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GEnDDn: An lncRNA–Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network

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Abstract

Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA–disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising insights into cancer targeted therapy and anti-cancer drug design. Here, we present an LDA prediction framework called GEnDDn based on deep learning. GEnDDn mainly comprises two steps: First, features of both lncRNAs and diseases are extracted by combining similarity computation, non-negative matrix factorization, and graph attention auto-encoder, respectively. And each lncRNA–disease pair (LDP) is depicted as a vector based on concatenation operation on the extracted features. Subsequently, unknown LDPs are classified by aggregating dual-net neural architecture and deep neural network. Using six different evaluation metrics, we found that GEnDDn surpassed four competing LDA identification methods (SDLDA, LDNFSGB, IPCARF, LDASR) on the lncRNADisease and MNDR databases under fivefold cross-validation experiments on lncRNAs, diseases, LDPs, and independent lncRNAs and independent diseases, respectively. Ablation experiments further validated the powerful LDA prediction performance of GEnDDn. Furthermore, we utilized GEnDDn to find underlying lncRNAs for lung cancer and breast cancer. The results elucidated that there may be dense linkages between IFNG-AS1 and lung cancer as well as between HIF1A-AS1 and breast cancer. The results require further biomedical experimental verification. GEnDDn is publicly available at https://github.com/plhhnu/GEnDDn.

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Availability of Data and Materials

Datasets and codes are publicly available at https://github.com/plhhnuGEnDDn.

Code Availability

Datasets and codes are publicly available at https://github.com/plhhnu/GEnDDn.

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Acknowledgements

We are very thankful for three anonymous reviewers and all authors of references.

Funding

L.H. Peng was supported by National Natural Science Foundation of China under Grant nos. 62072172 and 61803151 and Natural Science Foundation of Hunan Province of China under Grant 2023JJ50201. M. Chen was supported by National Natural Science Foundation of China under Grant no. 62172158.

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  1. Lihong Peng, Mengnan Ren and Liangliang Huang have contributed equally to this work and share first authorship.

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  1. College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, China

    Lihong Peng, Mengnan Ren & Liangliang Huang

  2. School of Computer Science, Hunan Institute of Technology, Hengyang, 421002, China

    Min Chen

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Contributions

L.H. Peng and L.L. Huang: conceptualization; L.H. Peng, and M. Chen: funding acquisition; L.H. Peng, L.L. Huang, and M. Chen: project administration; L.H. Peng and M.N. Ren: writing—original draft; L.H. Peng and M. Chen: writing—review and editing; L.H. Peng, M.N. Ren, L.L. Huang, and M. Chen: investigation; L.H. Peng, M.N. Ren, and L.L. Huang: methodology; L.L. Huang and M.N. Ren: software; L.H. Peng, L.L. Huang, and M. Chen: validation. All authors contributed to the article and approved the submitted version.

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Correspondence to Min Chen.

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Peng, L., Ren, M., Huang, L. et al. GEnDDn: An lncRNA–Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network. Interdiscip Sci Comput Life Sci (2024). https://doi.org/10.1007/s12539-024-00619-w

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