A Hybrid VAE Based Network Embedding Method for Biomedical Relation Mining

Bai, Tian; Li, Ying; Wang, Ye; Huang, Lan

doi:10.1007/s11063-021-10454-5

Cited by 2 publications

(3 citation statements)

References 24 publications

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“…Applications of graph embedding techniques for mass spectrometry- and sequencing-based data covered in this review are summarized in Table 2 [ 26 , 31 , 33 , 92 , 97 , 98 ]. By their nature, certain—OMICs data can be stored in a graph data structure.…”

Section: Applications Of Graph Embeddings In Mass Spectrometry- and S...mentioning

confidence: 99%

“…However, computational tools to study graph data structures in biological graphs can suffer from high computational and space costs, especially in large-scale information containing graphs [ 28 ]. Graph embedding algorithms can then be used to identify interactions between heterogeneous nodes such as: drug–target [ 26 , 99 – 101 ], miRNA-disease [ 30 , 31 ], miRNA-target [ 32 ], miRNA-gene [ 32 ], microbe-drug [ 102 ], gene–disease [ 31 , 103 ], gene–pathway [ 31 ], cell–gene [ 104 ], chemical–disease [ 31 ]. On the other hand, the interaction between homogeneous nodes may be protein–protein [ 26 – 29 ], drug–drug [ 34 , 100 , 102 ], microbe-microbe [ 102 ], gene–gene [ 104 ].…”

Section: Applications Of Graph Embeddings In Mass Spectrometry- and S...mentioning

confidence: 99%

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Graph embedding on mass spectrometry- and sequencing-based biomedical data

Alvarez-Mamani,

Dechant,

Beltran-Castañón

et al. 2024

BMC Bioinformatics

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Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction, community detection, and visualization. Although typically used in the context of guessing friendships in social media, several applications for graph embedding techniques in biomedical data analysis have emerged. While these approaches remain computationally demanding, several developments over the last years facilitate their application to study biomedical data and thus may help advance biological discoveries. Therefore, in this review, we discuss the principles of graph embedding techniques and explore the usefulness for understanding biological network data derived from mass spectrometry and sequencing experiments, the current workhorses of systems biology studies. In particular, we focus on recent examples for characterizing protein–protein interaction networks and predicting novel drug functions.

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Section: Applications Of Graph Embeddings In Mass Spectrometry- and S...mentioning

confidence: 99%

Section: Applications Of Graph Embeddings In Mass Spectrometry- and S...mentioning

confidence: 99%

Graph embedding on mass spectrometry- and sequencing-based biomedical data

Alvarez-Mamani,

Dechant,

Beltran-Castañón

et al. 2024

BMC Bioinformatics

View full text Add to dashboard Cite

show abstract

“…Here we use VAE [5,19], K-means, and random forest algorithms as our algorithm conductor that is known as the hybrid VKR described as Algorithm 1. From there, the data is fed into the main training loop of the VAE.…”

Section: Vkr Algorithm Analysismentioning

confidence: 99%

Predicting Partner’s Digital Transformation Based on Artificial Intelligence

Ding

2021

Applied Sciences

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Partner’s digital transformation is one of the most important metrics for businesses, particularly for businesses in the subscription world. Hence, how to predict partner transformation is a consistent focus in the industry. In this paper, we use an AI (Artificial Intelligence) relevant algorithm to analyze partner’s digital transformation issues and propose a novel method, named the hybrid VKR (VAE, K-means, and random forest) algorithm, to predict partner transformation. We apply our algorithm to partner transformation issues. First, we show the prediction of about 5980 partners from 25689 partners, who are transformed and sorted according to important indicators. Secondly, we recap the tremendous effort that was required by the company to obtain high-quality results for economic change when a partner is transforming along with one or many of the transformation dimensions. Finally, we identify unethical behavior by looking through deal transaction data. Overall, our work sheds light on several potential problems in partner transformation and calls for improved scientific practices in this area.

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A Hybrid VAE Based Network Embedding Method for Biomedical Relation Mining

Cited by 2 publications

References 24 publications

Graph embedding on mass spectrometry- and sequencing-based biomedical data

Graph embedding on mass spectrometry- and sequencing-based biomedical data

Predicting Partner’s Digital Transformation Based on Artificial Intelligence

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