An Ovarian Cancer Susceptible Gene Prediction Method Based on Deep Learning Methods

Abstract

Ovarian cancer (OC) is one of the most fatal diseases among women all around the world. It is highly lethal because it is usually diagnosed at an advanced stage which may reduce the survival rate greatly. Even though most of the patients are treated timely and effectively, the survival rate is still low due to the high recurrence rate of OC. With a large number of genome-wide association analysis (GWAS)-discovered risk regions of OC, expression quantitative trait locus (eQTL) analyses can explore candidate susceptible genes based on these risk loci. However, a large number of OC-related genes remain unknown. In this study, we proposed a novel gene prediction method based on different omics data and deep learning methods to identify OC causal genes. We first employed graph attention network (GAT) to obtain a compact gene feature representation, then a deep neural network (DNN) is utilized to predict OC-related genes. As a result, our model achieved a high AUC of 0.761 and AUPR of 0.788, which proved the accuracy and effectiveness of our proposed method. At last, we conducted a gene-set enrichment analysis to further explore the mechanism of OC. Finally, we predicted 245 novel OC causal genes and 10 top related KEGG pathways.

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

“…Deep learning methods, particularly attention mechanisms and transformer models, have seen profound advancements and deployments in this regard. Studies by Gong et al [111], Kayikci and Khoshgoftaar [112], Ye et al [113], and Wang et al [115] have extensively utilized such methods for biomedical data classification and disease prediction.…”

“…It demonstrated the potential for significant improvements in breast cancer detection and diagnosis, suggesting better patient outcomes. Ye et al [113] implemented a novel gene prediction method using a Siamese neural network, a deep learning architecture that employs twin branches with shared weights to compare and distinguish similarity or dissimilarity between input samples, containing a lightweight attention module for identifying ovarian cancer causal genes. This approach outperformed others in accuracy and effectiveness.…”

Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021–2023 Literature

“…Deep learning (unspecified/generic) [92][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][108][109] This category encompasses general deep learning approaches, which may include a variety of architectures and techniques. These approaches often involve several minor modifications or adaptations to cater to the specificities of the task at hand, without specializing in a particular method or model like the other categories.…”

“…The model exhibited significant potential in gene discovery and second opinions. In a similar vein, Ye et al [98] and Guo et al [99] proposed models for predicting susceptibility to ovarian cancer and prognosis of prostate cancer, respectively, underlining the versatility of deep learning techniques across various cancer types.…”

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

“…Finally, 34 biomarkers and 19 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with OC were identified. Similarly, Ye et al(105) identified the pathogenic genes of OC based on omics data and DL. CNN was used to predict OC-related genes, and the AUC and the area under precision-recall curve of the model were 0.761 and 0.788 respectively, which proved the accuracy and effectiveness of the model.…”