SAGESDA: Multi-GraphSAGE networks for predicting SnoRNA-disease associations

Momanyi, Biffon Manyura; Zhou, Yu-Wei; Grace-Mercure, Bakanina Kissanga; Temesgen, Sebu Aboma; Basharat, Ahmad; Ning, Lin; Tang, Lixia; Gao, Hui; Lin, Hao; Tang, Hua

doi:10.1016/j.crstbi.2023.100122

Cited by 4 publications

(2 citation statements)

References 44 publications

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“…This feature allows GraphSAGE to effectively handle large graphs by learning from a sample of the nodes, and it can be used to generate representations of new nodes. While GraphSAGE was designed to work with a single graph, some attempts have been performed to extend it to two interconnected graphs [ 41 ].…”

Section: Methodsmentioning

confidence: 99%

Integrative Multi-Omics Analysis for Etiology Classification and Biomarker Discovery in Stroke: Advancing towards Precision Medicine

Labarga,

Martínez-Gonzalez,

Barajas

2024

Biology

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Recent advancements in high-throughput omics technologies have opened new avenues for investigating stroke at the molecular level and elucidating the intricate interactions among various molecular components. We present a novel approach for multi-omics data integration on knowledge graphs and have applied it to a stroke etiology classification task of 30 stroke patients through the integrative analysis of DNA methylation and mRNA, miRNA, and circRNA. This approach has demonstrated promising performance as compared to other existing single technology approaches.

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Section: Methodsmentioning

confidence: 99%

Integrative Multi-Omics Analysis for Etiology Classification and Biomarker Discovery in Stroke: Advancing towards Precision Medicine

Labarga,

Martínez-Gonzalez,

Barajas

2024

Biology

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“…GNNs iteratively update node representations by aggregating information from neighboring nodes, efficiently leveraging both structural and feature information. Various GNN architectures [ 23 , 24 , 25 , 26 ] exhibit distinct characteristics and are employed in diverse scenarios. Some recent studies advocate utilizing a gene interaction graph [ 27 ], with gene expression serving as input, to predict breast cancer survival [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning

Ma,

Li,

Chu

et al. 2024

Sensors

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Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Among various cancers, breast cancer stands out due to its genetic underpinnings, which trigger uncontrolled cell proliferation, predominantly impacting women, and resulting in significant mortality rates. The utilization of biosensors in predicting survival time becomes paramount in formulating an optimal treatment strategy. However, conventional biosensors employing traditional machine learning methods encounter challenges in preprocessing features for the learning task. Despite the potential of deep learning techniques to automatically extract useful features, they often struggle to effectively leverage the intricate relationships between features and instances. To address this challenge, our study proposes a novel smart biosensor architecture that integrates a multi-view multi-way graph learning (MVMWGL) approach for predicting breast cancer survival time. This innovative approach enables the assimilation of insights from gene interactions and biosensor similarities. By leveraging real-world data, we conducted comprehensive evaluations, and our experimental results unequivocally demonstrate the superiority of the MVMWGL approach over existing methods.

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