsequence embedding for prediction of mammalian N⁶-methyladenosine sites from mRNA