Prediction of Tumor Lymph Node Metastasis Using Wasserstein Distance-Based Generative Adversarial Networks Combing with Neural Architecture Search for Predicting

• Published in: Mathematics (Basel) Vol. 11; no. 3; p. 729
• Main Authors: Wang, Yawen, Zhang, Shihua
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Accuracy; Algorithms; Cancer; Computer architecture; Datasets; Decision trees; Deep learning; Food science; Gene expression; generative adversarial network; Generative adversarial networks; Health aspects; lncRNA; lymph node metastasis; Lymph nodes; Lymphatic system; Machine learning; Medical prognosis; Medical research; Metastasis; Methods; neural architecture search; Neural networks; Optimization algorithms; Particle swarm optimization; Process parameters; Tumors; United States
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11030729

Long non-coding RNAs (lncRNAs) play an important role in development and gene expression and can be used as genetic indicators for cancer prediction. Generally, lncRNA expression profiles tend to have small sample sizes with large feature sizes; therefore, insufficient data, especially the imbalance of positive and negative samples, often lead to inaccurate prediction results. In this study, we developed a predictor WGAN-psoNN, constructed with the Wasserstein distance-based generative adversarial network (WGAN) and particle swarm optimization neural network (psoNN) algorithms to predict lymph node metastasis events in tumors by using lncRNA expression profiles. To overcome the complicated manual parameter adjustment process, this is the first time the neural network architecture search (NAS) method has been used to automatically set network parameters and predict lymph node metastasis events via deep learning. In addition, the algorithm makes full use of the advantages of WGAN to generate samples to solve the problem of imbalance between positive and negative samples in the data set. On the other hand, by constructing multiple GAN networks, Wasserstein distance was used to select the optimal sample generation. Comparative experiments were conducted on eight representative cancer-related lncRNA expression profile datasets; the prediction results demonstrate the effectiveness and robustness of the newly proposed method. Thus, the model dramatically reduces the requirement for deep learning for data quantity and the difficulty of architecture selection and has the potential to be applied to other classification problems.