• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.701-708
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• 2024

The advent of transformer architectures in deep learning has been a major breakthrough in natural language processing research. Object name recognition is a branch of natural language processing and is an important research area for tasks such as information retrieval. It is also important in the biomedical field, but the lack of Korean biomedical corpora for training has limited the development of Korean clinical research using AI. In this study, we built a new biomedical corpus for Korean biomedical entity name recognition and selected language models pre-trained on a large Korean corpus for transfer learning. We compared the name recognition performance of the selected language models by F1-score and the recognition rate by tag, and analyzed the errors. In terms of recognition performance, KlueRoBERTa showed relatively good performance. The error analysis of the tagging process shows that the recognition performance of Disease is excellent, but Body and Treatment are relatively low. This is due to over-segmentation and under-segmentation that fails to properly categorize entity names based on context, and it will be necessary to build a more precise morphological analyzer and a rich lexicon to compensate for the incorrect tagging.

• Genomics & Informatics
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• v.19 no.3
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• pp.23.1-23.7
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• 2021

Currently, coronavirus disease 2019 (COVID-19) literature has been increasing dramatically, and the increased text amount make it possible to perform large scale text mining and knowledge discovery. Therefore, curation of these texts becomes a crucial issue for Bio-medical Natural Language Processing (BioNLP) community, so as to retrieve the important information about the mechanism of COVID-19. PubAnnotation is an aligned annotation system which provides an efficient platform for biological curators to upload their annotations or merge other external annotations. Inspired by the integration among multiple useful COVID-19 annotations, we merged three annotations resources to LitCovid data set, and constructed a cross-annotated corpus, LitCovid-AGAC. This corpus consists of 12 labels including Mutation, Species, Gene, Disease from PubTator, GO, CHEBI from OGER, Var, MPA, CPA, NegReg, PosReg, Reg from AGAC, upon 50,018 COVID-19 abstracts in LitCovid. Contain sufficient abundant information being possible to unveil the hidden knowledge in the pathological mechanism of COVID-19.

• Journal of Ginseng Research
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• v.47 no.5
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• pp.672-680
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• 2023

Background: Korean Red Ginseng (KRG), the steamed root of Panax ginseng, has pharmacological activities for immunological and neurodegenerative disorders. But, the role of KRGE in multiple sclerosis (MS) remains unclear. Purpose: To determine whether KRG extract (KRGE) could inhibit demyelination in corpus callosum (CC) of cuprizone (CPZ)-induced murine model of MS Methods: Male adult mice were fed with a standard chow diet or a chow diet supplemented with 0.2% (w/w) CPZ ad libitum for six weeks to induce demyelination while were simultaneously administered with distilled water (DW) alone or KRGE-DW (0.004%, 0.02 and 0.1% of KRGE) by drinking. Results: Administration with KRGE-DW alleviated demyelination and oligodendrocyte degeneration associated with inhibition of infiltration and activation of resident microglia and monocyte-derived macrophages as well as downregulation of proinflammatory mediators in the CC of CPZ-fed mice. KRGE-DW also attenuated the level of infiltration of Th1 and Th17) cells, in line with inhibited Mrna expression of IFN-γ and IL-17, respectively, in the CC. These positive effects of KRGE-DW mitigated behavioral dysfunction based on elevated plus maze and the rotarod tests. Conclusion: The results strongly suggest that KRGE-DW may inhibit CPZ-induced demyelination due to its oligodendroglial protective and anti-inflammatory activities by inhibiting infiltration/activation of immune cells. Thus, KRGE might have potential in therapeutic intervention for MS.

• Journal of Life Science
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• v.33 no.8
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• pp.663-679
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• 2023

This review analyzes research trends related to new drug development using artificial intelligence from 2010 to 2022. This analysis organized the abstracts of 2,421 studies into a corpus, and words with high frequency and high connection centrality were extracted through preprocessing. The analysis revealed a similar word frequency trend between 2010 and 2019 to that between 2020 and 2022. In terms of the research method, many studies using machine learning were conducted from 2010 to 2020, and since 2021, research using deep learning has been increasing. Through these studies, we investigated the trends in research on artificial intelligence utilization by field and the strengths, problems, and challenges of related research. We found that since 2021, the application of artificial intelligence has been expanding, such as research using artificial intelligence for drug rearrangement, using computers to develop anticancer drugs, and applying artificial intelligence to clinical trials. This article briefly presents the prospects of new drug development research using artificial intelligence. If the reliability and safety of bio and medical data are ensured, and the development of the above artificial intelligence technology continues, it is judged that the direction of new drug development using artificial intelligence will proceed to personalized medicine and precision medicine, so we encourage efforts in that field.