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Cell Type Prediction for Single-cell RNA Sequencing based on Unsupervised Domain Adaptation and Semi-supervised Learning
http://doi.org/10.5626/JOK.2025.52.2.125
Single-cell RNA sequencing (scRNA-seq) techniques for measuring gene expression in individual cells have developed rapidly. Recently, deep learning has been employed to identify cell types in scRNA-seq analysis. Most methods utilize a dataset containing cell-type labels to train the model and then apply this model to other datasets. However, integrating multiple datasets can result in unexpected batch effects caused by variations in laboratories, experimenters, and sequencing techniques. Since batch effect can obscure the biological signals of interest, an effective batch correction method is essential. In this paper, we present a cell-type prediction model for scRNA-seq that utilizes unsupervised domain adaptation and semi-supervised learning to minimize distributional differences between datasets. First, we pre-train the proposed model using a source dataset that contains cell-type information. Subsequently, we train the model on the target dataset by leveraging adversarial training to align its distribution of the target dataset with that of the source dataset. Finally, we re-train the model to enhance performance through semi-supervised learning, utilizing both the source and target datasets with consistency regularization. The proposed model outperformed the other deep learning-based batch correction models by effectively removing batch effects.
Breast Cancer Subtype Classification Using Multi-omics Data Integration Based on Neural Network
Joungmin Choi, Jiyoung Lee, Jieun Kim, Jihyun Kim, Heejoon Chae
http://doi.org/10.5626/JOK.2020.47.9.835
Breast cancer is one of the highly heterogeneous diseases comprising multiple biological factors, causing multiple subtypes. Early diagnosis and accurate subtype prediction of breast cancer play a critical role in the prognosis of cancer and are crucial to providing appropriate treatment for each patient with different subtypes. To identify significant patterns from enormous volumes of genetic and epigenetic data, machine learning-based methods have been adopted to the breast cancer subtype classification. Recently, multi-omics data integration has attracted much attention as a promising approach in recognizing complex molecular mechanisms and providing a comprehensive view of patients. However, because of the characteristics of high dimensionality, multi-omics based approaches are limited in prediction accuracy. In this paper, we propose a neural network-based breast cancer subtype classification model using multi-omics data integration. The gene expression, DNA methylation, and miRNA omics dataset were integrated after preprocessing and the classification model was trained based on the neural network using the dataset. Our performance evaluation results showed that the proposed model outperforms all other methods, providing the highest classification accuracy of 90.45%. We expect this model to be useful in predicting the subtypes of breast cancer and improving patients’ prognosis.
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