Analysis and Prediction Model of Bioengineering Experimental Data Integrating Big Data and Deep Learning

The goal of this paper is to establish an analysis and prediction model of bioengineering experimental data combining big data and deep learning (DL), so as to improve the analysis efficiency of bioengineering experimental data. In this study, gene expression data and protein sequence data were used as experimental data sets, and data preprocessing and model training were carried out. It is not difficult to find that the model performs well in the analysis and prediction of gene expression data and protein sequence data, with low mean square error and small prediction error. At the same time, the accuracy, recall and F1 value all reached a high level. Compared with the traditional machine learning model, this model has obviously higher prediction accuracy and good generalization ability. Therefore, it can be considered that the analysis and prediction model of bioengineering experimental data combining big data and DL has broad application prospects in the field of bioengineering, and provides a new and effective method for the analysis and prediction of bioengineering experimental data.