Nucleotide-level Convolutional Neural Networks for Pre-miRNA Classification

Zheng, Xueming; Xu, Shengli; Zhang, Ying; Huang, Xinxiang

doi:10.1038/s41598-018-36946-4

Cited by 19 publications

(17 citation statements)

References 24 publications

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“…The results of this study have been obtained for amino acid sequences. It would be needed as a future work to investigate if this effect of padding on model performance can be translated to other biological sequences that are also one-hot encoded and padded, such as RNA 37 , 38 and miRNA 39 or DNA sequences 6 .…”

Section: Discussionmentioning

confidence: 99%

Effect of sequence padding on the performance of deep learning models in archaeal protein functional prediction

Rio

Martin

Perera-Lluna

et al. 2020

Sci Rep

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The use of raw amino acid sequences as input for deep learning models for protein functional prediction has gained popularity in recent years. This scheme obliges to manage proteins with different lengths, while deep learning models require same-shape input. To accomplish this, zeros are usually added to each sequence up to a established common length in a process called zero-padding. However, the effect of different padding strategies on model performance and data structure is yet unknown. We propose and implement four novel types of padding the amino acid sequences. Then, we analysed the impact of different ways of padding the amino acid sequences in a hierarchical Enzyme Commission number prediction problem. Results show that padding has an effect on model performance even when there are convolutional layers implied. Contrastingly to most of deep learning works which focus mainly on architectures, this study highlights the relevance of the deemed-of-lowimportance process of padding and raises awareness of the need to refine it for better performance. The code of this analysis is publicly available at https ://githu b.com/b2sla b/paddi ng_bench mark. Since the breakthrough of deep learning (DL) 1 , deep neural networks are being successfully applied in computational biology 2,3. This is due to their capacity for automatically extracting meaningful features from raw data 4. Specifically, DL is useful in the context of biological sequences, such as proteins or RNA, because it can learn directly from the sequence and hence, capture nonlinear dependencies and interaction effects. Some examples of applications of DL on biological sequences include prediction of specifities of DNA and RNA binding proteins 5 , DNA function quantification 6 , de novo peptide design 7 , detection of conserved DNA fragments 8 , prediction of protein associated GO terms 9 or quantification of the impact of genetic variation on gene regulatory mechanisms 3. The specific DL architectures able to leverage the inner structure of sequential biological data are Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). CNNs entail translational invariance 10 and can be used to find relevant patterns with biological meaning 5,8,11,12. For their part, bidirectional RNNs (and the derived Long Short-Term Memory and Gated Recurrent Units) are appropiate for modelling biological sequences since they are suited for data with a sequential but non-causal structure, variable length, and long-range dependencies 13-16. Both architectures are usually combined, as in DEEPre 17 , where a CNN-RNN model performs a hierarchical classification of enzymes. Proteins are long linear sequences constituted by amino acid residues attached covalently. These amino acid residues are represented by letters that cannot be directly processed by the mathematical operations used by DL models. Choosing how to digitally encode amino acids is a crucial step in this context, since it can affect to the overall performance of the models 18. A comprehensive review and a...

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Section: Discussionmentioning

confidence: 99%

Effect of sequence padding on the performance of deep learning models in archaeal protein functional prediction

Rio

Martin

Perera-Lluna

et al. 2020

Sci Rep

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“…The results of this study have been obtained for amino acid sequences. It would be needed as a future work to check if this effect of padding on model performance can be translated to other biological sequences that are also one-hot encoded and padded, such as RNA [35,36] and miRNA [37] or DNA sequences [6].…”

Section: Discussionmentioning

confidence: 99%

Effect of Sequence Padding on the Performance of Protein-Based Deep Learning Models

Rio¹,

Martin²,

Perera-Lluna³

et al. 2020

Preprint

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Background The use of raw amino acid sequences as input for protein-based deep learning models has gained popularity in recent years. This scheme obliges to manage proteins with different lengths, while deep learning models require same-shape input. To accomplish this, zeros are usually added to each sequence up to a established common length in a process called zero-padding. However, the effect of different padding strategies on model performance and data structure is yet unknown. Results We analysed the impact of different ways of padding the amino acid sequences in a hierarchical Enzyme Commission number prediction problem. Our results show that padding has an effect on model performance even when there are convolutional layers implied. We propose and implement four novel types of padding the amino acid sequences. Conclusions The present study highlights the relevance of the step of padding the one-hot encoded amino acid sequences when building deep learning-based models for Enzyme Commission number prediction. The fact that this has an effect on model performance should raise awareness on the need of justifying the details of this step on future works. The code of this analysis is available at https://github.com/b2slab/padding_benchmark.

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“…Through machine learning, a sequence can be easily judged as a pre-miRNA or not. Based on the ratio between positive and negative data, these predictors can be divided into two types: one is the balanced data (or low unbalanced data) based predictors [32][33][34][35][36][37][38][39][40][41][42][43] and others are the highly unbalanced data based predictors [31,[44][45][46]. Moreover, these predictors also can be roughly divided into human pre-miRNA predictors [31-35, 37, 38, 40, 42, 43], plant pre-miRNA predictors [36,39] and multi-species pre-miRNA predictors [41,[44][45][46][47] according to the species.…”

Section: Introductionmentioning

confidence: 99%

“…In the same year, Xue et al proposed a descriptor that formulates local contiguous structuresequence characteristics from pre-miRNAs, and then combined with SVM to construct the triplet-SVM classifier [33]. neural networks to the prediction of pre-miRNA [42]. Fu et al achieved better results in human pre-miRNA prediction [43].…”

Section: Introductionmentioning

confidence: 99%

A Brief Survey for MicroRNA Precursor Identification Using Machine Learning Methods

Guan

Zhang

et al. 2020

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MicroRNAs, a group of short non-coding RNA molecules, could regulate gene expression. Many diseases are associated with abnormal expression of miRNAs. Therefore, accurate identification of miRNA precursors is necessary. In the past 10 years, experimental methods, comparative genomics methods, and artificial intelligence methods have been used to identify pre-miRNAs. However, experimental methods and comparative genomics methods have their disadvantages, such as timeconsuming. In contrast, machine learning-based method is a better choice. Therefore, the review summarizes the current advances in pre-miRNA recognition based on computational methods, including the construction of benchmark datasets, feature extraction methods, prediction algorithms, and the results of the models. And we also provide valid information about the predictors currently available. Finally, we give the future perspectives on the identification of pre-miRNAs. The review provides scholars with a whole background of pre-miRNA identification by using machine learning methods, which can help researchers have a clear understanding of progress of the research in this field.

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Nucleotide-level Convolutional Neural Networks for Pre-miRNA Classification

Cited by 19 publications

References 24 publications

Effect of sequence padding on the performance of deep learning models in archaeal protein functional prediction

Effect of sequence padding on the performance of deep learning models in archaeal protein functional prediction

Effect of Sequence Padding on the Performance of Protein-Based Deep Learning Models

A Brief Survey for MicroRNA Precursor Identification Using Machine Learning Methods

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