Abstract
Within the area of bioinformatics, Deep Learning (DL) models have shown exceptional results in applications in which histological images, scans and tomographies are used. However, when gene expression data is under analysis, the performance is often limited, further hampered by the complexity of these models that require several instances, in the order of thousands, to provide good results. Due to the difficulty and the costs involved in the collection of medical data, the application of Data Augmentation (DA) techniques to alleviate the lack of samples is a topic of great relevance. State-of-the-art models based on Conditional Generative Adversarial Networks (CGAN) and some introduced modifications are used in this work to investigate the effect of DA for prediction of the vital status of patients from RNA-Seq gene expression data. Experimental results on several real-world data sets demonstrate the effectiveness and efficiency of the proposed models. The application of DA methods significantly increase prediction accuracy, leading by 12% with respect to benchmark data sets and 3.15% with respect to data processed with feature selection. Results based on CGAN models outperform in most cases, alternative methods like the SMOTE or noise injection techniques.
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Acknowledgements
The authors acknowledge the support from MICINN (Spain) through grant TIN2017-88728-C2-1-R and PID2020-116898RB-I00, from Universidad de Málaga y Junta de Andalucía through grant UMA20-FEDERJA-045, and from Instituto de Investigación Biomédica de Málaga - IBIMA (all including FEDER funds). The results published here are based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
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Moreno-Barea, F.J., Jerez, J.M., Franco, L. (2022). GAN-Based Data Augmentation for Prediction Improvement Using Gene Expression Data in Cancer. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer, Cham. https://doi.org/10.1007/978-3-031-08757-8_3
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