@misc{mo_su_yuan_xiao_zhang_lan_huang_2021, 
  title={Comparisons of forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Six Machine Learning Models Based on Multi-Omics}, 
  DOI={10.21203/rs.3.rs-1100398/v1}, 
  abstractNote={<jats:title>Abstract</jats:title>
        <jats:p><jats:bold>Background</jats:bold>: Machine learning methods showed excellent predictive ability in a wide range of fields. For the survival of head and neck squamous cell carcinoma (HNSC), its multi-omics influence is crucial. This study attempts to establish a variety of machine learning multi-omics models to predict the survival of HNSC and find the most suitable machine learning prediction method. <jats:bold>Results</jats:bold>: For omics of HNSC, the results of the six models all showed that the performance of multi-omics was better than each single-omic alone. Results were presented which showed that the BN model played a good prediction performance (area under the curve [AUC] 0.8250) in HNSC multi-omics data. The other machine learning models RF (AUC = 0.8002), NN (AUC = 0.7200), and GLM (AUC = 0.7145) also showed high predictive performance except for DT(AUC = 0.5149) and SVM(AUC = 0.6981). And the results of a vitro qPCR were consistent with the Random forest algorithm. <jats:bold>Conclusion:</jats:bold> Machine learning methods could better forecast the survival outcome of HNSC. Meanwhile, this study found that the Bayesian network was the most superior. Moreover, the forecast result of multi-omics was better than single-omic alone in HNSC.</jats:p>}, 
  publisher={Research Square Platform LLC}, 
  author={Mo, Liying and Su, Yuangang and Yuan, Jianhui and Xiao, Zhiwei and Zhang, Ziyan and Lan, Xiuwan and Huang, Daizheng}, 
  year={2021}, 
  month={Nov}
  }