MOMA: a multi-task attention learning algorithm for multi-omics data interpretation and classification. - Sehwan Moon; Hyunju Lee (Bioinform., 2022)

SDGCCA: Supervised Deep Generalized Canonical Correlation Analysis for Multi-Omics Integration. - Sehwan Moon; Jeongyoung Hwang; Hyunju Lee (J. Comput. Biol., 2022)

A Comparative Study of AI Models for Depression Assessment using Voice Features. - Eunkyoung Jeon; Sehwan Moon; Seihyoung Lee; Aram Lee; Sanghyun Kim; Kiwon Park; Jeong Eun Kim (BIBM, 2023)

Challenge in Classification of Depressive Symptoms Using Actigraphy Data. - Sehwan Moon; Aram Lee; Ju-Wan Kim; Eunkyoung Jeon; Min Jhon; Jeong Eun Kim (BIBM, 2023)

Comparative Study on the Performance of LLM-based Psychological Counseling Chatbots via Prompt Engineering Techniques. - Aram Lee; Sehwan Moon; Min Jhon; Ju-Wan Kim; Dae-Kwang Kim; Jeong Eun Kim; Kiwon Park; Eunkyoung Jeon (BIBM, 2024)

Evaluating the Effectiveness of Multi-Modal Data in Classifying Depressive Symptoms: Insights from Actigraphy, HRV, and Demographic Data. - Sehwan Moon; Eunkyoung Jeon; Aram Lee; Min Jhon; Ju-Wan Kim; Jeong Eun Kim (BIBM, 2024)

Deep metric loss for multimodal learning. - Sehwan Moon; Hyunju Lee (Mach. Learn., 2025)