12 citations · 17 across the 8 of their papers we have counts for
8 papers
ATTNSOM: Learning Cross-Isoform Attention for Cytochrome P450 Site-of-Metabolism
Hajung Kim, Eunha Lee, Sohyun Chung +3
Identifying metabolic sites where cytochrome P450 enzymes metabolize small-molecule drugs is essential for drug discovery. Although existing computational approaches have been prop…
GraphCliff: Short-Long Range Gating for Modeling Critical Activity Changes Caused by Subtle Molecular Differences
Hajung Kim, Jueon Park, Junseok Choe +3
The quantitative structure-activity relationship assumes a smooth mapping between molecular structure and biological activity. However, activity cliffs, defined as pairs of structu…
HiRef: Leveraging Hierarchical Ontology and Network Refinement for Robust Medication Recommendation
Yan Ting Chok, Soyon Park, Seungheun Baek +3
Medication recommendation is a crucial task for assisting physicians in making timely decisions from longitudinal patient medical records. However, real-world EHR data present sign…
KU AIGEN ICL EDI@BC8 Track 3: Advancing Phenotype Named Entity Recognition and Normalization for Dysmorphology Physical Examination Reports
Hajung Kim, Chanhwi Kim, Jiwoong Sohn +8
The objective of BioCreative8 Track 3 is to extract phenotypic key medical findings embedded within EHR texts and subsequently normalize these findings to their Human Phenotype Ont…
KU-DMIS at EHRSQL 2024:Generating SQL query via question templatization in EHR
Hajung Kim, Chanhwi Kim, Hoonick Lee +5
Transforming natural language questions into SQL queries is crucial for precise data retrieval from electronic health record (EHR) databases. A significant challenge in this proces…
KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization
Gangwoo Kim, Hajung Kim, Lei Ji +7
In this paper, we introduce CheXOFA, a new pre-trained vision-language model (VLM) for the chest X-ray domain. Our model is initially pre-trained on various multimodal datasets wit…
KitchenScale: Learning to predict ingredient quantities from recipe contexts
Donghee Choi, Mogan Gim, Samy Badreddine +3
Determining proper quantities for ingredients is an essential part of cooking practice from the perspective of enriching tastiness and promoting healthiness. We introduce KitchenSc…
RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer
Mogan Gim, Donghee Choi, Kana Maruyama +4
We propose a computational approach for recipe ideation, a downstream task that helps users select and gather ingredients for creating dishes. To perform this task, we developed Re…