Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Sunkyu Kim"'
Autor:
Jaesang Kim, Byungwook Lee, Ye Eun Jang, Daehan Kim, Insu Jang, Jaewoo Kang, Subin Cho, Keonwoo Kim, Sanghyuk Lee, Sangok Kim, Sunkyu Kim, Jaewon Kim, Jimin Hwang
Publikováno v:
Nucleic Acids Research
Fusion genes represent an important class of biomarkers and therapeutic targets in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data (ChimerSeq) and text mining of publications (ChimerPub) with
Autor:
Sunkyu Kim, Choong-kun Lee, Yonghwa Choi, Eun Sil Baek, Jeong Eun Choi, Joon Seok Lim, Jaewoo Kang, Sang Joon Shin
Publikováno v:
Frontiers in Oncology, Vol 11 (2021)
Frontiers in Oncology
Frontiers in Oncology
Most electronic medical records, such as free-text radiological reports, are unstructured; however, the methodological approaches to analyzing these accumulating unstructured records are limited. This article proposes a deep-transfer-learning-based n
Publikováno v:
Bioinformatics
Motivation Recent advances in deep learning have offered solutions to many biomedical tasks. However, there remains a challenge in applying deep learning to survival analysis using human cancer transcriptome data. As the number of genes, the input va
Autor:
Olexandr Isayev, David H. Drewry, Michael Mason, Jaewoo Kang, Andrew Lamb, Minji Jeon, Gustavo Stolovitzky, Sunkyu Kim, Carrow I. Wells, Robert J. Allaway, Tudor I. Oprea, Shuya Li, Stephen J. Capuzzi, Anna Cichonska, Kristen K. Dang, Tero Aittokallio, Timothy M. Willson, Ziaurrehman Tanoli, Krister Wennerberg, Sungjoon Park, Justin Guinney, Gregory Koytiger, Fangping Wan, Jianyang Zeng, Balaguru Ravikumar, Mariya Popova, Avner Schlessinger
Despite decades of intensive search for compounds that modulate the activity of particular targets, there are currently small-molecules available only for a small proportion of the human proteome. Effective approaches are therefore required to map th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::190ce84be10270927d9a2faa3fe5cb07
https://doi.org/10.1101/2019.12.31.891812
https://doi.org/10.1101/2019.12.31.891812
Publikováno v:
Bioinformatics
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity am
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7176123b4c09770623bfd6b80a9f3aa5
http://arxiv.org/abs/1901.08746
http://arxiv.org/abs/1901.08746
Autor:
Donghyeon Park, Hwisang Jeon, Yonghwa Choi, Miyoung Ko, Minji Jeon, Jinhyuk Lee, Sunkyu Kim, Aik Choon Tan, Jaewoo Kang
Publikováno v:
Bioinformatics (Oxford, England). 35(24)
Motivation Traditional drug discovery approaches identify a target for a disease and find a compound that binds to the target. In this approach, structures of compounds are considered as the most important features because it is assumed that similar
Publikováno v:
BMC Medical Genomics
BMC Medical Genomics, Vol 11, Iss S2, Pp 57-69 (2018)
BMC Medical Genomics, Vol 11, Iss S2, Pp 57-69 (2018)
Background Embedding techniques for converting high-dimensional sparse data into low-dimensional distributed representations have been gaining popularity in various fields of research. In deep learning models, embedding is commonly used and proven to
Autor:
Sungjoon Park, Sunkyu Kim, Kyubum Lee, Byounggun Kim, Sunwon Lee, Wonho Shin, Aik Choon Tan, Yonghwa Choi, Jaewoo Kang, Seongsoon Kim
Publikováno v:
BMC Bioinformatics
BMC Bioinformatics, Vol 19, Iss 1, Pp 1-13 (2018)
BMC Bioinformatics, Vol 19, Iss 1, Pp 1-13 (2018)
Background Molecular biomarkers that can predict drug efficacy in cancer patients are crucial components for the advancement of precision medicine. However, identifying these molecular biomarkers remains a laborious and challenging task. Next-generat