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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.
Drug Toxicity Prediction Using Integrated Graph Neural Networks and Attention-Based Random Walk Algorithm
Jong-Hoon Park, Jae-Woo Chu, Young-Rae Cho
http://doi.org/10.5626/JOK.2025.52.3.234
The traditional drug development process is often burdened by high costs and lengthy timelines, leading to increasing interest in AI-based drug development. In particular, the importance of AI models for preemptively evaluating drug toxicity is being emphasized. In this study, we propose a novel drug toxicity prediction model, named Integrated GNNs and Attention Randon Walk (IG-ARW). The proposed method integrates various Graph Neural Network (GNN) models and uses attention mechanisms to compute random walk transition probabilities, extracting graph features precisely. The model then conducts random walks to extract node features and graph features, ultimately predicting drug toxicity. IG-ARW was evaluated on three different datasets, demonstrating strong performances with AUC scores of 0.8315, 0.8894, and 0.7476, respectively. Notably, the model was proven to be highly effective not only in toxicity prediction, but also in predicting other drug characteristics.
Task-Oriented Dialogue System Using a Fusion Module between Knowledge Graphs
Jinyoung Kim, Hyunmook Cha, Youngjoong Ko
http://doi.org/10.5626/JOK.2024.51.10.882
The field of Task-Oriented Dialogue Systems focuses on using natural language processing to assist users in achieving specific tasks through conversation. Recently, transformer-based pre-trained language models have been employed to enhance performances of task-oriented dialogue systems. This paper proposes a response generation model based on Graph Attention Networks (GAT) to integrate external knowledge data into transformer-based language models for more specialized responses in dialogue systems. Additionally, we extend this research to incorporate information from multiple graphs, leveraging information from more than two graphs. We also collected and refined dialogue data based on music domain knowledge base to evaluate the proposed model. The collected dialogue dataset consisted of 2,076 dialogues and 226,823 triples. In experiments, the proposed model showed a performance improvement of 13.83%p in ROUGE-1, 8.26%p in ROUGE-2, and 13.5%p in ROUGE-L compared to the baseline KoBART model on the proposed dialogue dataset.
BERT-based Two-Stage Classification Models and Co-Attention Mechanism for Diagnosing Dementia and Schizophrenia-related Disease
Min-Kyo Jung, Seung-Hoon Na, Ko Woon Kim, Byoung-Soo Shin, Young-Chul Chung
http://doi.org/10.5626/JOK.2022.49.12.1071
Noting the recently increasing number of patients, we present deep learning methods for automatically diagnosing dementia and schizophrenia by exploring the use of the novel two-stage classification and the co-attention mechanism. First, the two-stage classification consists of two steps-the perplexity-based classification and the standard BERT-based classification. 1) the perplexity-based classification first prepares two types of BERTs, i.e., control-specific and patients-specific BERTs, pretrained from transcripts for controls and patients as the additional pretraining datasets, respectively, and then performs a simple threshold-based classification based on the difference between perplexity values of two BERTs for an input test transcript; then, for ambiguous cases where the perplexity difference only does not provide sufficient evidence for the classification, the standard BERT-based classification is performed based on a fine-tuned BERT. Second, the co-attention mechanism enriches the BERT-based representations from a doctor’s transcript and a client’s one by applying the cross-attention over them using the shared affinity matrix, and performs the classification based on the enriched co-attentive representations. Experiment results on a large-scale dataset of Korean transcripts show that the proposed two-stage classification outperforms the baseline BERT model on 4 out of 7 subtasks and the use of the co-attention mechanism achieves the best F1 score for 4 out of 8 subtasks.
OANet: Ortho-Attention Net Based on Attention Mechanism for Database Performance Prediction
Chanho Yeom, Jieun Lee, Sanghyun Park
http://doi.org/10.5626/JOK.2022.49.11.1026
Various parameters in a database can be modified, which are called knobs. Since the performance of the database varies according to the settings of the knobs, it is important to tune the knobs of the database. And when tuning, a model that can reliably and quickly predict database performance according to the knob setting is needed. However, even when the knob setting is the same, the results may be different if the workload performing the benchmark is different. Therefore, in this paper, we propose an OANet using the attention mechanism so that the relationship between the knob and the workload can also be considered. Through experiments, the performance prediction results of the database were compared to various machine learning techniques, and the superiority of the model was confirmed by showing the highest score.
A Cross Domain Adaptation Method based on Adversarial Cycle Consistence Learning for Rotary Machine Fault Diagnosis
http://doi.org/10.5626/JOK.2022.49.7.530
Research on data-based fault diagnosis models is being actively conducted in various industries. However, in the case of industrial equipment, various operating conditions occur, and it is difficult to secure sufficient training data. To solve this problem, a cross-domain adaptation technique can be utilized. In this study, we propose an adversarial consistency-maintaining transformation learning method that can maintain failure classification consistency even for the new untrained environmental data using the rotating body vibration data. The data generated through consistent learning creates a continuous invariant latent space between the new operating condition data distribution and the known data distribution and learns to maintain the failure classification performance through an adversarial learning network that shares the failure classification characteristic information. Therefore, the proposed method can provide a more stable and general classification performance by expanding the potential space to minimize the discrepancy between domain data. The experimental results of the proposed model showed about 88% accuracy for a real-machine dataset, and compared to the existing cross-domain adaptive learning methods, it showed a performance improvement of about 5-10%. According to the results of this study, it is expected to be an effective solution for the problem of equipment failure diagnosis at actual industrial sites.
Graph Embedding-Based Point-Of-Interest Recommendation Considering Weather Features
Kun Woo Lee, Jongseon Kim, Yon Dohn Chung
http://doi.org/10.5626/JOK.2022.49.3.221
As the Location-Based Services (LBS) grow rapidly, the Point-Of-Interest (POI) recommendation becomes an active research area to provide users appropriate information relevant to their locations. Recently, translation-based recommendation systems using graph embedding, such as TransRec, are attracting great attention. In this paper, we discovered some drawbacks of TransRec; it is limited in expressing the complex relationship between users and POIs, and the relation embedding is fixed without considering weather features. We propose WAPTRec, a graph embedding-based POI recommendation method considering the weather, that overcomes the drawback of TransRec. WAPTRec can rep resent the same POI embedding in different ways according to users by using a category projection matrix and attention mechanism. In addition, it provides better recommendation accuracy by utilizing the users’ movement history, category of POIs and weather features. Experiments using public datasets illustrated that WAPTRec outperformed the conventional translation-based recommendation methods.
Analyzing the Impact of Sequential Context Learning on the Transformer Based Korean Text Summarization Model
Subin Kim, Yongjun Kim, Junseong Bang
http://doi.org/10.5626/JOK.2021.48.10.1097
Text summarization reduces the sequence length while maintaining the meaning of the entire article body, solving the problem of overloading information and helping readers consume information quickly. To this end, research on a Transformer-based English text summarization model has been actively conducted. Recently, an abstract text summary model reflecting the characteristics of English with a fixed word order by adding a Recurrent Neural Network (RNN)-based encoder was proposed. In this paper, we study the effect of sequential context learning on the text abstract summary model by using an RNN-based encoder for Korean, which has more free word order than English. Transformer-based model and a model that added RNN-based encoder to existing Transformer model are trained to compare the performance of headline generation and article body summary for the Korean articles collected directly. Experiments show that the model performs better when the RNN-based encoder is added, and that sequential contextual information learning is required for Korean abstractive text summarization.
Denoising Multivariate Time Series Modeling for Multi-step Time Series Prediction
Jungsoo Hong, Jinuk Park, Jieun Lee, Kyeonghun Kim, Seung-Kyun Hong, Sanghyun Park
http://doi.org/10.5626/JOK.2021.48.8.892
The research field of time series forecasting predicts the future time point using seasonality in time series. In the industrial environment, since decision-making through continuous perspective prediction of the future is important, multi-step time series forecasting is necessary. However, multi-step prediction is highly unstable because of its dependency on predicted value of previous time prediction result. Therefore, the traditional time series forecasting makes a statistical prediction for the single time point. To address this limitation, we propose a novel encoder-decoder based neural network named ‘DTSNet’ which predicts multi-step time points for multivariate time series. To stabilize multi-step prediction, we exploit positional encoding to enhance representation for time point and propose a novel denoising training method. Moreover, we propose dual attention to resolve long-term dependencies and modeling complex patterns in time series, and we adopt multi-head strategy at linear projection layer for variable-specific modeling. To verify the performance improvement of our approach, we compare and analyze it with baseline models, and we demonstrate the proposed methods through comparison tests, such as, component ablation study and denoising degree experiment.
EFA-DTI: Prediction of Drug-Target Interactions Using Edge Feature Attention
Erkhembayar Jadamba, Sooheon Kim, Hyeonsu Lee, Hwajong Kim
http://doi.org/10.5626/JOK.2021.48.7.825
Drug discovery is a high-level field of research requiring the coordination of disciplines ranging from medicinal chemistry, systems biology, structural biology, and increasingly, artificial intelligence. In particular, drug-target interaction (DTI) prediction is central to the process of screening for and optimizing candidate substances to treat disease from a nearly infinite set of compounds. Recently, as computer performance has developed dramatically, studies using artificial intelligence neural networks have been actively conducted to reduce the cost and increase the efficiency of DTI prediction. This paper proposes a model that predicts an interaction value between a given molecule and protein using a learned molecule representation via Edge Feature Attention-applied Graph Net Embedding with Fixed Fingerprints and a protein representation using pre-trained protein embeddings. The paper describes architectures, experimental methods, and findings. The model demonstrated higher performance than DeepDTA and GraphDTA, which had previously demonstrated the best performance in DTI studies.
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