LIVIVO - Search results -

Search results

Result 1 - 10 of total 1722

Search options

Article: Outbreak Investigation of Scarlet Fever in a Kindergarten.

Lee, Hyunju

2018 Volume 50, Issue 1, Page(s) 65–66

Language	English
Publishing date	2018-04-10
Publishing country	Korea (South)
Document type	Editorial ; Comment
ZDB-ID	2573798-3
ISSN	2093-2340
ISSN	2093-2340
DOI	10.3947/ic.2018.50.1.65
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 6842: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
ab Jg. 2022: Lesesaal (EG)

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: HUBO and QUBO models for prime factorization.

Jun, Kyungtaek / Lee, Hyunju

Scientific reports

2023 Volume 13, Issue 1, Page(s) 10080

Abstract: The security of the RSA cryptosystem is based on the difficulty of factoring a large number N into prime numbers [Formula: see text] and [Formula: see text] satisfying [Formula: see text]. This paper presents a prime factorization method using a D-Wave ... ...

Abstract	The security of the RSA cryptosystem is based on the difficulty of factoring a large number N into prime numbers [Formula: see text] and [Formula: see text] satisfying [Formula: see text]. This paper presents a prime factorization method using a D-Wave quantum computer that could threaten the RSA cryptosystem in the future. The starting point for this method is very simple, representing two prime numbers as qubits. Then, we set the difference between the product of the two prime numbers expressed in qubits and N as a cost function, and we find the solution when the cost function is minimized. D-Wave's quantum annealer can find the minimum value of any quadratic problem. However, the cost function must be a higher-order unconstrained optimization (HUBO) model because it contains second- or higher-order terms. We used a hybrid solver accessible via Leap, D-Wave's real-time quantum cloud service, and the dimod package provided by the D-Wave Ocean software development kit (SDK) to solve the HUBO problem. We also successfully factorized 102,454,763 with 26 logical qubits. In addition, we factorized 1,000,070,001,221 using the range-dependent Hamiltonian algorithm.
Language	English
Publishing date	2023-06-21
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-36813-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Joint triplet loss with semi-hard constraint for data augmentation and disease prediction using gene expression data

Yeonwoo Chung / Hyunju Lee

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 10

Abstract: Abstract The accurate prediction of patients with complex diseases, such as Alzheimer’s disease (AD), as well as disease stages, including early- and late-stage cancer, is challenging owing to substantial variability among patients and limited ... ...

Abstract	Abstract The accurate prediction of patients with complex diseases, such as Alzheimer’s disease (AD), as well as disease stages, including early- and late-stage cancer, is challenging owing to substantial variability among patients and limited availability of clinical data. Deep metric learning has emerged as a promising approach for addressing these challenges by improving data representation. In this study, we propose a joint triplet loss model with a semi-hard constraint (JTSC) to represent data in a small number of samples. JTSC strictly selects semi-hard samples by switching anchors and positive samples during the learning process in triplet embedding and combines a triplet loss function with an angular loss function. Our results indicate that JTSC significantly improves the number of appropriately represented samples during training when applied to the gene expression data of AD and to cancer stage prediction tasks. Furthermore, we demonstrate that using an embedding vector from JTSC as an input to the classifiers for AD and cancer stage prediction significantly improves classification performance by extracting more accurate features. In conclusion, we show that feature embedding through JTSC can aid in classification when there are a small number of samples compared to a larger number of features.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Molecular data representation based on gene embeddings for cancer drug response prediction

Sejin Park / Hyunju Lee

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 11

Abstract: Abstract Cancer drug response prediction is a crucial task in precision medicine, but existing models have limitations in effectively representing molecular profiles of cancer cells. Specifically, when these models represent molecular omics data such as ... ...

Abstract	Abstract Cancer drug response prediction is a crucial task in precision medicine, but existing models have limitations in effectively representing molecular profiles of cancer cells. Specifically, when these models represent molecular omics data such as gene expression, they employ a one-hot encoding-based approach, where a fixed gene set is selected for all samples and omics data values are assigned to specific positions in a vector. However, this approach restricts the utilization of embedding-vector-based methods, such as attention-based models, and limits the flexibility of gene selection. To address these issues, our study proposes gene embedding-based fully connected neural networks (GEN) that utilizes gene embedding vectors as input data for cancer drug response prediction. The GEN allows for the use of embedding-vector-based architectures and different gene sets for each sample, providing enhanced flexibility. To validate the efficacy of GEN, we conducted experiments on three cancer drug response datasets. Our results demonstrate that GEN outperforms other recently developed methods in cancer drug prediction tasks and offers improved gene representation capabilities. All source codes are available at https://github.com/DMCB-GIST/GEN/ .
Keywords	Medicine ; R ; Science ; Q
Subject code	004 ; 006
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: HUBO and QUBO models for prime factorization

Kyungtaek Jun / Hyunju Lee

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 10

Abstract: Abstract The security of the RSA cryptosystem is based on the difficulty of factoring a large number N into prime numbers $$p$$ p and $$q$$ q satisfying $$N=p\times q$$ N = p × q . This paper presents a prime factorization method using a D-Wave quantum ... ...

Abstract	Abstract The security of the RSA cryptosystem is based on the difficulty of factoring a large number N into prime numbers $$p$$ p and $$q$$ q satisfying $$N=p\times q$$ N = p × q . This paper presents a prime factorization method using a D-Wave quantum computer that could threaten the RSA cryptosystem in the future. The starting point for this method is very simple, representing two prime numbers as qubits. Then, we set the difference between the product of the two prime numbers expressed in qubits and N as a cost function, and we find the solution when the cost function is minimized. D-Wave's quantum annealer can find the minimum value of any quadratic problem. However, the cost function must be a higher-order unconstrained optimization (HUBO) model because it contains second- or higher-order terms. We used a hybrid solver accessible via Leap, D-Wave’s real-time quantum cloud service, and the dimod package provided by the D-Wave Ocean software development kit (SDK) to solve the HUBO problem. We also successfully factorized 102,454,763 with 26 logical qubits. In addition, we factorized 1,000,070,001,221 using the range-dependent Hamiltonian algorithm.
Keywords	Medicine ; R ; Science ; Q
Subject code	512
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Individual-specific postural discomfort prediction using decision tree models.

Hyun, Soomin / Lee, Hyunju / Park, Woojin

Applied ergonomics

2024 Volume 118, Page(s) 104282

Abstract: The objective of the current study was to explore the utilization of the decision tree (DT) algorithm to model posture-discomfort relationships at the individual level. The DT algorithm has the advantage that it makes no assumptions about the ... ...

Abstract	The objective of the current study was to explore the utilization of the decision tree (DT) algorithm to model posture-discomfort relationships at the individual level. The DT algorithm has the advantage that it makes no assumptions about the distribution of data, is robust in representing non-linear data with noise, and produces white-box models that are interpretable. Individual-level modelling is essential for examining individual-specific postural discomfort perception processes and understanding the inter-individual variability. It also has practical applications, including the development of individual-specific digital human models and more precise and informative population accommodation analysis. Individual-specific DT models were generated using postural discomfort rating data for various seated upper body postures to predict discomfort based on postural and task variables. The individual-specific DT models accurately predicted postural discomfort and revealed large inter-individual variability in the modelling results. DT modelling is expected to greatly facilitate investigating the human discomfort perception process.
MeSH term(s)	Humans ; Decision Trees ; Male ; Algorithms ; Female ; Posture/physiology ; Adult ; Young Adult ; Sitting Position
Language	English
Publishing date	2024-04-03
Publishing country	England
Document type	Journal Article
ZDB-ID	2003513-5
ISSN	1872-9126 ; 0003-6870
ISSN (online)	1872-9126
ISSN	0003-6870
DOI	10.1016/j.apergo.2024.104282
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Molecular data representation based on gene embeddings for cancer drug response prediction.

Park, Sejin / Lee, Hyunju

Scientific reports

2023 Volume 13, Issue 1, Page(s) 21898

Abstract: Cancer drug response prediction is a crucial task in precision medicine, but existing models have limitations in effectively representing molecular profiles of cancer cells. Specifically, when these models represent molecular omics data such as gene ... ...

Abstract	Cancer drug response prediction is a crucial task in precision medicine, but existing models have limitations in effectively representing molecular profiles of cancer cells. Specifically, when these models represent molecular omics data such as gene expression, they employ a one-hot encoding-based approach, where a fixed gene set is selected for all samples and omics data values are assigned to specific positions in a vector. However, this approach restricts the utilization of embedding-vector-based methods, such as attention-based models, and limits the flexibility of gene selection. To address these issues, our study proposes gene embedding-based fully connected neural networks (GEN) that utilizes gene embedding vectors as input data for cancer drug response prediction. The GEN allows for the use of embedding-vector-based architectures and different gene sets for each sample, providing enhanced flexibility. To validate the efficacy of GEN, we conducted experiments on three cancer drug response datasets. Our results demonstrate that GEN outperforms other recently developed methods in cancer drug prediction tasks and offers improved gene representation capabilities. All source codes are available at https://github.com/DMCB-GIST/GEN/ .
MeSH term(s)	Humans ; Neural Networks, Computer ; Software ; Antineoplastic Agents/pharmacology ; Antineoplastic Agents/therapeutic use ; Neoplasms/drug therapy ; Neoplasms/genetics ; Precision Medicine
Chemical Substances	Antineoplastic Agents
Language	English
Publishing date	2023-12-11
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-49003-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Joint triplet loss with semi-hard constraint for data augmentation and disease prediction using gene expression data.

Chung, Yeonwoo / Lee, Hyunju

Scientific reports

2023 Volume 13, Issue 1, Page(s) 18178

Abstract: The accurate prediction of patients with complex diseases, such as Alzheimer's disease (AD), as well as disease stages, including early- and late-stage cancer, is challenging owing to substantial variability among patients and limited availability of ... ...

Abstract	The accurate prediction of patients with complex diseases, such as Alzheimer's disease (AD), as well as disease stages, including early- and late-stage cancer, is challenging owing to substantial variability among patients and limited availability of clinical data. Deep metric learning has emerged as a promising approach for addressing these challenges by improving data representation. In this study, we propose a joint triplet loss model with a semi-hard constraint (JTSC) to represent data in a small number of samples. JTSC strictly selects semi-hard samples by switching anchors and positive samples during the learning process in triplet embedding and combines a triplet loss function with an angular loss function. Our results indicate that JTSC significantly improves the number of appropriately represented samples during training when applied to the gene expression data of AD and to cancer stage prediction tasks. Furthermore, we demonstrate that using an embedding vector from JTSC as an input to the classifiers for AD and cancer stage prediction significantly improves classification performance by extracting more accurate features. In conclusion, we show that feature embedding through JTSC can aid in classification when there are a small number of samples compared to a larger number of features.
MeSH term(s)	Humans ; Deep Learning ; Learning ; Alzheimer Disease/genetics ; Neoplasms/genetics ; Gene Expression
Language	English
Publishing date	2023-10-24
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-45467-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Information amount, accuracy, and relevance of generative artificial intelligence platforms’ answers regarding learning objectives of medical arthropodology evaluated in English and Korean queries in December 2023: a descriptive study

Lee, Hyunju / Park, Soobin

Journal of educational evaluation for health professions

2023 Volume 20, Page(s) 39

Abstract: Purpose: This study assessed the performance of 6 generative artificial intelligence (AI) platforms on the learning objectives of medical arthropodology in a parasitology class in Korea. We examined the AI platforms’ performance by querying in Korean ... ...

Abstract	Purpose: This study assessed the performance of 6 generative artificial intelligence (AI) platforms on the learning objectives of medical arthropodology in a parasitology class in Korea. We examined the AI platforms’ performance by querying in Korean and English to determine their information amount, accuracy, and relevance in prompts in both languages. Methods: From December 15 to 17, 2023, 6 generative AI platforms—Bard, Bing, Claude, Clova X, GPT-4, and Wrtn—were tested on 7 medical arthropodology learning objectives in English and Korean. Clova X and Wrtn are platforms from Korean companies. Responses were evaluated using specific criteria for the English and Korean queries. Results: Bard had abundant information but was fourth in accuracy and relevance. GPT-4, with high information content, ranked first in accuracy and relevance. Clova X was 4th in amount but 2nd in accuracy and relevance. Bing provided less information, with moderate accuracy and relevance. Wrtn’s answers were short, with average accuracy and relevance. Claude AI had reasonable information, but lower accuracy and relevance. The responses in English were superior in all aspects. Clova X was notably optimized for Korean, leading in relevance. Conclusion: In a study of 6 generative AI platforms applied to medical arthropodology, GPT-4 excelled overall, while Clova X, a Korea-based AI product, achieved 100% relevance in Korean queries, the highest among its peers. Utilizing these AI platforms in classrooms improved the authors’ self-efficacy and interest in the subject, offering a positive experience of interacting with generative AI platforms to question and receive information.
MeSH term(s)	Artificial Intelligence ; Language ; Learning ; Republic of Korea
Language	English
Publishing date	2023-12-28
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	2586380-0
ISSN	1975-5937 ; 1975-5937
ISSN (online)	1975-5937
ISSN	1975-5937
DOI	10.3352/jeehp.2023.20.39
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Transcriptomic data in tumor-adjacent normal tissues harbor prognostic information on multiple cancer types.

Oh, Euiyoung / Lee, Hyunju

Cancer medicine

2023 Volume 12, Issue 10, Page(s) 11960–11970

Abstract: Background: In identifying prognostic markers in cancer, the roles of tumor-adjacent normal tissues are often confined to drawing expression differences between tumor and normal tissues rather than being treated as the main targets of investigations. ... ...

Abstract	Background: In identifying prognostic markers in cancer, the roles of tumor-adjacent normal tissues are often confined to drawing expression differences between tumor and normal tissues rather than being treated as the main targets of investigations. Thus, differential expression analysis between tumors and adjacent normal tissues is performed prior to prognostic analysis in previous studies. However, recent studies have suggested that the prognostic relevance of differentially expressed genes (DEGs) is insignificant for some cancers, contradicting conventional approaches METHODS: This study investigated the prognostic efficacy of transcriptomic data from tumors and adjacent normal tissues using The Cancer Genome Atlas dataset. Prognostic analysis using Cox regression models and survival prediction using machine-learning models and feature selection methods were employed. Results: The results revealed that for kidney, liver, and head and neck cancer, adjacent normal tissues harbored higher proportions of prognostic genes and exhibited better survival prediction performance than tumor tissues and DEGs in machine-learning models. Furthermore, the application of a distance correlation-based feature selection method to kidney and liver cancer using external datasets revealed that the selected genes for adjacent normal tissues exhibited higher prediction performance than those for tumor tissues. The study results suggest that the expression levels of genes in adjacent normal tissues are potential prognostic markers. The source code of this study is available at https://github.com/DMCB-GIST/Survival_Normal.
MeSH term(s)	Humans ; Transcriptome ; Prognosis ; Liver Neoplasms/pathology ; Gene Expression Profiling
Language	English
Publishing date	2023-03-31
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2659751-2
ISSN	2045-7634 ; 2045-7634
ISSN (online)	2045-7634
ISSN	2045-7634
DOI	10.1002/cam4.5864
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

To top

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito