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DGEPN-GCEN2V: a new framework for mining GGI and its application in biomarker detection

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References

  1. Xuan Q, Fang B W, Liu Y, et al. Automatic pearl classification machine based on a multistream convolutional neural network. IEEE Trans Ind Electron, 2018, 65: 6538–6547

    Article  Google Scholar 

  2. Qu W, Wang D L, Feng S, et al. A novel cross-modal hashing algorithm based on multimodal deep learning. Sci China Inf Sci, 2017, 60: 092104

    Article  Google Scholar 

  3. Danaee P, Ghaeini R, Hendrix D A. A deep learning approach for cancer detection and relevant gene identification. In: Proceedings of Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing, Hawaii, 2017, 219–229

    Google Scholar 

  4. Chen J Y, Wu Y Y, Lin X, et al. DOE-AND-SCA: a novel SCA based on DNN with optimal eigenvectors and automatic cluster number determination. IEEE Access, 2018, 6: 20764–20778

    Article  Google Scholar 

  5. Chira C, Sedano J, Villar J R, et al. Gene clustering for time-series microarray with production outputs. Soft Comput, 2016, 20: 4301–4312

    Article  Google Scholar 

  6. Singh V, Baranwal N, Sevakula R K, et al. Layerwise feature selection in stacked sparse auto-encoder for tumor type prediction. In: Proceedings of Bioinformatics and Biomedicine, Shenzhen, 2016. 1542–1548

    Google Scholar 

  7. Liang M X, Li Z Z, Chen T, et al. Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach. IEEE/ACM Trans Comput Biol Bioinf, 2015, 12: 928–937

    Article  Google Scholar 

  8. Xie R, Quitadamo A, Cheng J L, et al. A predictive model of gene expression using a deep learning framework. In: Proceedings of Bioinformatics and Biomedicine, Shenzhen, 2016, 676–681

    Google Scholar 

  9. Chen Y F, Li Y, Narayan R, et al. Gene expression inference with deep learning. Bioinformatics, 2016, 32: 1832–1839

    Article  Google Scholar 

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Acknowledgments

This work was partially supported by Zhejiang Natural Science Foundation (Grant No. LY19F020025), National Natural Science Foundation of China (Grant Nos. 61502423, 61572439), Zhejiang University Open Fund (Grant No. 2018KFJJ07), Zhejiang Science and Technology Plan Project (Grant Nos. LGF18F030009, 2017C33149), and Zhejiang Outstanding Youth Fund (Grant No. LR19F030001).

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Correspondence to Qi Xuan.

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Chen, J., Zheng, H., Xiong, H. et al. DGEPN-GCEN2V: a new framework for mining GGI and its application in biomarker detection. Sci. China Inf. Sci. 62, 199104 (2019). https://doi.org/10.1007/s11432-018-9704-7

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  • DOI: https://doi.org/10.1007/s11432-018-9704-7

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