Výsledky vyhledávání

Akademický článek

Advancing the early detection of canine cognitive dysfunction syndrome with machine learning-enhanced blood-based biomarkers

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

Zobrazit plný text záznamu

Akademický článek

Comparative analysis of whole exome sequencing kits for the canine genome.

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

Zobrazit plný text záznamu

Plný text ve formátu HTML

Akademický článek

Predicting chemical structure using reinforcement learning with a stack-augmented conditional variational autoencoder

Autor: Hwanhee Kim, Soohyun Ko, Byung Ju Kim, Sung Jin Ryu, Jaegyoon Ahn

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

Zobrazit plný text záznamu

Plný text ve formátu HTML

Akademický článek

Improved Prediction of Cancer Outcome Using Graph-Embedded Generative Adversarial Networks

Autor: Chihyun Park, Ilhwan Oh, Jonghwan Choi, Soohyun Ko, Jaegyoon Ahn

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

Zobrazit plný text záznamu

Akademický článek

GVES: machine learning model for identification of prognostic genes with a small dataset

Autor: Soohyun Ko, Jonghwan Choi, Jaegyoon Ahn

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

Zobrazit plný text záznamu

Plný text ve formátu HTML

Akademický článek

Increasing prediction accuracy of pathogenic staging by sample augmentation with a GAN.

Autor: ChangHyuk Kwon, Sangjin Park, Soohyun Ko, Jaegyoon Ahn

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

Zobrazit plný text záznamu

Improved Prediction of Cancer Outcome Using Graph-Embedded Generative Adversarial Networks

Autor: Soohyun Ko, Ilhwan Oh, Jonghwan Choi, Chihyun Park, Jaegyoon Ahn

Publikováno v: IEEE Access, Vol 9, Pp 20076-20088 (2021)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24953cdbfc64e1e4dfcf36ee1bf92fd6
https://doi.org/10.1109/access.2021.3054894

Zobrazit plný text záznamu

Increasing prediction accuracy of pathogenic staging by sample augmentation with a GAN

Autor: Soohyun Ko, Chang-Hyuk Kwon, Jaegyoon Ahn, Sangjin Park

Publikováno v: PLoS ONE
PLoS ONE, Vol 16, Iss 4, p e0250458 (2021)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e48f80f61d7073c579961dec2c5f9f98
http://europepmc.org/articles/PMC8078779

Zobrazit plný text záznamu

GVES: machine learning model for identification of prognostic genes with a small dataset

Autor: Soohyun Ko, Jaegyoon Ahn, Jonghwan Choi

Publikováno v: Scientific Reports
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

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání