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Prediction of Cancer Prognosis Using Patient-Specific Cancer Driver Gene Information
http://doi.org/10.5626/JOK.2024.51.6.574
Accurate prediction of cancer prognosis is crucial for effective treatment. Consequently, numerous studies on cancer prognosis have been conducted, with recent research leveraging various machine learning techniques such as deep learning. In this paper, we first constructed patient-specific gene networks for each patient, then selected patient-specific cancer driver genes, considering the heterogeneity of cancer. We propose a deep neural architecture that can predict the prognosis more accurately using patient-specific cancer driver gene information. When our method was applied to gene expression data for 11 types of cancer, it demonstrated a significantly higher prediction accuracy compared to the existing methods.
A Method for Cancer Prognosis Prediction Using Gene Embedding
http://doi.org/10.5626/JOK.2021.48.7.842
Identifying prognostic genes and using them to predict the prognosis of cancer patients can help provide them with more effective treatments. Many methods have been proposed to identify prognostic genes and predict cancer prognosis, and recent studies have focused on machine learning methods including deep learning. However, applying gene expression data to machine learning methods has the limitations of a small number of samples and a large number of genes. In this study, we additionally use a gene network to generate many random gene paths, which we used for training the model, thereby compensating for the small sample problem. We identified the prognostic genes and predicted the prognosis of patients using the gene expression data and gene networks for five cancer types and confirmed that the proposed method showed better predictive accuracy compared to other existing methods, and good performance on small sample data.
Identification of Heterogeneous Prognostic Genes and Prediction of Cancer Outcome using PageRank
http://doi.org/10.5626/JOK.2018.45.1.61
The identification of genes that contribute to the prediction of prognosis in patients with cancer is one of the challenges in providing appropriate therapies. To find the prognostic genes, several classification models using gene expression data have been proposed. However, the prediction accuracy of cancer prognosis is limited due to the heterogeneity of cancer. In this paper, we integrate microarray data with biological network data using a modified PageRank algorithm to identify prognostic genes. We also predict the prognosis of patients with 6 cancer types (including breast carcinoma) using the K-Nearest Neighbor algorithm. Before we apply the modified PageRank, we separate samples by K-Means clustering to address the heterogeneity of cancer. The proposed algorithm showed better performance than traditional algorithms for prognosis. We were also able to identify cluster-specific biological processes using GO enrichment analysis.
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