PRISM: Methylation Pattern-based, Reference-free Inference of Subclonal Makeup (ISMB 2019, Bioinformatics)
mirTime: Identifying Condition-Specific Targets of MicroRNA in Time-series Transcript Data using Gaussian Process Model and Spherical Vector Clustering (Bioinformatics 2019)
GABA-stimulated adipose-derived stem cells suppress subcutaneous adipose inflammation in obesity (PNAS, 2019)
DeepFam: Deep learning based alignment-free method for protein family modeling and prediction (ISMB 2018, Bioinformatics)
Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification (IJCAI 2017)
과제명: 멀티오믹스 분석 알고리즘 및 플랫폼 개발
연구비 지원기관: 한국연구재단 (과학기술정보통신부)
연구수행 기간: 2014.11.01 - 2022.10.31 (96개월)
과제명: 실험 및 문헌정보 거대 복잡형 데이터 통합분석추론 연구
연구비 지원기관: 한국연구재단 (과학기술정보통신부)
연구기간: 2017.9.1 - 2020.12.31 (40개월)

Multi-channel PINN: investigating scalable and transferable neural networks for drug discovery. Journal of Chemical Informatics 11.1 (2019): 46
Cognitive Profiling Related to Cerebral Amyloid Beta Burden Using Machine Learning Approaches. Frontiers in Aging Neuroscience. 10.3389 (2019)
The Disturbance in Dynamic Property in the Reconstructed State Space during Nitrous Oxide Administration. Neuroreport 30.3(2019):162–68
Differential Diagnosis of Jaw Pain using Informatics Technology. Journal of Oral Rehabilitation (2018)
A dynamic and parallel approach for repetitive prime labeling of XML with MapReduce, The Journal of Supercomputing, 73:2 (2017)

Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization. Sangwon Jung, Taeeon Park, Sanghyuk Chun, and Taesup Moon. The 11th International Conference on Learning Representations (ICLR), May 2023

Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks. Hongjoon Ahn, Youngyi Yang, Quan Gan, David Wipf, and Taesup Moon. Neural Information Processing Systems (NeurIPS), December 2022

GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-training. Jaeseok Byun, Taebaek Hwang, Jianlong Fu, and Taesup Moon. European Conference on Computer Vision (ECCV), October 2022

Learning Fair Classifiers with Partially Annotated Group Labels. Sangwon Jung, Sanghyuk Chun, and Taesup Moon. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022

SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning. Sungmin Cha, Beomyoung Kim, Youngjoon Yoo, and Taesup Moon. Neural Information Processing Systems (NeurIPS), December 2021

SS-IL: Separated Softmax for Incremental Learning. Hongjoon Ahn, Jihwan Kwak, Subin Lim, Hyeonsu Bang, Hyojun Kim, and Taesup Moon. International Conference on Computer Vision (ICCV), October 2021

Fair Feature Distillation for Visual Recognition. Sangwon Jung, Donggyu Lee, Taeeon Park, and Taesup Moon. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021

FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise. Jaeseok Byun, Sungmin Cha, and Taesup Moon. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021

Continual Learning with Node-Importance based Adaptive Group Sparse Regularization. Sangwon Jung, Hongjoon Ahn, Sungmin Cha, and Taesup Moon. Neural Information Processing Systems (NeurIPS), December 2020

Uncertainty-based continual learning with adaptive regularization. Hongjoon Ahn, Sungmin Cha, Donggyu Lee and Taesup Moon. Proceedings of Neural Information Processing Systems (NeurIPS), December 2019

Fooling neural network interpretations via adversarial model manipulation. Juyeon Heo, Sunghwan Joo, and Taesup Moon. Proceedings of Neural Information Processing Systems (NeurIPS), December 2019

D. Kim*, Y. M. Sung*, J. Park*, S. Kim, J. Kim, J. Park, H. Ha, J. Y. Bae, S. Kim, and D. Baek, General Rules for Functional MicroRNA Targeting, Nature Genetics, 2016
D. Garcia*, D. Baek*#, C. Shin, G. Bell, A. Grimson, and D. Bartel#, Weak Seed-Pairing Stability and High Target-Site Abundance Decrease the Proficiency of lsy-6 and Other miRNAs, Nature Structural and Molecular Biology, 2011.
D. Baek*, J. Villen*, C. Shin*, F. Camargo, S. Gygi, and D. Bartel, The Impact of MicroRNAs on Protein Output, Nature, 2008.
D. Baek#, C. Davis, B. Ewing, D. Gordon, and P. Green#, Characterization and Predictive Discovery of Evolutionarily Conserved Mammalian Alternative Promoters, Genome Research, 2007.
D. Baek# and P. Green#, Sequence Conservation, Relative Isoform Frequencies, and Nonsense-Mediated Decay in Evolutionarily Conserved Alternative Splicing, Proceedings of the National Academy of Sciences U.S.A., 2005.
고정밀 암 진단을 위한 멀티오믹스 기반 유전체변이 발굴 및 진단용 소프트웨어 플랫폼 개발
당뇨합병증 정밀의료를 위한 엑소좀 다중오믹스 분석 플랫폼 개발

Park HR, Kim IH, Kang H, McCairn KW, Lee DS, Kim BN, Kim DG, Paek SH.Electrophysiological and imaging evidence of sustained inhibition in limbic and frontal networks following deep brain stimulation for treatment refractory obsessive compulsive disorder.PLoS One. 2019 Jul 19;14(7):e0219578. doi: 10.1371/journal.pone.0219578. eCollection 2019
Yi HG, Jeong YH, Kim Y, Choi YJ, Moon YE, Park SH, Kang KS, Jang J, Youn H, Paek SH. Cho DW. A bioprinted human-glioblastoma-on-a-chip for the identification of patient-specific responses to chemoradiotherapy. Nat Biomed Eng. 2019 Mar 18. doi: 10.1038/s41551-019-0363-x.
Choi SH, Kim YB, Paek SH, Cho ZH.Papez Circuit Observed by in vivo Human Brain With 7.0T MRI Super-Resolution Track Density Imaging and Track Tracing.Front Neuroanat. 2019 Feb 18;13:17. doi: 10.3389/fnana.2019.00017. eCollection 2019.
Choi H, Ha S, Im HJ, Paek SH, Lee DS. Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging. Neuroimage Clin. 2017 Sep 10;16:586-594. doi: 10.1016/j.nicl.2017.09.010. eCollection 2017. PMID:28971009
Jung YJ, Kim HJ, Jeon BS, Park H, Lee WW, Paek SH.An 8-Year Follow-up on the Effect of Subthalamic Nucleus Deep Brain Stimulation on Pain in Parkinson Disease.JAMA Neurol. 2015 Mar 23. doi: 10.1001/jamaneurol.2015.8.
미래뇌융합기술개발사업 위탁과제
(과제명: 영장류 이식 환경 구축 및 개발된 시스템의 이식후 성능평가, 연구비 지원기관: 과학기술정보통신부, 연구수행기간: 2020년 1월 1일-2022년 12월 31일)

Mi Jeong Kwon, Sae Byul Lee, Jinil Han, Jeong Eon Lee, Jong Won Lee, Gyungyub Gong, Peter D. Beitsch, Seok Jin Nam, Sei Hyun Ahn, Byung-Ho Nam, Young Kee Shin. BCT score predicts chemotherapy benefit in Asian patients with mone receptor-positive, HER2-negative, lymph node-negative breast cancer. PLoS One. 2018 Nov 21;13(11):e0207155. doi: 10.1371/journal.pone.0207155. eCollection 2018
Han J, Choi YL, Kim H, Choi JY, Lee SK, Lee JE, Choi JS, Park S, Choi JS, Kim YD, Nam SJ, Nam BH, Kwon MJ, Shin YK. MMP11 and CD2 as Novel Prognostic Factors in Hormone Receptor-Negative, HER2-Positive Breast Cancer. Breast Cancer Res Treat. 2017 Apr 13. doi: 10.1007/s10549-017-4234-4. [Epub ahead of print]
Gong G, Kwon MJ, Han J, Lee HJ, Lee SK, Lee JE, Lee SH, Park S, Choi JS, Cho SY, Ahn SH, Lee JW, Cho SR, Moon Y, Nam BH, Nam SJ, Choi YL, Shin YK. A new molecular prognostic score for predicting the risk of distant metastasis in patients with HR+/HER2- early breast cancer. Scientific Reports. 2017 Mar 28;7:45554. doi: 10.1038/srep45554.
Oh E, Choi Y-L, Park T, Lee S, Nam SJ, Shin YK. A prognostic model for lymph node-negative breast cancer patients based on the integration of proliferation and immunity. Breast Cancer Res Treat. 2012 Apr;132(2):499-509. doi: 10.1007/s10549-011-1626-8

Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation
Deeply Self-Supervised Contour Embedded Neural Network Applied to Liver Segmentation
AI기반 의료영상 데이터 Labeling 기술연구, (주)인피니트헬스케어, 2020-04-01~2021-03-31
인공지능기반 파노라마, CT영상 병변 인식 기술개발, (주)오스템임플란트, 2019-12-16~2021-02-15
인공지능 기반 CT 영상 분할 기술 개발, (주)오스템임플란트, 2019-02-01~2019-10-31

기계학습 기반의 음성 분석으로 자살 위험 예측

Ahn, W.-Y., Kishida, K. T., Gu, X., Lohrenz, T., Harvey, A. H., Alford, J. R., Smith, K. B., Yaffe, G., Hibbing, J. R., Dayan, P., & Montague, P. R. (2014) Nonpolitical images evoke neural predictors of political ideology. Current Biology, 24, 1-7.
Ahn, W.-Y., Haines, N., & Zhang, L. (2017) Revealing neuro-computational mechanisms of reinforcement learning and decision-making with the hBayesDM package. Computational Psychiatry, 1:1.
Ahn, W.-Y. & Vassileva, J. (2016) Machine learning identifies substance-specific behavioral markers for heroin and amphetamine dependence. Drug and Alcohol Dependence, 161, 247-257.
Aylward, J., Valton, V., Ahn, W. Y., Bond, R. L., Dayan, P., Roiser, J. P., & Robinson, O. J. (2019). Altered learning under uncertainty in unmedicated mood and anxiety disorders. Nature Human Behaviour, 3(10), 1116-1123.
Yang, J., Pitt, M. A., Ahn, W. Y., & Myung, J. I. (in press) ADOpy: A Python Package for Adaptive Design Optimization. Behavior Research Methods.
  기계학습을 이용한 빠르고 높은 신뢰도의 멀티모달 금연 표지 개발, 한국연구재단 (연구책임자), 2017-2022년
작업기억-의사결정 상호작용의 뇌신경망에 관한 통합적 연구, 한국연구재단 (공동연구자), 2018-2020년
뇌영상기법과 기계학습을 이용한 비정상적 의사결정의 신경계산학적 기전 연구, 서울대학교 창의선도 신진 연구자 지원사업 (연구책임자), 2019-2022년

Kwon, D., & Oh, S. (2019). The number of letters in number words influences the response time in numerical comparison tasks: Evidence using Korean number words. Attention, Perception, & Psychophysics, 81(8), 2612-2618.
Ryu, D., & Oh, S. (2018). The effect of good continuation on the contact order judgment of causal events. Journal of Vision, 18(11), 5-5.
Lee, H., & Oh, S. (2016). How directional change in reading/writing habits relates to directional change in displayed pictures. Laterality: Asymmetries of Body, Brain and Cognition, 21(1), 1-11.
Oh, S. (2013). Eyes can switch finger stroke. Perception, 42(6), 681–684.
Oh S. (2011). The eyeglass reversal. Attention, Perception & Psychophysics. 73, 1336-1343.
Baby mind: 아기 모사형 인공지능 개발
서울대학교 얼굴 데이터베이스 구축
미술감상에서 감상자 자세

Genet Med. 2019 Dec;21(12):2695-2705. doi: 10.1038/s41436-019-0588-9.
Oncogenic effects of germline variants in lysosomal storage disease genes.
Sci Rep. 2019 Mar 5;9(1):3465. doi: 10.1038/s41598-019-39706-0.
Interpretation of EBV infection in pan-cancer genome considering viral life cycle: LiEB (Life cycle of Epstein-Barr virus).
Blood Cancer J. 2018 May 23;8(5):43. doi: 10.1038/s41408-018-0083-6.
RTK-RAS pathway mutation is enriched in myeloid sarcoma.

인공지능기반 최적파이토슈티컬 도출시스템 및 응용연구실, 기초연구실사업, 과기정통부
아동청소년 비만 예방관리를 위한 BT-IT 융합기반 통합 플랫폼 기술개발, 과기정통부
방송통신 융합기술을 활용한 보육기관 맞춤형 스마트 웰니스 서비스 개발, 과기정통부

Generation of PET attenuation map for whole-body time-of-flight 18F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps. J Nucl Med. 2019 Aug;60(8):1183-1189.
Improving the accuracy of simultaneously reconstructed activity and attenuation maps using deep learning. J Nucl Med. 2018 Oct;59(10):1624-1629.
Deep-dose: a voxel dose estimation method using deep convolutional neural network for personalized internal dosimetry. Sci Rep. 2019; 9:10308.
Measurement of glomerular filtration rate using quantitative SPECT/CT and deep-learning-based kidney segmentation. Sci Rep. 2019;9:4223.
r amyloid PET using a deep learning approach. Hum Brain Mapp. 2018 May 11;39(9):3769–3778.
뇌질환 임상연구를 위한 7T MR-Compatible PET System 개발, 과학기술
정보통신부, 2014.07 - 2019.04
차세대 초저선량 PET 시스템 핵심 기술 개발, 과학기술 정보통신부, 2016.06 - 2021.03

Jaeyong Lee and Steven N. MacEachern. (2020). A New Proof of the Stick-Breaking Construction of Dirichlet Processes. JKSS.
Kyoungjae Lee, Jaeyong Lee and Lizhen Lin. (2019.12) Minimax Posterior Convergence Rates and Model Selection Consistency in High-dimensional DAG Models based on Sparse Cholesky Factors. Annals of Statistics, 47(6), 3413-3437.
Kyoungjae Lee and Jaeyong Lee.(2018).  Optimal Bayesian Minimax Rates for Unconstrained Large Covariance Matrices. Bayesian Analysis, 13(4), 1215-1233.
Seongil Jo, Jaeyong Lee, Peter Muller, Fernando A. Quintana & Lorenzo Trippa. (2017). Dependent Species Sampling Models for Spatial Density Estimation. Bayesian Analysis, 12(2), 379-406.
Sarat C. Dass, Jaeyong Lee, Kyoungjae Lee & Jonghun Park. (2017). Laplace based approximate posterior inference for differential equation models. Statistics and Computing, 27(3), 679-698.
인공지능과 빅데이터 분석을 위한 베이즈 추론의 수학적 기반 이론 연구. 과학기술정보통신부. 2018.09.01-2023.08.31.
신뢰도 검사시 불량발생 리스크, 추가 샘플링 확보에 따른 리스크 감소 대책 등에 대한 통계적 확률적 연구. 삼성전자. 2018.09.01-2023.08.31.
카드 거래 자료를 이용한 카드 고객 거래 패턴 분석. 코나아이(주). 2019.01.01-2019.05.31.

Development of a Smartphone Application for Dietary Self-Monitoring. Front Nutr. 2019;6:149
Association of depression and anxiety disorder with the risk of mortality in breast cancer: A National Health Insurance Service study in Korea. Breast Cancer Res Treat. 2020;179(2):491-498
Use of a Mobile Application for Self-Monitoring Dietary Intake: Feasibility Test and an Intervention Study. Nutrients. 2017;9(7):pii: E748
Red meat intake, CYP2E1 and PPARγ polymorphisms, and colorectal cancer risk. Eur J Cancer Prev. 2019;28(4):304-310

Shin D, Ji S, Lee D, Lee J, Oh SH, Lee J*,
Deep Reinforcement Learning Designed RF Pulse: DeepRF_SLR
arXiv. 2019 Dec;1912.09015.
Jung W*, Steffen Bollmann*, Lee J*
[REVIEW] Overview of quantitative susceptibility mapping using deep learning – Current status, challenges and opportunities
arXiv. 2019 Dec;1912.05410.
Jung W, Yoon J, Ji S, Choi JY, Kim JM, Nam Y, Kim EY, Lee J*
Exploring linearity of deep neural network trained QSM: QSMnet+
Neuroimage 2020 in press
Lee J, Lee D, Choi JY, Shin D, Shin HG, Lee J*
Artificial neural network for myelin water imaging
Magn Res Med 2020 83(5):1875-1883
Yoon J, Gong E, Chatnuntawech I, Bilgic B, Lee J, Jung W, Ko J, Jung H, Setsompop K, Zaharchuk G, Kim EY, Pauly J, Lee J*
Quantitative susceptibility mapping using deep neural network: QSMnet
NeuroImage 2018 179:199-206

Artificial intelligence utilized cholesterol profile calculation
Common data modeling

Answerer in questioner's mind: Information theoretic approach to goal-oriented visual dialog, S.-W. Lee, Y.-J. Heo, B.-T. Zhang, The 32nd Annual Conference on Neural Information Processing Systems (NIPS 2018), (Spotlight)
Bilinear attention networks, J.-H. Kim, J. Jun, B.-T. Zhang, The 32nd Annual Conference on Neural Information Processing Systems (NIPS 2018)
Overcoming catastrophic forgetting by incremental moment matching, S.-W. Lee, J.-H. Kim, J. Jun, J.-W. Ha, and B.-T. Zhang, The 31st Annual Conference on Neural Information Processing Systems (NIPS 2017)
Multimodal residual learning for visual QA, J.-H. Kim, S.-W. Lee, D.-H. Kwak, M.-O. Heo, J. Kim, J.-W. Ha, and B.-T. Zhang, In The 30th Annual Conference on Neural Information Processing Systems (NIPS 2016)

H. Chang, J. Yeo, J.-g. Kim, H. Kim, J. Lim, M. Lee, H. H. Kim, J. Ohk, H.-Y. Jeon, H. Lee, H. Jung, K.-W. Kim and V. N. Kim (2018) “Terminal Uridylyltransferases Execute Programmed Clearance of Maternal Transcriptome in Vertebrate Embryos” Molecular Cell, 70(1):72-82.e7.
H. Chang, J. Lim, M. Ha, and V. N. Kim (2014) “TAIL-seq: Genome-wide Determination of Poly(A) Tail Length and 3′ End Modifications” Molecular Cell, 53(6):1044-1052.
J. Lim, M. Ha, H. Chang, S. C. Kwon, D. K. Simanshu, D. J. Patel, and V. N. Kim (2014) “Uridylation by TUT4 and TUT7 marks mRNA for degradation” Cell, 159(6):1365-1376.
J. Cho, H. Chang, S. C. Kwon, B. Kim, Y. Kim, J. Choe, M. Ha, Y. K. Kim and V. N. Kim (2012) “LIN28A is a suppressor of ER-associated translation in embryonic stem cells” Cell, 151: 765-777.
J. Lim, D. Kim, Y. Lee, M. Ha, M. Lee, J. Yeo, H. Chang, J. Song, K. Ahn, V. N. Kim (2018) “Mixed tailing by TENT4A and TENT4B shields mRNA from rapid deadenylation” Science, eaam5794.

J. Park, J. Lee, and D. Jeon, “A 65-nm Neuromorphic Image Classification Processor With Energy-Efficient Training Through Direct Spike-Only Feedback,” IEEE Journal of Solid-State Circuits (JSSC), 2020.
S. Moon, K. Shin, and D. Jeon, “Enhancing Reliability of Analog Neural Network Processors,” IEEE Transactions on VLSI Systems (TVLSI), 2019.
J. Park, Y. Kwon, Y. Park, and D. Jeon, “Microarchitecture-Aware Code Generation for Deep Learning on Single-ISA Heterogeneous Multi-Core Mobile Processors,” IEEE Access, 2019.
D. Jeon, Q. Dong, Y. Kim, X. Wang, S. Chen, H. Yu, D. Blaauw, and D. Sylvester, “A 23-mW Face Recognition Processor with Mostly-Read 5T Memory in 40-nm CMOS,” IEEE Journal of Solid-State Circuits (JSSC), 2017.
D. Jeon, N. Ickes, P. Raina, H.-C. Wang, and A. P. Chandrakasan, “A 0.6V, 8mW 3D Vision Processor for a Navigation Device for the Visually Impaired,” IEEE International Solid-State Circuits Conference (ISSCC), 2016.
자가 학습이 가능한 초저전력 혼성신호 뉴로모픽 프로세서 설계, 과학기술정보통신부, 2019~2022.
고효율 딥러닝 하드웨어 가속기 개발, 한국과학기술연구원, 2019~2021.
모바일 시스템을 위한 저전력 머신 러닝 하드웨어 가속기 개발, 과학기술정보통신부, 2016~2019.

Sungkyu Jung, Myung Hee Lee and Jeongyoun Ahn (2018). “On the number of principal components in high dimensions,” Biometrika 105(2), 389-402.
Sungkyu Jung, Jeongyoun Ahn, and Yongho Jeon (2019). “Penalized Orthogonal Iteration for Sparse Estimation of Generalized Eigenvalue Problem” Journal of Computational and Graphical Statistics. 28(3) 710-721.
Gen Li and Sungkyu Jung (2017). “Incorporating Covariates into Integrated Factor Analysis of Multi-View Data,” Biometrics 73 (4), 1433-1442.
Byungwon Kim, Stephan Huckemann, Joern Schulz, and Sungkyu Jung (2019). “Small sphere distributions for directional data with application to medical imaging”, Scandinavian Journal of Statistics. 46(4) 1047-1071.

Atherogenic dyslipidemia promotes autoimmune follicular helper T cell responses via IL-27.
Nat Immunol. 2018 Jun;19(6):583-593. doi: 10.1038/s41590-018-0102-6
IL-27 confers a protumorigenic activity of regulatory T cells via CD39.
Proc Natl Acad Sci U S A. 2019 Feb 19;116(8):3106-3111.
Fibrinogen cleavage products and Toll-like receptor 4 promote the generation of programmed cell death 1 ligand 2-positive dendritic cells in allergic asthma.
J Allergy Clin Immunol. 2018 Aug;142(2):530-541
Proatherogenic conditions promote autoimmune T helper 17 cell responses in vivo.
Immunity. 2014 Jan 16;40(1):153-65
Follicular regulatory T cells expressing Foxp3 and Bcl-6 suppress germinal center reactions.
Nat Med. 2011 Jul 24;17(8):983-8.

Byeong-Min Lee*, Chisong Lee*, Shayan Fakhraei Lahiji, Ui-Won Jung , Gehoon Chung#, Hyungil Jung# Dissolving Microneedles for Rapid and Painless Local Anesthesia. Pharmaceutics (2020) 12(4):366.

2015 WHO classification of Lung tumours and Pleura, 2015 IARC press (공저자)
2020 WHO classification of Lung tumours and Pleura, 2020 IARC press (공저자 집필중)
Atlas of ALK and ROS1 testing in lung cancer 2nded. IASLC 2016 (공저자)
NCCN guidelines : Non-Small Cell Lung Cancer Asian Consensus Statements 2018
Targeted sequencing analysis of pulmonary adenocarcinoma with ultiple synchronous ground-glass/lepidic nodules J Thorac Oncol 2018;13:1776-83

Characterization of brain network supporting episodic memory in the absence of one medial temporal lobe.

Jeong W, Lee H, Kim JS, Chung CK.

Hum Brain Mapp. 2019 May;40(7):2188-2199.
Direct Stimulation of Human Hippocampus During Verbal Associative Encoding Enhances Subsequent Memory Recollection.

Jun S, Kim JS, Chung CK.

Front Hum Neurosci. 2019 Feb 5;13:23.
Neural basis of episodic memory in the intermediate term after medial temporal lobe resection.

Jeong W, Lee H, Kim JS, Chung CK.

J Neurosurg. 2018 Oct 26;131(3):790-798.
Postoperative seizure outcome-guided machine learning for interictal electrocorticography in neocortical epilepsy.

Park SC, Chung CK.

J Neurophysiol. 2018 Jun 1;119(6):2265-2275.
Disrupted Resting State Network of Fibromyalgia in Theta frequency.

Choe MK, Lim M, Kim JS, Lee DS, Chung CK.

Sci Rep. 2018 Feb 1;8(1):2064.
감각운동통합 상지 제어 뇌-컴퓨터 인터페이스 개발, 연구재단, 2016-2020
작업기억의 기전, 연구재단, 2018-2020

Hoang DT, Song J, Periwal V, and Jo J. Network inference in stochastic systems from neurons to currencies: Improved performance at small sample size, Phys Rev E, 99:023311 (2019)
Song J, Marsili M, and Jo J. Resolution and relevance trade-offs in deep learning, Journal of Statistical Mechanics, 12:123406 (2018)
Hoang DT, Jo J and Periwal V. Data-driven inference of hidden nodes in networks, Phys Rev E, 99:042114 (2019)
Cubero RJ, Jo J, Marsili M, Roudi Y and Song J. Statistical criticality arises in most informative representations, Journal of Statistical Mechanics, 6:063402 (2019)
Xu J and Jo J. Immunological recognition by articial neural networks, Journal of Korean Physical Society, 73:1908-1917 (2018)
Dynamics Inference from Time Series Data, NRF, 2019.06.01 - 2022.02.28

Multi-channel PINN: investigating scalable and transferable neural networks for drug discovery. Journal of Chemical Informatics 11.1 (2019): 46
Cognitive Profiling Related to Cerebral Amyloid Beta Burden Using Machine Learning Approaches. Frontiers in Aging Neuroscience. 10.3389 (2019)
The Disturbance in Dynamic Property in the Reconstructed State Space during Nitrous Oxide Administration. Neuroreport 30.3(2019):162–68
Differential Diagnosis of Jaw Pain using Informatics Technology. Journal of Oral Rehabilitation (2018)
A dynamic and parallel approach for repetitive prime labeling of XML with MapReduce, The Journal of Supercomputing, 73:2 (2017)
- 과제명: "빅데이터 기반의 신약 탐색 SW 개발"

 - 연구비 지원 기관: 미래창조 과학부

 - 연구 수행 기간: 2017. 04. 01. ~ 2019. 12. 31.
- 과제명: "WiseKB: 빅데이터 이해 기반 자가학습형 지식베이스 및 추론 기술 개발"

 - 연구비 지원 기관: 과학기술정보통신부

 - 연구 수행 기관: 2013. 05.01. ~ 현재

"The sexual brain, genes, and cognition: A machine-predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children." Human Brain Mapping
"Association of Genome-wide Polygenic Scores for Multiple Psychiatric and Common Traits Identify Preadolescent Youth with Risk for Suicide." JAMA Network Open
"Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth." Human Brain Mapping
"Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data." NPJ Digital Medicine
"Diagnosis and prognosis of Alzheimer's disease using brain morphometry and white matter connectomes." Neuroimage-Clinical
"Associations between brain structure and connectivity in infants and exposure to selective serotonin reuptake inhibitors during pregnancy. " JAMA pediatrics
"The Effects of Obstructive Sleep Apnea Syndrome on the Dentate Gyrus and Learning and Memory in Children.'" The Journal of Neuroscience
"Clinically anxious individuals show disrupted feedback between inferior frontal gyrus and prefrontal-limbic control circuit."  Journal of Neuroscience
"Neural correlates of aggression in medication-naive children with ADHD: multivariate analysis of morphometry and tractography."  Neuropsychopharmacology
"Hyper-reactive human ventral tegmental area and aberrant mesocorticolimbic connectivity in overgeneralization of fear in generalized anxiety disorder."  Journal of Neuroscience
"Circuit-wide structural and functional measures predict ventromedial prefrontal cortex fear generalization: implications for generalized anxiety disorder."  Journal of Neuroscience

Nano Letters 19, 7464−7469 (2019)
Nature Nanotechnology 13, 910-914 (2018)
Nature Communications 9, 351 (2018)
Nano Letters 18, 734 (2018)
Nature Communications 7, 13569 (2016)