Category / Seunghyun Park
Genetic risk score to predict biochemical recurrence after radical prostatectomy in prostate cancer: prospective cohort study
hc-OTU: A Fast and Accurate Method for Clustering Operational Taxonomic Units based on Homopolymer Compaction
deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks
Byunghan Lee, Junghwan Baek, Seunghyun Park, and Sungroh Yoon, in Proceedings of the 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB), pp. 434-442, Seattle, USA, October 2016.
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Computational Prediction of Competitive Endogenous RNA
Genome-wide detection of allelic genetic variation to predict biochemical recurrence after radical prostatectomy among prostate cancer patients using an exome SNP chip
Jong Jin Oh, Seunghyun Park, Sang Eun Lee, Sung Kyu Hong, Sangchul Lee, Hak Min Lee, Jung Keun Lee, Jin-Nyoung Ho, Sungroh Yoon, and Seok-Soo Byun, Journal of Cancer Research and Clinical Oncology, vol. 141, no. 8, pp. 1493-1501, August 2015.
The Use of Exome Genotyping to Predict Pathological Gleason Score Upgrade after Radical Prostatectomy in Low-Risk Prostate Cancer Patients
In-Depth Analysis of Interrelation between Quality Scores and Real Errors in Illumina Reads
Graph-Based Binning Method for Clustering 16s rRNA Sequences into OTUs
Hyun-soo Choi, Seunghyun Park, Chulwoo Kim, and Sungroh Yoon, in Proceedings of the 28th International Technical Conference on Circuits/Systems, Computers and Communications, Yeosu, Korea, July 2013.