Using molecular fingerprints and unsupervised learning algorithms to find simulants of chemical warfare agents

Gu, Leilei; Zhang, Xinxin; Li, Ké; Jia, Guozhu

doi:10.1088/1742-6596/1684/1/012072

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…GMM is a probabilistic clustering method, which also belongs to the generative model. It assumes that all the data points are generated from a mixture of a finite number of Gaussian distributions ( Gu et al, 2020 ). GMM model has excellent clustering performance.…”

Section: Methodsmentioning

confidence: 99%

SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition

Sun

Cheng²,

Meng³

et al. 2023

Front. Genet.

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Integrating multi-omics data for cancer subtype recognition is an important task in bioinformatics. Recently, deep learning has been applied to recognize the subtype of cancers. However, existing studies almost integrate the multi-omics data simply by concatenation as the single data and then learn a latent low-dimensional representation through a deep learning model, which did not consider the distribution differently of omics data. Moreover, these methods ignore the relationship of samples. To tackle these problems, we proposed SADLN: A self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition. SADLN combined encoder, self-attention, decoder, and discriminator into a unified framework, which can not only integrate multi-omics data but also adaptively model the sample’s relationship for learning an accurately latent low-dimensional representation. With the integrated representation learned from the network, SADLN used Gaussian Mixture Model to identify cancer subtypes. Experiments on ten cancer datasets of TCGA demonstrated the advantages of SADLN compared to ten methods. The Self-Attention Based Deep Learning Network (SADLN) is an effective method of integrating multi-omics data for cancer subtype recognition.

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Section: Methodsmentioning

confidence: 99%

SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition

Sun

Cheng²,

Meng³

et al. 2023

Front. Genet.

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show abstract

“…The GMM Clustering GMM is a probabilistic clustering method, which also belongs to the generative model. It assumes that all the data points are generated from a mixture of a finite number of Gaussian distributions [42]. GMM model has excellent clustering performance.…”

Section: Discriminatormentioning

confidence: 99%

SADLN: Self-Attention Based Deep Learning Network of Integrating Multi-omics Data for Cancer Subtype Recognition

Gong

Sun

Cheng

et al. 2022

Preprint

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Background: Integrating multi-omics data for cancer subtype recognition is an important task in bioinformatics. Recently, deep learning has applied to recognize the subtype of cancers. However, existing studies almost integrate the multi-omics data simply by concatenation as the single data and then learn a latent low-dimensional representation through deep learning model, which didn't considering the distributes differently of omics data. Moreover, these methods ignore the relationship of samples. Results :In order to tackle these problems, we proposed SADLN: a self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition. SADLN combined encoder, self-attention, decoder, and discriminator into a unified framework, which can not only integrate multi-omics data but also adaptively model the sample’s relationship for learning a accurately latent low-dimensional representation. With the integrated representation learned from the network, SADLN used Gaussian Mixture Model to identify cancer subtypes. Experiments on ten cancer datasets of TCGA demonstrated the advantages of SADLN compared to ten methods. Conclusions: The Self-Attention Based Deep Learning Network (SADLN) is a effective method of integrate multi-omics data for cancer subtype recognition.

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Machine Learning for Drug Discovery Using Agglomerative Hierarchical Clustering

Lakshmi

Varma

2023

Smart Innovation, Systems and Technologies

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Using molecular fingerprints and unsupervised learning algorithms to find simulants of chemical warfare agents

Cited by 5 publications

References 22 publications

SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition

SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition

SADLN: Self-Attention Based Deep Learning Network of Integrating Multi-omics Data for Cancer Subtype Recognition

Machine Learning for Drug Discovery Using Agglomerative Hierarchical Clustering

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