Transformer-based Feature Extraction Approach for Hematopoietic Cancer Subtype Classification through Gene Expression Profile

다국어 초록 (Multilingual Abstract)

Recently, with the development of computing performance, machine learning, and deep learning, research is being actively conducted to solve various problems using the technology not only in the computer field but also in many other fields. As genetic information is becoming digitized in the field of biology, many studies are being conducted on ways to use computers to solve problems associated with various diseases using the genetic information. Cancer, which is mainly caused by genetic defects in cells, is one of the most active fields, and a lot of research has already been conducted. However, most studies are focused on classifying cancer and normal or cancers of different organs. The challenge is to clinically detect cancer in cells that have the ability to differentiate from a single cell into many different types of cells. Cells with this characteristic are called multipotent cells, and a typical cancer is breast cancer. In the case of breast cancer, the genetic markers of mammary stem cells are clear and have been utilized in various treatments. In contrast, hematopoietic stem cells, which are also multipotent cells, have the ability to differentiate into a wider variety of cells and differentiate in various locations in the body, making early diagnosis and prediction clinically difficult. Nevertheless, hematologic stem cell subtypes of hematologic cancers are less studied than other cancers, and unlike breast cancer, there are no accurate genetic markers of subtype differentiation. Therefore, this dissertation proposes a feature extraction technique using transformers to solve the subtype classification problem of hematopoietic cancer and detect genetic indicators. A transformer is a large structure that utilizes an attachment technique, which is currently being actively utilized in the field of natural language processing (NLP). In NLP, a transformer consists of an encoder and a decoder. The encoder is responsible for extracting contextual meaning, and the decoder is responsible for generating context from the extracted meaning. Using this concept, this dissertation proposes a transformer-based autoencoder (TFAE), a new feature extraction algorithm using a transformer encoder combined with autoencoder, with the goal of obtaining feature information meaningful for subtype classification of hematopoietic cancer.
The details of research contents can be summarized as follows: First, this dissertation presents a transformer-based feature extraction algorithm, TFAE, for gene expression data. TFAE is designed to extract features by using a transformer-encoder to extract important feature of the data, and then extending it to a decoder to create the original. Second, in order to compare the feature extraction of the proposed feature extraction algorithm, different algorithms used for feature extraction are applied. For this purpose, PCA (Principal Component Analysis) and NMF (Non-Negative Factorization), which are widely used statistical-based feature extraction algorithms, and AE (Autoencoder) and VAE (Variational Autoencoder), which are deep learning-based feature extraction algorithms, were applied and compared. Third, multiple classifiers were applied to real tabular genomic data to classify hematopoietic cancer subtypes. Each set of features was applied to eight multiclass classifiers for performance evaluation. Finally, applied XAI (eXplainable Artificial Intelligence) to find genes that are important for hematopoietic cancer subtype classification. For this purpose, this research applied the SHAP (SHapley Additive exPlanations) algorithm, one of the XAI techniques, to show how much the extracted genes affect the subtyping and to detect which genes are important. To achieve these research objectives, the data of five types of blood cancers were collected from TCGA (The Cancer Genome Atlas), a representative open database of genetic information, and experimental data were generated by preprocessing, and then feature extraction algorithms including the proposed TFAE were used to extract genes of the same size. In order to determine how much a particular gene contributes to the subtype classification of hematopoietic cancers, this research applied the SHAP algorithm, one of the explanatory artificial intelligence (AI) techniques, to find the top 20 genes that best classify hematopoietic cancer subtypes.
The overall experimental results show that the feature extraction techniques for each classifier yield reasonable performance for hematopoietic cancer subtype classification but the proposed TFAE algorithm can achieve better results than other feature extraction algorithms. In particular, when TFAE was combined with LGBM to classify hematopoietic cancer subtypes, the best performance was achieved with Accuracy 0.9857, Precision 0.9753, Recall 0.9635, Specificity 0.9963, F1 score 0.9691, G-mean 0.9797, and Balanced accuracy 0.9543. Although other algorithms have lower performance, they showed sufficiently significant performance in classification, confirming that this approach is effective. Consequently, the findings of this dissertation showed that our proposed feature extraction model, namely TFAE, could more accurately classify the hematopoietic cancer subtypes, and the SHAP method could identify the genes which are significant for each subtype classification.
This dissertation can be regarded as one of the studies that showed the research potential of feature extraction techniques for classifier algorithms by applying transformer techniques to biological data, as apply real world hematopoietic cancer data to subtype classification. In the future, plans are to further develop this research and work on feature extraction for biological data using methods with similar representations such as Generative Adversarial Network (GAN) and diffusion.

목차 (Table of Contents)

• Introduction 1
• Gene expression 3
• Measurement of gene expression 5
• Cancer classification research using genetic information 7
• Research motivations 9
• Research objectives and contributions 12
• Organization of the dissertation 14
• Literature Review 16
• Gene expression quantification in RNA-seq analysis 16
• Machine learning-based cancer classification 18
• Deep learning-based cancer classification 20
• Cancer subtype classification 23
• Transformer-based cancer classification 26
• Transformer-based Hematopoietic Cancer Subtype Feature Extraction 29
• Framework of proposed feature extraction 29
• Transformer-based feature extraction 32
• Autoencoder for extracting genetic features 40
• Variational autoencoder for extracting genetic features 43
• Research Methods for Hematopoietic Subtype Classification 46
• Hematopoietic cancer dataset 47
• Deep learning-based feature extraction methods 50
• Statistics-based feature extraction methods 59
• Shapley additive explanations for feature importance 61
• Classification algorithms for subtype classification 62
• Performance measures 71
• Experimental environments 74
• Experimental Results and Discussion 75
• Losses for deep learning feature extraction 75
• Visualization for feature extraction methods 79
• Hematopoietic cancer subtype classification results 85
• SHAP explanations for deep learning feature extraction 110
• Discussion 119
• Conclusion and Future work 125
• References 128
• Appendix 153
• 요약 (Korean) 196

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