September 11, 2025Open Access

MultiGRNFormer: A Transformer-Based Multi-Omics GRN Inference Framework

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Authors

HCHao ChenZhejiang University of Water Resource and Electric Power CCChao ChengChongqing University of Posts and Telecommunications JWJianxin WangCentral South University

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Implication

This study demonstrates a Transformer-based approach for multi-omics gene regulatory network inference, highlighting improved accuracy through data integration and augmentation.

Key Points

The MultiGRNFormer model improves gene regulatory network inference accuracy through multi-omics data integration.
Experiments showed superior performance compared to existing methods, demonstrating the importance of combining data types.
Key features include leveraging a Transformer encoder and employing a positional encoding mechanism for input sensitivity.
These innovations suggest new directions for deep learning techniques in gene regulatory network research.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68d41711713b0b5dfea629e2 https://doi.org/10.20944/preprints202509.0669.v1

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Implication

This study demonstrates a Transformer-based approach for multi-omics gene regulatory network inference, highlighting improved accuracy through data integration and augmentation.

MultiGRNFormer: A Transformer-Based Multi-Omics GRN Inference Framework

Discussion

Member takes

Implication

Key Points

Cite This Study

MultiGRNFormer: A Transformer-Based Multi-Omics GRN Inference Framework

Discussion

Member takes

Implication

Key Points

Cite This Study

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