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1
Chromatin structure-based prediction of recurrent noncoding mutations in cancer

Kim, Kwoneel; Jang, Kiwon; Yang, Woojin; Choi, Eun-Young; Park, Seong-Min; Bae, Mingyun; Kim, Youn-Jae; et al, NATURE GENETICS, v.48, no.11, pp.1321 - 1326, 2016-11

2
Convolutional neural network model to predict causal risk factors that share complex regulatory features

Lee, Taeyeop; Sung, Min Kyung; Lee, Seulkee; Yang, Woojin; Oh, Jaeho; Kim, Jeong Yeon; Hwang, Seongwon; et al, NUCLEIC ACIDS RESEARCH, v.47, no.22, pp.e146 - e146, 2019-12

3
Global transcription network incorporating distal regulator binding reveals selective cooperation of cancer drivers and risk genes

Kim, Kwoneel; Yang, Woojin; Lee, Kang Seon; Bang, Hyoeun; Jang, Kiwon; Kim, Sang Cheol; Yang, Jin Ok; et al, NUCLEIC ACIDS RESEARCH, v.43, no.12, pp.5716 - 5729, 2015-07

4
Machine learning for the identification of noncoding driver mutations in cancer = 암 세포에서 발생하는 돌연변이의 기능을 확인하기 위한 머신러닝 알고리즘 연구link

Yang, Woojin; 양우진; et al, 한국과학기술원, 2017

5
Predicting the recurrence of noncoding regulatory mutations in cancer

Yang, Woojin; Bang, Hyoeun; Jang, Kiwon; Sung, Min Kyung; Choi, JungKyoon, BMC BIOINFORMATICS, v.17, 2016-12

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