Abstract
Prediction of differential essential genes is an important field to research cell development and differentiation, drug discovery and disease causes. The goal of this work is to extract gene expression and topological changes in biomolecular networks for identifying the essential nodes or modules. Based on the random forests model, this paper proposed an essential node prediction algorithm for biomolecular networks called Differential Network Analysis method based on Random Forests (DNARF). The algorithm had two main points. First, the five-dimension eigenvector construction method was put forward to extract the differential information of nodes in networks. Second, a positive sample expansion method based on the Pearson correlation coefficient was present to solve the problem that positive and negative samples may be unbalanced. In the simulated data experiments, the DNARF algorithm was compared with three other algorithms. The results showed that the DNARF had an excellent performance on the prediction of essential genes. In the real data experiments, four gene regulatory networks were used as datasets. DNARF algorithm predicted five essential genes related to leukemia: HES1, STAT1, TAL1, SPI1 and RFXANK, which had been proved by literatures. Also, DNARF could be applied to other biological networks to identify new essential genes.
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Acknowledgement
This work was partially supported by the National Natural Science Foundation of China [No. 61873156] and the Project of NSFS [No. 17ZR1409900].
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Xie, J., Sun, J., Li, J., Yang, F., Li, H., Wang, J. (2019). A Novel Differential Essential Genes Prediction Method Based on Random Forests Model. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_51
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