Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation

Huang, Yiran; Chen, Fuhao; Sun, Hongtao; Zhong, Cheng

doi:10.1186/s12859-024-05662-4

Cited by 6 publications

(3 citation statements)

References 65 publications

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“…Yu et al [ 14 ] performed differential expression analysis on biologically important genes in the gene regulatory networks and constructed a machine learning-based binary classification model for each breast cancer subtype using the differential expression genes. Each type of omics data exhibits specific disease associations [ 15 , 16 ]. However, the analysis of single omics data do not capture the interrelationships between molecules at different levels, which may fail to provide a comprehensive understanding of the biological processes of breast cancer [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning

Huang,

Zeng,

Zhong

2024

BMC Bioinformatics

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Background Classifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large number of multi-omics biological data. Leveraging and integrating the available multi-omics data can effectively enhance the accuracy of identifying breast cancer subtypes. However, few efforts focus on identifying the associations of different omics data to predict the breast cancer subtypes. Results In this paper, we propose a differential sparse canonical correlation analysis network (DSCCN) for classifying the breast cancer subtypes. DSCCN performs differential analysis on multi-omics expression data to identify differentially expressed (DE) genes and adopts sparse canonical correlation analysis (SCCA) to mine highly correlated features between multi-omics DE-genes. Meanwhile, DSCCN uses multi-task deep learning neural network separately to train the correlated DE-genes to predict breast cancer subtypes, which spontaneously tackle the data heterogeneity problem in integrating multi-omics data. Conclusions The experimental results show that by mining the associations among multi-omics data, DSCCN is more capable of accurately classifying breast cancer subtypes than the existing methods.

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Section: Introductionmentioning

confidence: 99%

Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning

Huang,

Zeng,

Zhong

2024

BMC Bioinformatics

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“…Therefore, the classification of breast cancer subtypes is of great importance for the precision treatment and prognosis prediction of breast cancer [5][6][7]. To analyze the heterogeneous genetic data related to breast cancer, multi-omics data can be leveraged [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

DiffRS-net: A Novel Framework for Classifying Breast Cancer Subtypes on Multi-Omics Data

Zeng,

Huang,

Huang

2024

Applied Sciences

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The precise classification of breast cancer subtypes is crucial for clinical diagnosis and treatment, yet early symptoms are often subtle. The use of multi-omics data from high-throughput sequencing can improve the classification accuracy. However, most research primarily focuses on the association between individual omics data and breast cancer, neglecting the interactions between different omics. This may fail to provide a comprehensive understanding of the biological processes of breast cancer. Here, we propose a novel framework called DiffRS-net for classifying breast cancer subtypes by identifying the association among different omics. DiffRS-net performs a differential analysis on each omics datum to identify differentially expressed genes (DE-genes) and adopts a robustness-aware Sparse Multi-View Canonical Correlation Analysis to detect multi-way association among DE-genes. These DE-genes with high levels of correlation are then used to train an attention learning network, thereby enhancing the prediction accuracy of breast cancer subtypes. The experimental results show that, by mining the associations between multi-omics data, DiffRS-net achieves a more accurate classification of breast cancer subtypes than the existing methods.

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“…Enzyme is one of the important proteins in living organisms and plays a catalytic role in various processes of life activities, including metabolism, nutrition, and energy conversion [ 1 , 2 ]. It is thus of great significance to identify the function of protein enzymes expressed by genes [ 3–5 ]. According to the Swiss-Prot database [ 6 ] (as of June 2023), 274,340 out of 570 420 manually annotated proteins are enzymes.…”

Section: Introductionmentioning

confidence: 99%

GloEC: a hierarchical-aware global model for predicting enzyme function

Huang,

Lin,

Lan

et al. 2024

Briefings in Bioinformatics

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The annotation of enzyme function is a fundamental challenge in industrial biotechnology and pathologies. Numerous computational methods have been proposed to predict enzyme function by annotating enzyme labels with Enzyme Commission number. However, the existing methods face difficulties in modelling the hierarchical structure of enzyme label in a global view. Moreover, they haven’t gone entirely to leverage the mutual interactions between different levels of enzyme label. In this paper, we formulate the hierarchy of enzyme label as a directed enzyme graph and propose a hierarchy-GCN (Graph Convolutional Network) encoder to globally model enzyme label dependency on the enzyme graph. Based on the enzyme hierarchy encoder, we develop an end-to-end hierarchical-aware global model named GloEC to predict enzyme function. GloEC learns hierarchical-aware enzyme label embeddings via the hierarchy-GCN encoder and conducts deductive fusion of label-aware enzyme features to predict enzyme labels. Meanwhile, our hierarchy-GCN encoder is designed to bidirectionally compute to investigate the enzyme label correlation information in both bottom-up and top-down manners, which has not been explored in enzyme function prediction. Comparative experiments on three benchmark datasets show that GloEC achieves better predictive performance as compared to the existing methods. The case studies also demonstrate that GloEC is capable of effectively predicting the function of isoenzyme. GloEC is available at: https://github.com/hyr0771/GloEC.

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Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation

Cited by 6 publications

References 65 publications

Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning

Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning

DiffRS-net: A Novel Framework for Classifying Breast Cancer Subtypes on Multi-Omics Data

GloEC: a hierarchical-aware global model for predicting enzyme function

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