The Block Hidden Markov Model for Biological Sequence Analysis. - Kyoung-Jae Won; Adam Prügel-Bennett; Anders Krogh (KES, 2004)

Training HMM structure with genetic algorithm for biological sequence analysis. - Kyoung-Jae Won; Adam Prügel-Bennett; Anders Krogh (Bioinform., 2004)

Evolving hidden Markov models for protein secondary structure prediction. - Kyoung-Jae Won; Thomas Hamelryck; Adam Prügel-Bennett; Anders Krogh (Congress on Evolutionary Computation, 2005)

Evolving the structure of hidden Markov models. - Kyoung-Jae Won; Adam Prügel-Bennett; Anders Krogh (IEEE Trans. Evol. Comput., 2006)

An evolutionary method for learning HMM structure: prediction of protein secondary structure. - Kyoung-Jae Won; Thomas Hamelryck; Adam Prügel-Bennett; Anders Krogh (BMC Bioinform., 2007)

Modeling promoter grammars with evolving hidden Markov models. - Kyoung-Jae Won; Albin Sandelin; Troels Torben Marstrand; Anders Krogh (Bioinform., 2008)

Prediction of regulatory elements in mammalian genomes using chromatin signatures. - Kyoung-Jae Won; Iouri Chepelev; Bing Ren; Wei Wang 051 (BMC Bioinform., 2008)

Evolving Fisher Kernels for Biological Sequence Classification. - Kyoung-Jae Won; Craig Saunders; Adam Prügel-Bennett (Evol. Comput., 2013)

Gaussian derivative wavelets identify dynamic changes in histone modification. - Nha Nguyen; Kyoung-Jae Won (CIBCB, 2014)

A wavelet-based method to exploit epigenomic language in the regulatory region. - Nha Nguyen; An P. N. Vo; Kyoung-Jae Won (Bioinform., 2014)

A Wavelet Approach to Detect Enriched Regions and Explore Epigenomic Landscapes. - Nha Nguyen; An P. N. Vo; Kyoung-Jae Won (J. Comput. Biol., 2014)

A Stationary Wavelet Entropy-Based Clustering Approach Accurately Predicts Gene Expression. - Nha Nguyen; An P. N. Vo; Inchan Choi; Kyoung-Jae Won (J. Comput. Biol., 2015)

Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus. - David E. Condon; Phu V. Tran; Yu-Chin Lien; Jonathan Schug; Michael K. Georgieff; Rebecca A. Simmons; Kyoung-Jae Won (BMC Bioinform., 2018)

Processing Millions of Single Cells by SHARP. - Shibiao Wan; Junil Kim; Yiping Fan; Kyoung-Jae Won (BCB, 2020)

VeTra: a tool for trajectory inference based on RNA velocity. - Guangzheng Weng; Junil Kim; Kyoung-Jae Won (Bioinform., 2021)

Survey on unsupervised learning methods for optical flow estimation. - Tomislav Dobricki; Xiahai Zhuang; Kyoung-Jae Won; Byung-Woo Hong (ICTC, 2022)

Regularization in Network Optimization via Trimmed Stochastic Gradient Descent With Noisy Label. - Kensuke Nakamura 001; Bong-Soo Sohn; Kyoung-Jae Won; Byung-Woo Hong (IEEE Access, 2022)

ChatGPT and large language models in academia: opportunities and challenges. - Jesse G. Meyer; Ryan J. Urbanowicz; Patrick C. N. Martin; Karen O'Connor; Ruowang Li; Pei-Chen Peng; Tiffani J. Bright; Nicholas P. Tatonetti; Kyoung-Jae Won; Graciela Gonzalez-Hernandez; Jason H. Moore (BioData Min., 2023)

SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution. - Linbu Liao; Esha Madan; António M. Palma; Hyobin Kim; Amit Kumar; Praveen Bhoopathi; Robert Winn; Jose Trevino; Paul Fisher; Cord Herbert Brakebusch; Gahyun Kim; Junil Kim; et al. (BMC Bioinform., 2025)