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Article: Downregulation of

Lee, Sang-Soo / Park, JeongMan / Oh, Sooyeon / Kwack, KyuBum

2022 Volume 14, Issue 5

Abstract: Gastric cancer is a common tumor, with a high mortality rate. The severity of gastric cancer is assessed by TNM staging. Long noncoding RNAs (lncRNAs) play a role in cancer treatment; investigating the clinical significance of novel biomarkers associated ...

Abstract	Gastric cancer is a common tumor, with a high mortality rate. The severity of gastric cancer is assessed by TNM staging. Long noncoding RNAs (lncRNAs) play a role in cancer treatment; investigating the clinical significance of novel biomarkers associated with TNM staging, such as lncRNAs, is important. In this study, we investigated the association between the expression of the lncRNA
Language	English
Publishing date	2022-02-23
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers14051149
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Broccoli Cultivated with Deep Sea Water Mineral Fertilizer Enhances Anti-Cancer and Anti-Inflammatory Effects of AOM/DSS-Induced Colorectal Cancer in C57BL/6N Mice.

Lee, Yeon-Jun / Pan, Yanni / Lim, Daewoo / Park, Seung-Hwan / Sin, Sin-Il / Kwack, KyuBum / Park, Kun-Young

International journal of molecular sciences

2024 Volume 25, Issue 3

Abstract: This study aimed to determine the alleviating effect of broccoli grown with deep sea water mineral (DSWM) fertilizer extracted from deep sea water on the development of colorectal cancer in C57BL/6N mice treated with AOM/DSS. Naturaldream Fertilizer ... ...

Abstract	This study aimed to determine the alleviating effect of broccoli grown with deep sea water mineral (DSWM) fertilizer extracted from deep sea water on the development of colorectal cancer in C57BL/6N mice treated with AOM/DSS. Naturaldream Fertilizer Broccoli (NFB) cultured with deep sea water minerals (DSWM) showed a higher antioxidant effect and mineral content. In addition, orally administered NFB, showed a level of recovery in the colon and spleen tissues of mice compared with those in normal mice through hematoxylin and eosin (H&E) staining. Orally administered NFB showed the inhibition of the expression of inflammatory cytokine factors IL-1β, IL-6, TNF, IFN-γ, and IL-12 while increasing the expression of IL-10. Furthermore, the expression of inflammatory cytokines and NF-κB in the liver tissue was inhibited, and that of inflammatory enzymes, such as COX-2 and iNOS, was reduced. In the colon tissue, the expression of p53 and p21 associated with cell cycle arrest increased, and that of Bcl-2 associated with apoptosis decreased. Additionally, the expression of Bax, Bad, Bim, Bak, caspase 9, and caspase 3 increased, indicating enhanced activation of apoptosis-related factors. These results demonstrate that oral administration of broccoli cultivated using DSWM significantly restores spleen and colon tissues and simultaneously inhibits the NF-κB pathway while significantly decreasing cytokine expression. Moreover, by inducing cell cycle arrest and activating cell apoptosis, they also suggest alleviating AOM/DSS-induced colon cancer symptoms in C57BL/6N mice.
MeSH term(s)	Mice ; Animals ; Colitis/metabolism ; NF-kappa B/metabolism ; Fertilizers/adverse effects ; Brassica/metabolism ; Mice, Inbred C57BL ; Colonic Neoplasms/metabolism ; Cytokines/metabolism ; Mice, Inbred Strains ; Minerals/metabolism ; Anti-Inflammatory Agents/adverse effects ; Seawater ; Dextran Sulfate/adverse effects ; Colon/metabolism ; Disease Models, Animal
Chemical Substances	NF-kappa B ; Fertilizers ; Cytokines ; Minerals ; Anti-Inflammatory Agents ; Dextran Sulfate (9042-14-2)
Language	English
Publishing date	2024-01-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms25031650
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Article ; Online: Machine Learning Approach to Facilitate Knowledge Synthesis at the Intersection of Liver Cancer, Epidemiology, and Health Disparities Research.

Hyams, Travis C / Luo, Ling / Hair, Brionna / Lee, Kyubum / Lu, Zhiyong / Seminara, Daniela

JCO clinical cancer informatics

2022 Volume 6, Page(s) e2100129

Abstract: Purpose: Liver cancer is a global challenge, and disparities exist across multiple domains and throughout the disease continuum. However, liver cancer's global epidemiology and etiology are shifting, and the literature is rapidly evolving, presenting a ... ...

Abstract	Purpose: Liver cancer is a global challenge, and disparities exist across multiple domains and throughout the disease continuum. However, liver cancer's global epidemiology and etiology are shifting, and the literature is rapidly evolving, presenting a challenge to the synthesis of knowledge needed to identify areas of research needs and to develop research agendas focusing on disparities. Machine learning (ML) techniques can be used to semiautomate the literature review process and improve efficiency. In this study, we detail our approach and provide practical benchmarks for the development of a ML approach to classify literature and extract data at the intersection of three fields: liver cancer, health disparities, and epidemiology. Methods: We performed a six-phase process including: training (I), validating (II), confirming (III), and performing error analysis (IV) for a ML classifier. We then developed an extraction model (V) and applied it (VI) to the liver cancer literature identified through PubMed. We present precision, recall, F1, and accuracy metrics for the classifier and extraction models as appropriate for each phase of the process. We also provide the results for the application of our extraction model. Results: With limited training data, we achieved a high degree of accuracy for both our classifier and for the extraction model for liver cancer disparities research literature performed using epidemiologic methods. The disparities concept was the most challenging to accurately classify, and concepts that appeared infrequently in our data set were the most difficult to extract. Conclusion: We provide a roadmap for using ML to classify and extract comprehensive information on multidisciplinary literature. Our technique can be adapted and modified for other cancers or diseases where disparities persist.
MeSH term(s)	Humans ; Liver Neoplasms/diagnosis ; Liver Neoplasms/epidemiology ; Liver Neoplasms/therapy ; Machine Learning
Language	English
Publishing date	2022-05-27
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Intramural
ISSN	2473-4276
ISSN (online)	2473-4276
DOI	10.1200/CCI.21.00129
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: LitSuggest: a web-based system for literature recommendation and curation using machine learning.

Allot, Alexis / Lee, Kyubum / Chen, Qingyu / Luo, Ling / Lu, Zhiyong

Nucleic acids research

2021 Volume 49, Issue W1, Page(s) W352–W358

Abstract: Searching and reading relevant literature is a routine practice in biomedical research. However, it is challenging for a user to design optimal search queries using all the keywords related to a given topic. As such, existing search systems such as ... ...

Abstract	Searching and reading relevant literature is a routine practice in biomedical research. However, it is challenging for a user to design optimal search queries using all the keywords related to a given topic. As such, existing search systems such as PubMed often return suboptimal results. Several computational methods have been proposed as an effective alternative to keyword-based query methods for literature recommendation. However, those methods require specialized knowledge in machine learning and natural language processing, which can make them difficult for biologists to utilize. In this paper, we propose LitSuggest, a web server that provides an all-in-one literature recommendation and curation service to help biomedical researchers stay up to date with scientific literature. LitSuggest combines advanced machine learning techniques for suggesting relevant PubMed articles with high accuracy. In addition to innovative text-processing methods, LitSuggest offers multiple advantages over existing tools. First, LitSuggest allows users to curate, organize, and download classification results in a single interface. Second, users can easily fine-tune LitSuggest results by updating the training corpus. Third, results can be readily shared, enabling collaborative analysis and curation of scientific literature. Finally, LitSuggest provides an automated personalized weekly digest of newly published articles for each user's project. LitSuggest is publicly available at https://www.ncbi.nlm.nih.gov/research/litsuggest.
MeSH term(s)	COVID-19 ; Data Curation ; Healthcare Disparities ; Humans ; Internet ; Liver Neoplasms/epidemiology ; Machine Learning ; Publications ; Software
Language	English
Publishing date	2021-05-05
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Intramural
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkab326
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Recent advances of automated methods for searching and extracting genomic variant information from biomedical literature.

Lee, Kyubum / Wei, Chih-Hsuan / Lu, Zhiyong

Briefings in bioinformatics

2020 Volume 22, Issue 3

Abstract: Motivation: To obtain key information for personalized medicine and cancer research, clinicians and researchers in the biomedical field are in great need of searching genomic variant information from the biomedical literature now than ever before. Due ... ...

Abstract	Motivation: To obtain key information for personalized medicine and cancer research, clinicians and researchers in the biomedical field are in great need of searching genomic variant information from the biomedical literature now than ever before. Due to the various written forms of genomic variants, however, it is difficult to locate the right information from the literature when using a general literature search system. To address the difficulty of locating genomic variant information from the literature, researchers have suggested various solutions based on automated literature-mining techniques. There is, however, no study for summarizing and comparing existing tools for genomic variant literature mining in terms of how to search easily for information in the literature on genomic variants. Results: In this article, we systematically compared currently available genomic variant recognition and normalization tools as well as the literature search engines that adopted these literature-mining techniques. First, we explain the problems that are caused by the use of non-standard formats of genomic variants in the PubMed literature by considering examples from the literature and show the prevalence of the problem. Second, we review literature-mining tools that address the problem by recognizing and normalizing the various forms of genomic variants in the literature and systematically compare them. Third, we present and compare existing literature search engines that are designed for a genomic variant search by using the literature-mining techniques. We expect this work to be helpful for researchers who seek information about genomic variants from the literature, developers who integrate genomic variant information from the literature and beyond.
MeSH term(s)	Data Mining ; Genetic Variation ; Precision Medicine ; PubMed ; Publications ; Search Engine
Language	English
Publishing date	2020-08-08
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Intramural ; Review
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbaa142
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Author Correction: ΔNp63 regulates a common landscape of enhancer associated genes in non-small cell lung cancer.

Napoli, Marco / Wu, Sarah J / Gore, Bethanie L / Abbas, Hussein A / Lee, Kyubum / Checker, Rahul / Dhar, Shilpa / Rajapakshe, Kimal / Tan, Aik Choon / Lee, Min Gyu / Coarfa, Cristian / Flores, Elsa R

Nature communications

2022 Volume 13, Issue 1, Page(s) 1717

Language	English
Publishing date	2022-03-25
Publishing country	England
Document type	Published Erratum
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-022-29349-7
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Article ; Online: Immune landscape in molecular subtypes of human papillomavirus-negative head and neck cancer.

Xie, Mengyu / Chaudhary, Ritu / Slebos, Robbert J C / Lee, Kyubum / Song, Feifei / Poole, Maria I / Hoening, Dirk S / Noel, Leenil C / Hernandez-Prera, Juan C / Conejo-Garcia, Jose R / Chung, Christine H / Tan, Aik Choon

Molecular carcinogenesis

2023 Volume 63, Issue 1, Page(s) 120–135

Abstract: Head and neck squamous cell carcinomas (HNSCC) remain a poorly understood disease clinically and immunologically. HPV is a known risk factor of HNSCC associated with better outcome, whereas HPV-negative HNSCC are more heterogeneous in outcome. Gene ... ...

Abstract	Head and neck squamous cell carcinomas (HNSCC) remain a poorly understood disease clinically and immunologically. HPV is a known risk factor of HNSCC associated with better outcome, whereas HPV-negative HNSCC are more heterogeneous in outcome. Gene expression signatures have been developed to classify HNSCC into four molecular subtypes (classical, basal, mesenchymal, and atypical). However, the molecular underpinnings of treatment response and the immune landscape for these molecular subtypes are largely unknown. Herein, we described a comprehensive immune landscape analysis in three independent HNSCC cohorts (>700 patients) using transcriptomics data. We assigned the HPV
MeSH term(s)	Humans ; Squamous Cell Carcinoma of Head and Neck/genetics ; Human Papillomavirus Viruses ; Papillomavirus Infections/complications ; Papillomavirus Infections/genetics ; Head and Neck Neoplasms/genetics ; Immunotherapy ; Tumor Microenvironment
Language	English
Publishing date	2023-09-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	1004029-8
ISSN	1098-2744 ; 0899-1987
ISSN (online)	1098-2744
ISSN	0899-1987
DOI	10.1002/mc.23640
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 2664: Show issues

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Article ; Online: Author Correction

Marco Napoli / Sarah J. Wu / Bethanie L. Gore / Hussein A. Abbas / Kyubum Lee / Rahul Checker / Shilpa Dhar / Kimal Rajapakshe / Aik Choon Tan / Min Gyu Lee / Cristian Coarfa / Elsa R. Flores

Nature Communications, Vol 13, Iss 1, Pp 1-

ΔNp63 regulates a common landscape of enhancer associated genes in non-small cell lung cancer

2022 Volume 1

Keywords	Science ; Q
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Drug drug interaction extraction from the literature using a recursive neural network.

Sangrak Lim / Kyubum Lee / Jaewoo Kang

PLoS ONE, Vol 13, Iss 1, p e

2018 Volume 0190926

Abstract: Detecting drug-drug interactions (DDI) is important because information on DDIs can help prevent adverse effects from drug combinations. Since there are many new DDI-related papers published in the biomedical domain, manually extracting DDI information ... ...

Abstract	Detecting drug-drug interactions (DDI) is important because information on DDIs can help prevent adverse effects from drug combinations. Since there are many new DDI-related papers published in the biomedical domain, manually extracting DDI information from the literature is a laborious task. However, text mining can be used to find DDIs in the biomedical literature. Among the recently developed neural networks, we use a Recursive Neural Network to improve the performance of DDI extraction. Our recursive neural network model uses a position feature, a subtree containment feature, and an ensemble method to improve the performance of DDI extraction. Compared with the state-of-the-art models, the DDI detection and type classifiers of our model performed 4.4% and 2.8% better, respectively, on the DDIExtraction Challenge'13 test data. We also validated our model on the PK DDI corpus that consists of two types of DDIs data: in vivo DDI and in vitro DDI. Compared with the existing model, our detection classifier performed 2.3% and 6.7% better on in vivo and in vitro data respectively. The results of our validation demonstrate that our model can automatically extract DDIs better than existing models.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2018-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: ΔNp63 regulates a common landscape of enhancer associated genes in non-small cell lung cancer

Marco Napoli / Sarah J. Wu / Bethanie L. Gore / Hussein Abbas / Kyubum Lee / Rahul Checker / Shilpa Dhar / Kimal Rajapakshe / Aik Choon Tan / Min Gyu Lee / Cristian Coarfa / Elsa R. Flores

Nature Communications, Vol 13, Iss 1, Pp 1-

2022 Volume 16

Abstract: The mechanistic role regulated by the oncogene ∆Np63 in lung cancer development is currently unclear. Here, the authors show that ΔNp63 is pro-tumorigenic in lung adenocarcinoma as well as squamous cell carcinoma, and maintains lung cancer progenitor ... ...

Abstract	The mechanistic role regulated by the oncogene ∆Np63 in lung cancer development is currently unclear. Here, the authors show that ΔNp63 is pro-tumorigenic in lung adenocarcinoma as well as squamous cell carcinoma, and maintains lung cancer progenitor cells via regulation of super-enhancer-associated genes, including BCL9L
Keywords	Science ; Q
Language	English
Publishing date	2022-02-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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