Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Soohyun Ko"'
Autor:
Chae Young Kim, Jinhye Kim, Sunmi Yoon, Isaac Jinwon Yi, Hyuna Lee, Sanghyuk Seo, Dae Won Kim, Soohyun Ko, Sun-A Kim, Changhyuk Kwon, Sun Shin Yi
Publikováno v:
Frontiers in Veterinary Science, Vol 11 (2024)
Up to half of the senior dogs suffer from canine cognitive dysfunction syndrome (CCDS), the diagnosis method relies on subjective questionnaires such as canine cognitive dysfunction rating (CCDR) scores. Therefore, the necessity of objective diagnosi
Externí odkaz:
https://doaj.org/article/b4fea0a779bd4103b753d29d64863b95
Autor:
Jinhee Jang, Yong-Jik Lee, Soohyun Ko, A M Abd El-Aty, Ibrahim Gecili, Ji Hoon Jeong, ChangHyuk Kwon, Tae Woo Jung
Publikováno v:
PLoS ONE, Vol 19, Iss 11, p e0312203 (2024)
ObjectivesAs the public's interest in companion dogs grows, health issues in these animals are also emerging, necessitating the optimization of whole exome sequencing (WES) as a valuable method for disease prediction. While WES targeting the human ge
Externí odkaz:
https://doaj.org/article/b91a990d412d48ee946fa9ea7ec86596
Publikováno v:
Journal of Cheminformatics, Vol 14, Iss 1, Pp 1-12 (2022)
Abstract In this paper, a reinforcement learning model is proposed that can maximize the predicted binding affinity between a generated molecule and target proteins. The model used to generate molecules in the proposed model was the Stacked Condition
Externí odkaz:
https://doaj.org/article/01327ec5eb24459cbd7fb1c48ad25b1b
Publikováno v:
IEEE Access, Vol 9, Pp 20076-20088 (2021)
Precise prognosis of cancer patients is important because it is associated with suggesting appropriate therapeutic strategies. Several computational and statistical methods have been proposed, but further improvement of these methods in terms of pred
Externí odkaz:
https://doaj.org/article/4c578031689942a981df1c9b902f3b37
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
Abstract Machine learning may be a powerful approach to more accurate identification of genes that may serve as prognosticators of cancer outcomes using various types of omics data. However, to date, machine learning approaches have shown limited pre
Externí odkaz:
https://doaj.org/article/2ccc46503e47424fa94cca6e873e2639
Publikováno v:
PLoS ONE, Vol 16, Iss 4, p e0250458 (2021)
Accurate prediction of cancer stage is important in that it enables more appropriate treatment for patients with cancer. Many measures or methods have been proposed for more accurate prediction of cancer stage, but recently, machine learning, especia
Externí odkaz:
https://doaj.org/article/fa8cb78838934b6c9ae56be039cf2fbd
Publikováno v:
IEEE Access, Vol 9, Pp 20076-20088 (2021)
Precise prognosis of cancer patients is important because it is associated with suggesting appropriate therapeutic strategies. Several computational and statistical methods have been proposed, but further improvement of these methods in terms of pred
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24953cdbfc64e1e4dfcf36ee1bf92fd6
https://doi.org/10.1109/access.2021.3054894
https://doi.org/10.1109/access.2021.3054894
Autor:
Soohyun Ko
Publikováno v:
Korean Academy Welfare Counseling. 8:47-91
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f4944b26a4205cfd9b3fd5a1568acc8e
https://doi.org/10.20497/jwce.2019.8.1.47
https://doi.org/10.20497/jwce.2019.8.1.47
Publikováno v:
PLoS ONE
PLoS ONE, Vol 16, Iss 4, p e0250458 (2021)
PLoS ONE, Vol 16, Iss 4, p e0250458 (2021)
Accurate prediction of cancer stage is important in that it enables more appropriate treatment for patients with cancer. Many measures or methods have been proposed for more accurate prediction of cancer stage, but recently, machine learning, especia
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e48f80f61d7073c579961dec2c5f9f98
http://europepmc.org/articles/PMC8078779
http://europepmc.org/articles/PMC8078779
Publikováno v:
Scientific Reports
Scientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
Scientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
Machine learning may be a powerful approach to more accurate identification of genes that may serve as prognosticators of cancer outcomes using various types of omics data. However, to date, machine learning approaches have shown limited prediction a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::449fa64d66a4ca40bb269b79a9ea1fae
https://pubmed.ncbi.nlm.nih.gov/33431999
https://pubmed.ncbi.nlm.nih.gov/33431999