FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network - Gu, Hong; Xu, Xiaolu; Qin, Pan; Wang, Jia (Frontiers in Genetics, 2020)
Identification of driver genes, whose mutations cause the development of tumors, is crucial for the improvement of cancer research and precision medicine. To overcome the problem that the traditional frequency-based methods cannot detect lowly recurrently mutated driver genes, researchers have focused on the functional impact of gene mutations and proposed the function-based methods. However, most of the function-based methods estimate the distribution of the null model through the nonparametric method, which is sensitive to sample size. Besides, such methods could probably lead to under-selection or over-selection results. In this study, we proposed a method to identify driver genes by using functional impact prediction neural network (FI-net). An artificial neural network as a parametric model was constructed to estimate the functional impact scores for genes, in which multi-omics features were used as the multivariate inputs. Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 21 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. According to the comprehensive evaluation criterion, FI-net was powerful among various datasets and outperformed the other methods in terms of the precision, the numbers of identified driver genes, and other criteria. Furthermore, the results illustrated that FI-net can identify known and potential novel driver genes.