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A Graph Neural Network Approach for Predicting the Lung Carcinogenicity of Single Molecular Compounds
http://doi.org/10.5626/JOK.2025.52.6.482
Cancer is one of the major diseases causing millions of deaths worldwide every year, and lung cancer has been recorded as the leading cause of cancer-related deaths in Korea in 2022. Therefore, research on lung cancer-causing compounds is essential, and this study proposes and evaluates a novel approach to predict lung cancer-causing potential using graph neural networks to overcome the limitations of existing machine learning and deep learning methods. Based on SMILES(Simplified Molecular Input Line Entry System) information from the compound carcinogenicity databases CPDB, CCRIS, IRIS and T3DB, the structure and chemical properties of molecules were converted into graph data for training, and the proposed model showed superior prediction performance compared to other models. This demonstrates the potential of graph neural networks as an effective tool for lung cancer prediction and suggests that they can make important contributions to future cancer research and treatment development.
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.
Homomorphic Encryption-Based Support Computation for Privacy-Preserving Association Analysis
Yunsoo Park, Lynin Sokhonn, Munkyu Lee
http://doi.org/10.5626/JOK.2024.51.3.203
Homomorphic encryption is a cryptographic scheme that enables computation on ciphertexts without decryption. Homomorphic encryption is attracting attention as a cryptographic technology that can solve the issue of user privacy invasion in machine learning and cloud services. A representative scheme of homomorphic encryption is the CKKS scheme. CKKS is an approximate homomorphic encryption scheme that supports real and complex number operations. In this paper, we propose a method to efficiently compute support among evaluation metrics of association analysis using CKKS scheme, and a method to compute supports in parallel using matrix multiplication for multiple itemsets. We implemented and evaluated the proposed method to compute supports using the HEaaN library. According to evaluation results, the support value calculated by the proposed method was almost identical to that calculated without encryption, confirming that the proposed method could effectively calculate the support value while protecting user data privacy.
Improvement of Prostate Cancer Aggressiveness Prediction Based on the Deep Learning Model Using Size Normalization and Multiple Loss Functions on Multi-parametric MR Images
Yoon Jo Kim, Julip Jung, Sung Il Hwang, Helen Hong
http://doi.org/10.5626/JOK.2023.50.10.866
Prostate cancer is the second most common cancer in men worldwide, and it is essential to predict the aggressiveness of prostate cancer because the recurrence rate and the effectiveness of treatment vary depending on the aggressiveness. This study enhances the information on small tumors by applying size normalization to predict the aggressiveness of prostate cancer in multi-parametric MR imaging. Additionally, we propose the use of multiple loss functions to distinguish tumors with different aggressiveness while having a similar visual appearance. Experimental results show that the proposed model trained with size-normalized ADC maps achieves an accuracy of 76.28%, sensitivity of 76.81%, specificity of 75.86%, and an AUC of 0.77. Moreover, compared to the tumor-centered ADC maps, size-normalized ADC maps demonstrate improved performance in tumors smaller than 1.5 cm, with an accuracy of 76.47%, sensitivity of 90.91%, and specificity of 69.57%, corresponding to a significant improvement of 17.65%, 27.27%, and 13.05% respectively.
Detecting Implicitly Abusive Language by Applying Out-of-Distribution Problem
Jisu Shin, Hoyun Song, Jong C. Park
http://doi.org/10.5626/JOK.2022.49.11.948
Implicitly abusive language detection is a difficult problem to solve due to diversity of expressions and absence of a clear definition. Previous studies have claimed that implicitly abusive language should be classified and defined in detail, accompanied by corresponding datasets. However, this is not only inefficient, but also hard to flexibly respond to language changes. Our work proposes an efficient and effective method that processes implicitly abusive language as Out-of-Distribution data for the first time. In our experiments, a model with the proposed method performed better than a general pre-trained model and lexicon-based models. We also performed sentiment analysis and a case study to analyze characteristics of implicitly abusive language in detail and differences between a general pre-trained model and our model.
Optimizing Homomorphic Compiler HedgeHog for DNN Model based on CKKS Homomorphic Encryption Scheme
Dongkwon Lee, Gyejin Lee, Suchan Kim, Woosung Song, Dohyung Lee, Hoon Kim, Seunghan Jo, Kyuyeon Park, Kwangkeun Yi
http://doi.org/10.5626/JOK.2022.49.9.743
We present a new state-of-the-art optimizing homomorphic compiler HedgeHog based on high-level input language. Although homomorphic encryption enables safe and secure third party computation, it is hard to build high-performance HE applications without expertise. Homomorphic compiler lowers this hurdle, but most of the existing compilers are based on HE scheme that does not support real number computation and a few compilers based on the CKKS HE scheme that supports real number computation uses low-level input language. We present an optimizing compiler HedgeHog whose input language supports high-level DNN operators. In addition to its ease of use, compiled HE code shows a maximum of 22% more of speedup than the existing state-of-the-art compiler.
Automatic Generation of Secure Communication Code in Model-based Software Development Framework
Jaewoo Son, Jangryul Kim, EunJin Jeong, Soonhoi Ha
http://doi.org/10.5626/JOK.2022.49.9.669
With the development of the Internet of Things (IoT), the importance of communication and security is growing, as the connection between embedded platforms becomes common. The model-based software development methodology, one of the methods of developing embedded software, is effective for software development on different platforms, by automatically generating code suitable for the platform from a platform-independent model. This is useful in distributed embedded systems by also generating remote communication code, but there are no studies on automatic secure communication code generation. In this paper, we propose a method for automatically applying security on communication, in a model-based software development framework. The efficiency and validity of the proposed method were verified through the implementation of examples, that require communication between different platforms with various encryption methods.
Network-level Tracker Detection Using Features of Encrypted Traffic
Dongkeun Lee, Minwoo Joo, Wonjun Lee
http://doi.org/10.5626/JOK.2022.49.4.314
Third-party trackers breach users’ data privacy by compiling large amounts of personal data such as location or browsing history through web tracking techniques. Although previous research has proposed several methods to protect the users from web tracking via its detection and blockage, their effectiveness is limited in terms of dependency or performance. To this end, this paper proposes a novel approach to detect trackers at the network level using features of encrypted traffic. The proposed method first builds a classification model based on the features extracted from side-channel information of encrypted traffic generated by trackers. It then prevents leakage of user information by accurately detecting tracker traffic within the network independently from the user’s browsers or devices. We validate the feasibility of utilizing features of encrypted traffic in tracker detection by studying the distinctive characteristics of tracker traffic derived from real-world encrypted traffic analysis.
Implementation and Application of Functional Encryption-Based Matrix Multiplication
http://doi.org/10.5626/JOK.2022.49.3.196
Functional Encryption is an encryption scheme that allows someone who possesses a secret key to obtain only the function value from a ciphertext but not to learn any information about the plaintext. In this paper, we proposed a method to calculate a matrix product based on inner-product functional encryption and accelerated the proposed method by applying precomputation. In addition, we proposed a privacy-preserving application for dimensionality reduction of the vectors by performing secure principal component analysis (PCA) based on the proposed method. According to the experimental results, matrix multiplication based on functional encryption for a 1000-dimensional square matrix and a 1000-dimensional vector was performed in 452.66 seconds and was accelerated by 3.81 times using 4.46 MB of memory when the precomputation was applied, i.e., it was performed in 118.87 seconds.
Automatic Segmentation of Lung Cancer in Chest CT Images through Capsule Network-based Dual-Window Ensemble Learning
Jumin Lee, Julip Jung, Helen Hong, Bong-Seog Kim
http://doi.org/10.5626/JOK.2021.48.8.905
It is difficult to accurately segment lung cancer in chest CT images when it has an irregular shape or nearby structures have a similar intensity as lung cancer. In this study, we proposed a dual window ensemble network that uses a capsule network to learn the relationship between lung cancer and nearby structures and additionally considers the mediastinal window image with the lung window image to distinguish lung cancer from the nearby structures. First, intensity and spacing normalization was performed on the input images of the lung window setting and mediastinal window setting. Second, two types of 2D capsule network were performed with the lung and mediastinal setting images. Third, the final segmentation mask was generated by ensemble the probability maps of the lung and mediastinal window images through average voting by reflecting the weight based on the characteristics of each image. The proposed method showed a Dice similarity coefficient(DSC) of 75.98% which was 0.53% higher than the method not considering the weight of each window setting. Furthermore, segmentation accuracy was improved even when lung cancer was surrounded by nearby structures.
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