Identification of Cancer Driver Genes by Integrating Multiomics Data with Graph Neural Networks

Song, Hongzhi; Yin, Chaoyi; Li, Zhuopeng; Feng, Kai; Cao, Yangkun; Gu, Yujie; Sun, Huiyan

doi:10.3390/metabo13030339

Cited by 6 publications

(3 citation statements)

References 64 publications

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“…There is also a need for methods that can effectively deal with the limited availability of labeled data in genomics. One promising approach is to leverage unsupervised or semi-supervised learning techniques, which can make use of unlabeled data to improve model performance [158][159][160]. Transfer learning, where a model trained on a large dataset is fine-tuned on a smaller, task-specific dataset, could also be a promising approach for dealing with the scarcity of labeled data [161][162][163].…”

Section: Future Workmentioning

confidence: 99%

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

Choi

Lee

2023

Biology

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The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors.

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Section: Future Workmentioning

confidence: 99%

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

Choi

Lee

2023

Biology

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“…1 -4.6). Some of the earliest work in the cancer-GNN junction aimed at the prediction of cancer driver genes with GCNs [109]; this was followed with a comprehensive study by Song et al [110] developing a robust multimodal (36 features plus PPI) GAT-centered framework for identification of driver genes across different cancers. Yang et al [111] focused on a narrower problem of identifying a small number of genes for a cancer-specific tumor mutational burden estimation panel, essential for estimating the potential effectiveness of immune checkpoint inhibitor therapy.…”

Section: Other Research Directions Activities and Modalitiesmentioning

confidence: 99%

Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends

Gogoshin,

Rodin

2023

Preprint

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Next-generation cancer and oncology research needs to take full advantage of the multi-modal structured, or graph, information, with the graph datatypes ranging from molecular structures to spatially resolved imaging and digital pathology to biological networks to knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on the large multi-modal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. Subsequently, we identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise the guidelines for cancer and oncology researchers or physician-scientists asking the question of whether they should adopt the GNN methodology in their research pipelines.

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“…Currently, with the development of computer vision, deep learning has been widely applied in agriculture [4][5][6], medicine [7][8][9], and other fields. Though the classification of Gastrodia elata takes both weights and shape into consideration, it is only sorted by manual experience or only considering weight, leading to low sorting accuracy and a heavy workload.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Detection of Gastrodia elata Based on Improved YOLOX

Duan,

Lin,

et al. 2023

Agronomy

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Identifying the grade of Gastrodia elata in the market has low efficiency and accuracy. To address this issue, an I-YOLOX object detection algorithm based on deep learning and computer vision is proposed in this paper. First, six types of Gastrodia elata images of different grades in the Gastrodia elata planting cooperative were collected for image enhancement and labeling as the model training dataset. Second, to improve feature information extraction, an ECA attention mechanism module was inserted between the backbone network CSPDarknet and the neck enhancement feature extraction network FPN in the YOLOX model. Then, the impact of the attention mechanism and application position on model improvement was investigated. Third, the 3 × 3 convolution in the neck enhancement feature extraction network FPN and the head network was replaced by depthwise separable convolution (DS Conv) to reduce the model size and computation amount. Finally, the EIoU loss function was used to predict boundary frame regression at the output prediction end to improve the convergence speed of the model. The experimental results indicated that compared with the original YOLOX model, the mean average precision of the improved I-YOLOX network model was increased by 4.86% (97.83%), the model computation was reduced by 5.422 M (reaching 3.518 M), the model size was reduced by 20.6 MB (reaching 13.7 MB), and the image frames detected per second increased by 3 (reaching 69). Compared with other target detection algorithms, the improved model outperformed Faster R-CNN, SSD-VGG, YOLOv3s, YOLOv4s, YOLOv5s, and YOLOv7 algorithms in terms of mean average precision, model size, computation amount, and frames per second. The lightweight model improved the detection accuracy and speed of different grades of Gastrodia elata and provided a theoretical basis for the development of online identification systems of different grades of Gastrodia elata in practical production.

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Identification of Cancer Driver Genes by Integrating Multiomics Data with Graph Neural Networks

Cited by 6 publications

References 64 publications

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

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

Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends

Hierarchical Detection of Gastrodia elata Based on Improved YOLOX

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