A deep learning model for predicting transcription factor binding location at single nucleotide resolution

Salekin, Sirajul; Zhang, Jianqiu Michelle; Huang, Yufei

doi:10.1109/bhi.2017.7897204

Cited by 15 publications

(6 citation statements)

References 17 publications

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“…The design demonstrates a promising result to predict the sequence specification of DNA and ribonucleic acid (RNA) binding. This has inspired the following research like DeepSHR [20], DeepSEA [21] and Dilated [22]. KEGRU [23] uses a bidirectional gated recurrent (BiGRU) unit with k-mer sequences to find RNA protein binding sites.…”

Section: Deep Learning Techniques Have Accomplished Exceptional Outco...mentioning

confidence: 99%

DeepBiG: A Hybrid Supervised CNN and Bidirectional GRU Model for Predicting the DNA Sequence

Chuah,

He,

Huang

2024

IJACSA

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Understanding the deoxyribonucleic acid (DNA) sequence is a major component of bioinformatics research. The amount of biological data increases tremendously. Hence, there is a need for effective approaches to handle the critical problem in the general computational framework of DNA sequence prediction and classification. Numerous deep learning languages can be used to complete these tasks compared to manual techniques that have been followed for ages. The aim of this project is to employ effective approaches for pre-processing DNA sequences and using deep learning languages to train the sequences for making judgments, predictions, and classifications of DNA sequences into known categories. In this study, the pre-processing methods include k-mers and tokenization. We employ a novel hybrid deep learning algorithm that combines convolutional neural networks and is followed by bidirectional gated recurrent networks. This combination can capture dependencies within the genome sequence, even in large datasets with a lot of noise. The proposed model is compared with existing widely used models and classifiers. The results show that the proposed model achieves a good result with an accuracy of 82.90%. The dataset consists of 44,391 labeled DNA sequences obtained from the Encode project.

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Section: Deep Learning Techniques Have Accomplished Exceptional Outco...mentioning

confidence: 99%

DeepBiG: A Hybrid Supervised CNN and Bidirectional GRU Model for Predicting the DNA Sequence

Chuah,

He,

Huang

2024

IJACSA

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“…DeepSNR [ 74 ] was a deep learning method based on CNN. The convolution part of the DeepSNR model had the same structure as the DeepBind network.…”

Section: Deep Learning In Motif Miningmentioning

confidence: 99%

A survey on deep learning in DNA/RNA motif mining

Shen

Zhang

et al. 2020

Briefings in Bioinformatics

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DNA/RNA motif mining is the foundation of gene function research. The DNA/RNA motif mining plays an extremely important role in identifying the DNA- or RNA-protein binding site, which helps to understand the mechanism of gene regulation and management. For the past few decades, researchers have been working on designing new efficient and accurate algorithms for mining motif. These algorithms can be roughly divided into two categories: the enumeration approach and the probabilistic method. In recent years, machine learning methods had made great progress, especially the algorithm represented by deep learning had achieved good performance. Existing deep learning methods in motif mining can be roughly divided into three types of models: convolutional neural network (CNN) based models, recurrent neural network (RNN) based models, and hybrid CNN–RNN based models. We introduce the application of deep learning in the field of motif mining in terms of data preprocessing, features of existing deep learning architectures and comparing the differences between the basic deep learning models. Through the analysis and comparison of existing deep learning methods, we found that the more complex models tend to perform better than simple ones when data are sufficient, and the current methods are relatively simple compared with other fields such as computer vision, language processing (NLP), computer games, etc. Therefore, it is necessary to conduct a summary in motif mining by deep learning, which can help researchers understand this field.

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“…To obtain a baseline prediction performance, we also trained SVM classifiers using the one-hot encoded modification sequences. One hot encoding is a widely used encoding approach in deep learning to represent biological sequences with numeric formats [32][33][34][35][36]. The one-hot encoding converts a 51-bp mRNA sequence into a 4 × 51 binary matrix, where each row corresponds to A, C, G, or U, and a single "1" in each column encodes the corresponding nucleotide at that location of the sequence.…”

Section: Training Predictors For Epitranscriptome Sites Based On Featmentioning

confidence: 99%

Predicting Sites of Epitranscriptome Modifications Using Unsupervised Representation Learning Based on Generative Adversarial Networks

Salekin

Mostavi

Chiu³

et al. 2020

Front. Phys.

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Epitranscriptome is an exciting area that studies different types of modifications in transcripts, and the prediction of such modification sites from the transcript sequence is of significant interest. However, the scarcity of positive sites for most modifications imposes critical challenges for training robust algorithms. To circumvent this problem, we propose MR-GAN, a generative adversarial network (GAN)-based model, which is trained in an unsupervised fashion on the entire pre-mRNA sequences to learn a low-dimensional embedding of transcriptomic sequences. MR-GAN was then applied to extract embeddings of the sequences in a training dataset we created for nine epitranscriptome modifications, namely, m 6 A, m 1 A, m 1 G, m 2 G, m 5 C, m 5 U, 2 ′-O-Me, pseudouridine (), and dihydrouridine (D), of which the positive samples are very limited. Prediction models were trained based on the embeddings extracted by MR-GAN. We compared the prediction performance with the one-hot encoding of the training sequences and SRAMP, a state-of-the-art m 6 A site prediction algorithm, and demonstrated that the learned embeddings outperform one-hot encoding by a significant margin for up to 15% improvement. Using MR-GAN, we also investigated the sequence motifs for each modification type and uncovered known motifs as well as new motifs not possible with sequences directly. The results demonstrated that transcriptome features extracted using unsupervised learning could lead to high precision for predicting multiple types of epitranscriptome modifications, even when the data size is small and extremely imbalanced.

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A deep learning model for predicting transcription factor binding location at single nucleotide resolution

Cited by 15 publications

References 17 publications

DeepBiG: A Hybrid Supervised CNN and Bidirectional GRU Model for Predicting the DNA Sequence

DeepBiG: A Hybrid Supervised CNN and Bidirectional GRU Model for Predicting the DNA Sequence

A survey on deep learning in DNA/RNA motif mining

Predicting Sites of Epitranscriptome Modifications Using Unsupervised Representation Learning Based on Generative Adversarial Networks

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