Promoter prediction in nannochloropsis based on densely connected convolutional neural networks

Wei, Pi-Jing; Pang, Zhen-Zhen; Jiang, Lin‐Jie; Tan, Dayu; Su, Yansen; Zheng, Chun-Hou

doi:10.1016/j.ymeth.2022.03.017

Cited by 8 publications

(10 citation statements)

References 44 publications

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“…Methods based on deep-learning primarily focus on training a neural network with DNA sequences or DNA sequences with epigenomic characteristics (such as histone modifications, chromatin accessibility, DNA methylation, or CpG islands) as inputs. Though some scholars have trained their networks with epigenome features [67,68,71,74,75,82], most have done so with only DNA sequences as inputs [69,70,72,73,[77][78][79][80][81]85,[88][89][90][98][99][100]102,[104][105][106][107][108][109][110][111]142]. Predicting enhancers and promoters directly from DNA sequences is believed to be more applicable than identifying them from multiple epigenomic features because the epigenomic characteristics data carries with it substantial sequencing costs, and a high rate of false positives.…”

Section: Methods For Identifying Enhancer/promoter Based On Deep-lear...mentioning

confidence: 99%

“…(i) Encoding a DNA sequence as in Section “Vector representations of DNA sequence”. (ii) Constructing a neural network to predict the presence of enhancers or promoters, such as CNN [69,73,76,78,81,85,88,98–100,102,104,111], transfer learning [110], or LSTM [82,88,108]. To establish the right characteristics and increase the accuracy of identifying an enhancer or promoter, the above methods either improve the input layer of DNA feature vector representation (for example, dna2vec) or neural network architectures or change the activation functions.…”

Section: Prediction Of Enhancer and Promotermentioning

confidence: 99%

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Computational methods for identifying enhancer‐promoter interactions

Gong,

Chen,

Tang

et al. 2023

Quant. Biol.

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BackgroundAs parts of the cis‐regulatory mechanism of the human genome, interactions between distal enhancers and proximal promoters play a crucial role. Enhancers, promoters, and enhancer‐promoter interactions (EPIs) can be detected using many sequencing technologies and computation models. However, a systematic review that summarizes these EPI identification methods and that can help researchers apply and optimize them is still needed.ResultsIn this review, we first emphasize the role of EPIs in regulating gene expression and describe a generic framework for predicting enhancer‐promoter interaction. Next, we review prediction methods for enhancers, promoters, loops, and enhancer‐promoter interactions using different data features that have emerged since 2010, and we summarize the websites available for obtaining enhancers, promoters, and enhancer‐promoter interaction datasets. Finally, we review the application of the methods for identifying EPIs in diseases such as cancer.ConclusionsThe advance of computer technology has allowed traditional machine learning, and deep learning methods to be used to predict enhancer, promoter, and EPIs from genetic, genomic, and epigenomic features. In the past decade, models based on deep learning, especially transfer learning, have been proposed for directly predicting enhancer‐promoter interactions from DNA sequences, and these models can reduce the parameter training time required of bioinformatics researchers. We believe this review can provide detailed research frameworks for researchers who are beginning to study enhancers, promoters, and their interactions.

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Section: Methods For Identifying Enhancer/promoter Based On Deep-lear...mentioning

confidence: 99%

Section: Prediction Of Enhancer and Promotermentioning

confidence: 99%

Computational methods for identifying enhancer‐promoter interactions

Gong,

Chen,

Tang

et al. 2023

Quant. Biol.

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“…In recent years DenseNet is also widely used in various fields, Pi et al. proposed propose a method called DNPPro based on densely connected convolutional neural networks to predict the promoter of Nannochloropsis [21]. Jiao et al.…”

Section: Related Workmentioning

confidence: 99%

“…Inspired by citations [5] and [21], this work proposes a DenseNet-based feature-weighted convolutional network recognition model for efficient classification of industrial parts. The highlights of this work are:…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by citations [5] and [21], this work proposes a DenseNet‐based feature‐weighted convolutional network recognition model for efficient classification of industrial parts. The highlights of this work are: Embedding SE‐block in DenseNet to extract feature weights to enhance the object features of useful information while suppressing the features of useless information, thus effectively improving the accuracy of the model; further optimizing the proposed model structure by using the focal loss function for the problem of classification difficulties caused by similar shapes of some parts. To address the problem of lack of open datasets, design the hardware and software system for automatic industrial part acquisition in practical industrial part classification applications, with functions of automatic part data acquisition, preprocessing of acquired data, real‐time part identification and model training. Since the collected images are difficult to be directly used in the model training, the traditional image preprocessing method is not targeted and efficient in the specified scene in this work, so the Canny edge detection algorithm is modified and a novel preprocessing process is proposed. …”

Section: Introductionmentioning

confidence: 99%

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A DenseNet‐based feature weighting convolutional network recognition model and its application in industrial part classification

An,

Sun,

Song

et al. 2023

IET Image Processing

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Traditional warehousing typically needs machine learning or manual tagging to classify objects. However, this method is less robust and consumes a lot of labour and material resources. Based on DenseNet, this work proposes a feature weighting convolutional network recognition model and designs a set of software and hardware for data acquisition, which is applied to the efficient classification of industrial parts in warehouse management. Firstly, this work modifies DenseNet by embedding SE‐Block, and replaces the cross‐entropy loss function with the focus loss function to optimize the model structure. Secondly, a multi‐view hardware and software acquisition system is designed to complete the functions of part image acquisition, image preprocessing, model training and part recognition. Finally, an industrial parts sorting experiment was designed. Compared with the original DenseNet model, the proposed weighted convolutional network identification model showed that the accuracy of the modified model was increased by 3.09% and the convergence rate was significantly improved. The modified model proposed in this work aims to improve the recognition accuracy of industrial parts in modern warehouse management, so as to modify the classification efficiency of warehouse parts in production.

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