Combining Gene Ontology with Deep Neural Networks to Enhance the Clustering of Single Cell RNA-Seq Data

Peng, Jiajie; Wang, Xiaoyu; Shang, Xuequn

doi:10.1101/437020

Cited by 10 publications

(13 citation statements)

References 33 publications

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“…proposed both an unsupervised method called "GOAE" (Gene Ontology AutoEncoder) and a supervised method called "GONN" (Gene Ontology Neural Network) for dimensionality reduction and clustering [51]. Their experimental results show that, by incorporating the prior knowledge from GO, both clustering performance and interpretability of the neural networks can be improved.…”

Section: Deep Learning Methods For Scrna-seq Data Analysismentioning

confidence: 99%

Emerging deep learning methods for single‐cell RNA‐seq data analysis

Zheng

Wang

2019

Quant. Biol.

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Deep learning is making major breakthrough in several areas of bioinformatics. Anticipating that this will occur soon for the single-cell RNA-seq data analysis, we review newly published deep learning methods that help tackle computational challenges. Autoencoders are found to be the dominant approach. However, methods based on deep generative models such as generative adversarial networks (GANs) are also emerging in this area.

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Section: Deep Learning Methods For Scrna-seq Data Analysismentioning

confidence: 99%

Emerging deep learning methods for single‐cell RNA‐seq data analysis

Zheng

Wang

2019

Quant. Biol.

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“…2. Taking the diffusion state matrix about drug as an input example, by adding noise to the input training data and making the self-encoder learn to remove this noise, the real input which has not been polluted by noise can be obtained [24]. Therefore, the encoder can obtain the most essential features from the original input to get more robust representation.…”

Section: Denoising-autoencoder-based Feature Selectormentioning

confidence: 99%

A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network

Peng

Shang

2020

BMC Bioinformatics

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Background Drug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and thus is a vital step in drug discovery. Because of the particularity of biochemical experiments, the development of new drugs is not only costly, but also time-consuming. Therefore, the computational prediction of drug target interactions has become an essential way in the process of drug discovery, aiming to greatly reducing the experimental cost and time. Results We propose a learning-based method based on feature representation learning and deep neural network named DTI-CNN to predict the drug-target interactions. We first extract the relevant features of drugs and proteins from heterogeneous networks by using the Jaccard similarity coefficient and restart random walk model. Then, we adopt a denoising autoencoder model to reduce the dimension and identify the essential features. Third, based on the features obtained from last step, we constructed a convolutional neural network model to predict the interaction between drugs and proteins. The evaluation results show that the average AUROC score and AUPR score of DTI-CNN were 0.9416 and 0.9499, which obtains better performance than the other three existing state-of-the-art methods. Conclusions All the experimental results show that the performance of DTI-CNN is better than that of the three existing methods and the proposed method is appropriately designed.

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“…The final step of the analysis would be to investigate the interactome. One way to achieve this is to implement GOAE (Gene Ontology Autoencoder) or GONN (Gene Ontology Neural Network), where genetic interaction networks are created using neural networks 30 . These two methods can be combined with GO further to understand the biological meaning behind the formed networks.…”

Section: Introductionmentioning

confidence: 99%

What has single‐cell RNA sequencing revealed about microglial neuroimmunology?

Kubick

Flournoy²,

Klimovich³

et al. 2020

Immunity Inflam & Disease

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The use of single‐cell RNA sequencing (scRNA‐seq) in microglial research is increasing rapidly. The basic workflow of this approach consists of isolating single cells, followed by sequencing. scRNA‐seq is capable of examining microglial heterogeneity on a cellular level. However, the results gained from applying this technique suffer from discrepancies due to differences between applied methods characteristics such as the number of cells sequenced and the depth of sequencing. This review aims to shed more light on the recent developments that happened in this field and how they are related to the methods used. To do that, we track the progress and limitations of various scRNA‐seq methods currently available. The review then summarizes the current knowledge gained using scRNA‐seq in the field of microglia, including novel subpopulations associated with function and development under homeostasis as well during several pathological conditions such as Alzheimer, lipopolysaccharide response, and HIV in relation to the methods employed. Our review points out that despite major developments found using this technique, current scRNA‐seq methods suffer from high cost, low yields, and nonstandardization of generated data. Additional development of scRNA‐seq methods will raise our awareness of microglia's heterogeneity and plasticity under healthy and pathological conditions.

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Combining Gene Ontology with Deep Neural Networks to Enhance the Clustering of Single Cell RNA-Seq Data

Cited by 10 publications

References 33 publications

Emerging deep learning methods for single‐cell RNA‐seq data analysis

Emerging deep learning methods for single‐cell RNA‐seq data analysis

A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network

What has single‐cell RNA sequencing revealed about microglial neuroimmunology?

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