همایش ملی بیوانفورماتیک ایران

صفحه اصلی / 4th international edition and 13th Iranian Conference on Bioinformatics

Attention based Graph Neural Network for Identifying Coding and Non-coding Breast Cancer Drivers

نویسندگان :

Bahar Mahdavi¹ Mitra Nemati Andavari² Mehdi Rajabizadeh³ Mansoor Rezghi⁴

1- دانشگاه تربیت مدرس 2- دانشگاه تربیت مدرس 3- دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته کرمان 4- دانشگاه تربیت مدرس

کلمات کلیدی :

Graph Neural Network،Attention Mechanism،Breast Cancer Driver Genes،Coding and Non-coding Genes،Precision Medicines

چکیده :

Breast cancer remains one of the foremost causes of cancer-related mortality among women worldwide (Heer et al., 2020, Sung et al., 2021), driven by a complex interplay of genetic and epigenetic alterations. Identifying both coding and non-coding cancer driver genes is crucial for understanding tumorigenic mechanisms, developing targeted therapies, and improving patient prognostics (Byler et al., 2014, Dumitrescu, 2018). Traditional methodologies predominantly focus on coding regions, often overlooking the regulatory roles of non-coding regions in cancer progression. To address this gap, we propose an innovative attention-based graph neural network framework designed to identify both coding and non-coding breast cancer driver genes by leveraging multi-omics data integration and sophisticated attention mechanisms. Our proposed model integrates diverse genomic datasets from The Cancer Genome Atlas (TCGA) (Weinstein et al., 2013), including gene expression profiles, mutation data, copy number variations, methylation patterns, and protein–protein interaction networks. We used these datasets to construct a comprehensive heterogeneous gene network containing various types of nodes and edges. To process this network, we utilized a graph convolutional network (GCN) architecture called the heterogeneous graph transformer (HGT) (Hu et al., 2020). The model captures the intricate relationships and dependencies among genes within the network. Incorporating a self-attention mechanism enables the model to assign different weights to various nodes and interactions, allowing it to focus on the most influential features and effectively filter out noise and irrelevant data, thereby enhancing the identification of critical driver genes that play pivotal roles in cancer development and progression amidst the vast genomic landscape. The framework operates through a two-stage process: (1) constructing a condition-specific breast cancer network that encompasses both coding genes and non-coding RNAs, and (2) applying hierarchical attention layers to prioritize nodes based on their significance within the network. This dual approach not only improves the detection of known coding drivers but also uncovers novel non-coding drivers that regulate key oncogenic pathways. Furthermore, the integration of multi-omics data provides a holistic view of the molecular landscape, facilitating the discovery of driver genes with increased accuracy and biological relevance. Comparative analyses show that our proposed model outperforms state-of-the-art methods like CBNA (Pham et al., 2019), and NIBNA (Chaudhary et al., 2021), achieving superior performance in identifying both coding and non-coding drivers. Notably, it predicted a significant number of novel miRNA and coding drivers, many of which have been validated in recent literature. In conclusion, the attention-based graph neural network offers a robust and scalable solution for the comprehensive identification of coding and non-coding breast cancer driver genes. By leveraging multi-omics data integration and advanced attention mechanisms within a GCN architecture, our proposed model enhances the accuracy of driver gene detection and provides critical insights into the molecular underpinnings of breast cancer. This framework is poised to contribute significantly to the fields of cancer genomics and precision medicine, ultimately aiding in the development of targeted interventions and more effective diagnostic and therapeutic strategies for breast cancer patients.