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생물정보학, 생물통계, 기계학습, 데이터마이닝, 디지털 헬스케어 등
듀크대학교 biostatistics and bioinformatics 학과 박사후 연구원
삼성서울병원 의생명정보센터 책임연구원
Marquis Who's Who in the World 등재
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Transcription Factor Binding Sites의
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1. Mutational hotspots in the mitochondrial genome of lung cancer, Biochemical and
Biophysical Research Communications, 407, 23-27. 2011.
2. Multiple Testing for Gene Sets from Microarray Experiment, BMC Bioinformatics,
12, 209. 2011.
3. Phase II study of gefitinib versus erlotinib in patients with advanced non-small
cell lung cancer who failed previous chemotherapy, Lung Cancer, 75, 82-88. 2012.
4. Phase III Trial Comparing Capecitabine Plus Cisplatin Versus Capecitabine Plus Cisplat
in With Concurrent Capecitabine Radiotherapy in Completely Resected Gastric Cancer
With D2 Lymph Node Dissection: The ARTIST Trial, Journal of Clinical Oncology,
30, 268-273. 2012.
5. Analysis of survival data with group lasso, Communications in Statistics - Simulation
and Computation, 41, 1593-1605. 2012.
6. Somatic hypermutation and outcomes of platinum based chemotherapy in patients
with high grade serous ovarian cancer, Gynecologic Oncology, 126, 103-108. 2012.
7. Gliomatosis peritonei is associated with frequent recurrence, but does not affect over
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8. Predictive Modeling Using a Somatic Mutational Profile in Ovarian High Grade Serous
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9. Poor prognosis of uterine serous carcinoma compared with grade 3 endometrioid car
cinoma in early stage patients, Virchows Archiv, 462, 289-296. 2013.
10. Prediction of a Time-to-Event Trait Using Genome Wide SNP Data,
BMC Bioinformatics, 14, 58. 2013.
11. Clinical relevance of gain-of-function mutations of p53 in high-grade serous
ovarian carcinoma, PLoS ONE, 8, e72609. 2013.
12. SNP selection in Genome-Wide Association Studies via Penalized Support
Vector Machine with MaxTest, Computational and Mathematical Methods in
Medicine, 2013, 340678. 2013.
13. Prediction of Disease-Free Survival in Hepatocellular Carcinoma by Gene
Expression Profiling, Annals of Surgical Oncology, 20, 3747-3753. 2013.
14. A Survey of c-MET Expression and Amplification in 287 Patients with Hepatocellular
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15. Genetic profiling to predict recurrence of early cervical cancer,
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16. Gastric cancer (GC) patients with hedgehog pathway activation: PTCH1 and
GLI2 as independent prognostic factors, Targeted Oncology, 8, 271-280. 2013.
17. Transcription factor-binding site identification and gene classification via fusion
of the supervised-weighted discrete kernel clustering and support vector machine,
Journal of Applied Statistics, 41, 573-581. 2014.
18. Gene expression profiles for the prediction of progression-free survival in
diffuse large B cell lymphoma: results of a DASL assay, Annals of
Hematology, 93, 437-447. 2014.
19. A distinctive ovarian cancer molecular subgroup characterized by poor
prognosis and somatic focal copy number amplifications at chromosome 19,
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20. Nanostring-Based Multigene Assay to Predict Recurrence for Gastric Cancer
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21. Effect of simvastatin plus cetuximab/irinotecan for KRAS mutant colorectal
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cetuximab, Invest New Drugs, 32, 535-541. 2014.
22. The expression pattern of 19 genes predicts the histology of endometrial
carcinoma, Scientific Reports, 4, 5174. 2014.
23. Transcriptome analysis of CD133-positive stem cells and prognostic
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24. Statistical Issues in Design and Analysis of NanoString Projects,
Cancer Informatics, 13, 35-43. 2014.
25. A Seven-gene Signature Can Predict Distant Recurrence in Patients with
Triple-negative Breast Cancers (TNBCs) Who Receive Adjuvant Chemotherapy
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26. EBV-positive diffuse large B-cell lymphoma in young adults:
is this a distinct disease entity?, Annals of Oncology, 26, 548-555. 2015.
27. Practical approach to estimate sample size for prediction model construction
using high throughput data, Journal of Biomedical Informatics, 35, 355:362. 2015.
28. Integrated Copy Number and Gene Expression Analysis of Epstein–Barr
Virus (EBV)Positive Diffuse Large B Cell Lymphoma (DLBCL) Compared with
EBV Negative DLBCL, the Genes, Chromosomes and Cancer, 54, 383-
29. Kernel-based random effect time-varying coefficient model for longitudinal data.
Neurocomputing, 267, 500-507, 2017.
30. Semivarying coefficient least-squares support vector regression for analyzing high-dimensional
gene-environmental data. Journal of Applied Statistics, 1-12, 2017.
31. Detection of chromosome structural variation by targeted next-generation sequencing and
a deep learning application, Scientific Reports, 9, 3644, 2019.
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