Wonseok Shin | Publications

2023
1. iScience
  
  Supervised chemical graph mining improves drug-induced liver injury prediction
  
  Sangsoo Lim, Youngkuk Kim, Jeonghyeon Gu, Sunho Lee, Wonseok Shin, and Sun Kim
  
  iScience 2023
  
  Abs HTML
  
  Drug-induced liver injury (DILI) is the main cause of drug failure in clinical trials. The characterization of toxic compounds in terms of chemical structure is important because compounds can be metabolized to toxic substances in the liver. Traditional machine learning approaches have had limited success in predicting DILI, and emerging deep graph neural network (GNN) models are yet powerful enough to predict DILI. In this study, we developed a completely different approach, supervised subgraph mining (SSM), a strategy to mine explicit subgraph features by iteratively updating individual graph transitions to maximize DILI fidelity. Our method outperformed previous methods including state-of-the-art GNN tools in classifying DILI on two different datasets: DILIst and TDC-benchmark. We also combined the subgraph features by using SMARTS-based frequent structural pattern matching and associated them with drugs’ ATC code.
2022
1. Journal of KIISE
  
  부분 그래프 매칭 문제를 위한 새로운 동적 매칭 순서와 성능 비교
  
  Seunghwan Min, Wonseok Shin, Chaewon Kim, and Kunsoo Park
  
  정보과학회논문지 2022
2024
1. VLDB 2024
  
  Cardinality Estimation of Subgraph Matching: A Filtering-Sampling Approach
  
  Wonseok Shin, Siwoo Song, Kunsoo Park, and Wook-Shin Han
  
  Proceedings of the VLDB Endowment 2024
  
  Abs arXiv Blog
  
  Subgraph counting is a fundamental problem in understanding and analyzing graph structured data, yet computationally challenging. This calls for an accurate and efficient algorithm for Subgraph Cardinality Estimation, which is to estimate the number of all isomorphic embeddings of a query graph in a data graph. We present FaSTest, a novel algorithm that combines (1) a powerful filtering technique to significantly reduce the sample space, (2) an adaptive tree sampling algorithm for accurate and efficient estimation, and (3) a worst-case optimal stratified graph sampling algorithm for difficult instances. Extensive experiments on real-world datasets show that FaSTest outperforms state-of-the-art sampling-based methods by up to two orders of magnitude and GNN-based methods by up to three orders of magnitude in terms of accuracy.
2021
1. KCC 2021
  
  부분 그래프 매칭 문제를 위한 향상된 동적 매칭 순서
  
  Seunghwan Min, Wonseok Shin, Chaewon Kim, and Kunsoo Park
  
  한국정보과학회 학술발표논문집 2021
2022
1. Preprint
  
  Triangular Contrastive Learning on Molecular Graphs
  
  MinGyu Choi, Wonseok Shin, Yijingxiu Lu, and Sun Kim
  
  Preprint. 2022
  
  Abs arXiv Blog
  
  Recent contrastive learning methods have shown to be effective in various tasks, learning generalizable representations invariant to data augmentation thereby leading to state of the art performances. Regarding the multifaceted nature of large unlabeled data used in self-supervised learning while majority of real-word downstream tasks use single format of data, a multimodal framework that can train single modality to learn diverse perspectives from other modalities is an important challenge. In this paper, we propose TriCL (Triangular Contrastive Learning), a universal framework for trimodal contrastive learning. TriCL takes advantage of Triangular Area Loss, a novel intermodal contrastive loss that learns the angular geometry of the embedding space through simultaneously contrasting the area of positive and negative triplets. Systematic observation on embedding space in terms of alignment and uniformity showed that Triangular Area Loss can address the line-collapsing problem by discriminating modalities by angle. Our experimental results also demonstrate the outperformance of TriCL on downstream task of molecular property prediction which implies that the advantages of the embedding space indeed benefits the performance on downstream tasks.

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2022