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Article ; Online: Computational Discovery of Cancer Immunotherapy Targets by Intercellular CRISPR Screens.

Yim, Soorin / Hwang, Woochang / Han, Namshik / Lee, Doheon

2022 Volume 13, Page(s) 884561

Abstract: Cancer immunotherapy targets the interplay between immune and cancer cells. In particular, interactions between cytotoxic T lymphocytes (CTLs) and cancer cells, such as PD-1 ( ...

Abstract	Cancer immunotherapy targets the interplay between immune and cancer cells. In particular, interactions between cytotoxic T lymphocytes (CTLs) and cancer cells, such as PD-1 (
MeSH term(s)	CRISPR-Cas Systems ; Clustered Regularly Interspaced Short Palindromic Repeats/genetics ; Humans ; Immunotherapy ; T-Lymphocytes, Cytotoxic ; Triple Negative Breast Neoplasms/genetics
Language	English
Publishing date	2022-05-16
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2022.884561
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Epigenetic modification of gene expression in cancer cells by terahertz demethylation.

Cheon, Hwayeong / Hur, Junho K / Hwang, Woochang / Yang, Hee-Jin / Son, Joo-Hiuk

Scientific reports

2023 Volume 13, Issue 1, Page(s) 4930

Abstract: Terahertz (THz) radiation can affect the degree of DNA methylation, the spectral characteristics of which exist in the terahertz region. DNA methylation is an epigenetic modification in which a methyl ( ... ...

Abstract	Terahertz (THz) radiation can affect the degree of DNA methylation, the spectral characteristics of which exist in the terahertz region. DNA methylation is an epigenetic modification in which a methyl (CH
MeSH term(s)	Humans ; Epigenesis, Genetic ; DNA Methylation ; Demethylation ; Melanoma ; Gene Expression ; Terahertz Radiation
Language	English
Publishing date	2023-03-26
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/s41598-023-31828-w
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Identification of potential pan-coronavirus therapies using a computational drug repurposing platform.

Hwang, Woochang / Han, Namshik

Methods (San Diego, Calif.)

2021 Volume 203, Page(s) 214–225

Abstract: In the past 20 years, there have been several infectious disease outbreaks in humans for which the causative agent has been a zoonotic coronavirus. Novel infectious disease outbreaks, as illustrated by the current coronavirus disease 2019 (COVID-19) ... ...

Abstract	In the past 20 years, there have been several infectious disease outbreaks in humans for which the causative agent has been a zoonotic coronavirus. Novel infectious disease outbreaks, as illustrated by the current coronavirus disease 2019 (COVID-19) pandemic, demand a rapid response in terms of identifying effective treatments for seriously ill patients. The repurposing of approved drugs from other therapeutic areas is one of the most practical routes through which to approach this. Here, we present a systematic network-based drug repurposing methodology, which interrogates virus-human, human protein-protein and drug-protein interactome data. We identified 196 approved drugs that are appropriate for repurposing against COVID-19 and 102 approved drugs against a related coronavirus, severe acute respiratory syndrome (SARS-CoV). We constructed a protein-protein interaction (PPI) network based on disease signatures from COVID-19 and SARS multi-omics datasets. Analysis of this PPI network uncovered key pathways. Of the 196 drugs predicted to target COVID-19 related pathways, 44 (hypergeometric p-value: 1.98e-04) are already in COVID-19 clinical trials, demonstrating the validity of our approach. Using an artificial neural network, we provide information on the mechanism of action and therapeutic value for each of the identified drugs, to facilitate their rapid repurposing into clinical trials.
MeSH term(s)	Antiviral Agents/pharmacology ; Antiviral Agents/therapeutic use ; COVID-19/drug therapy ; Drug Repositioning/methods ; Humans ; Pandemics ; SARS-CoV-2
Chemical Substances	Antiviral Agents
Language	English
Publishing date	2021-11-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1066584-5
ISSN	1095-9130 ; 1046-2023
ISSN (online)	1095-9130
ISSN	1046-2023
DOI	10.1016/j.ymeth.2021.11.002
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Article: Identification of potential pan-coronavirus therapies using a computational drug repurposing platform

Hwang, Woochang / Han, Namshik

Methods. 2021 Nov. 03,

2021

Abstract	In the past 20 years, there have been several infectious disease outbreaks in humans for which the causative agent has been a zoonotic coronavirus. Novel infectious disease outbreaks, as illustrated by the current coronavirus disease 2019 (COVID-19) pandemic, demand a rapid response in terms of identifying effective treatments for seriously ill patients. The repurposing of approved drugs from other therapeutic areas is one of the most practical routes through which to approach this. Here, we present a systematic network-based drug repurposing methodology, which interrogates virus–human, human protein–protein and drug–protein interactome data. We identified 196 approved drugs that are appropriate for repurposing against COVID-19 and 102 approved drugs against a related coronavirus, severe acute respiratory syndrome (SARS-CoV). We constructed a protein–protein interaction (PPI) network based on disease signatures from COVID-19 and SARS multi-omics datasets. Analysis of this PPI network uncovered key pathways. Of the 196 drugs predicted to target COVID-19 related pathways, 44 (hypergeometric p-value: 1.98e−04) are already in COVID-19 clinical trials, demonstrating the validity of our approach. Using an artificial neural network, we provide information on the mechanism of action and therapeutic value for each of the identified drugs, to facilitate their rapid repurposing into clinical trials.
Keywords	COVID-19 infection ; Orthocoronavirinae ; data collection ; drugs ; etiological agents ; humans ; mechanism of action ; multiomics ; neural networks ; pandemic ; protein-protein interactions ; therapeutics
Language	English
Dates of publication	2021-1103
Publishing place	Elsevier Inc.
Document type	Article
Note	Pre-press version
ZDB-ID	1066584-5
ISSN	1095-9130 ; 1046-2023
ISSN (online)	1095-9130
ISSN	1046-2023
DOI	10.1016/j.ymeth.2021.11.002
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Epigenetic modification of gene expression in cancer cells by terahertz demethylation

Hwayeong Cheon / Junho K. Hur / Woochang Hwang / Hee-Jin Yang / Joo-Hiuk Son

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

2023 Volume 12

Abstract: Abstract Terahertz (THz) radiation can affect the degree of DNA methylation, the spectral characteristics of which exist in the terahertz region. DNA methylation is an epigenetic modification in which a methyl (CH3) group is attached to cytosine, a ... ...

Abstract	Abstract Terahertz (THz) radiation can affect the degree of DNA methylation, the spectral characteristics of which exist in the terahertz region. DNA methylation is an epigenetic modification in which a methyl (CH3) group is attached to cytosine, a nucleobase in human DNA. Appropriately controlled DNA methylation leads to proper regulation of gene expression. However, abnormal gene expression that departs from controlled genetic transcription through aberrant DNA methylation may occur in cancer or other diseases. In this study, we demonstrate the modification of gene expression in cells by THz demethylation using resonant THz radiation. Using an enzyme-linked immunosorbent assay, we observed changes in the degree of global DNA methylation in the SK-MEL-3 melanoma cell line under irradiation with 1.6-THz radiation with limited spectral bandwidth. Resonant THz radiation demethylated living melanoma cells by 19%, with no significant occurrence of apurinic/apyrimidinic sites, and the demethylation ratio was linearly proportional to the power of THz radiation. THz demethylation downregulates FOS, JUN, and CXCL8 genes, which are involved in cancer and apoptosis pathways. Our results show that THz demethylation has the potential to be a gene expression modifier with promising applications in cancer treatment.
Keywords	Medicine ; R ; Science ; Q
Subject code	570 ; 612
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Concept embedding to measure semantic relatedness for biomedical information ontologies.

Park, Junseok / Kim, Kwangmin / Hwang, Woochang / Lee, Doheon

Journal of biomedical informatics

2019 Volume 94, Page(s) 103182

Abstract: There have been many attempts to identify relationships among concepts corresponding to terms from biomedical information ontologies such as the Unified Medical Language System (UMLS). In particular, vector representation of such concepts using ... ...

Abstract	There have been many attempts to identify relationships among concepts corresponding to terms from biomedical information ontologies such as the Unified Medical Language System (UMLS). In particular, vector representation of such concepts using information from UMLS definition texts is widely used to measure the relatedness between two biological concepts. However, conventional relatedness measures have a limited range of applicable word coverage, which limits the performance of these models. In this paper, we propose a concept-embedding model of a UMLS semantic relatedness measure to overcome the limitations of earlier models. We obtained context texts of biological concepts that are not defined in UMLS by utilizing Wikipedia as an external knowledgebase. Concept vector representations were then derived from the context texts of the biological concepts. The degree of relatedness between two concepts was defined as the cosine similarity between corresponding concept vectors. As a result, we validated that our method provides higher coverage and better performance than the conventional method.
MeSH term(s)	Biological Ontologies ; Humans ; Natural Language Processing ; Semantics ; Unified Medical Language System
Language	English
Publishing date	2019-04-19
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2019.103182
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Article ; Online: Current and prospective computational approaches and challenges for developing COVID-19 vaccines.

Hwang, Woochang / Lei, Winnie / Katritsis, Nicholas M / MacMahon, Méabh / Chapman, Kathryn / Han, Namshik

Advanced drug delivery reviews

2021 Volume 172, Page(s) 249–274

Abstract: SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods ... ...

Abstract	SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.
MeSH term(s)	Animals ; B-Lymphocytes/drug effects ; B-Lymphocytes/immunology ; COVID-19/genetics ; COVID-19/immunology ; COVID-19/prevention & control ; COVID-19 Vaccines/administration & dosage ; COVID-19 Vaccines/genetics ; COVID-19 Vaccines/immunology ; Computational Biology/methods ; Computational Biology/trends ; Drug Development/methods ; Drug Development/trends ; Epitopes/genetics ; Epitopes/immunology ; Gene Expression Profiling/methods ; Gene Expression Profiling/trends ; Humans ; SARS-CoV-2/drug effects ; SARS-CoV-2/genetics ; SARS-CoV-2/metabolism
Chemical Substances	COVID-19 Vaccines ; Epitopes
Language	English
Publishing date	2021-02-06
Publishing country	Netherlands
Document type	Journal Article ; Review
ZDB-ID	639113-8
ISSN	1872-8294 ; 0169-409X
ISSN (online)	1872-8294
ISSN	0169-409X
DOI	10.1016/j.addr.2021.02.004
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: DILI

Rathee, Sanjay / MacMahon, Meabh / Liu, Anika / Katritsis, Nicholas M / Youssef, Gehad / Hwang, Woochang / Wollman, Lilly / Han, Namshik

Frontiers in genetics

2022 Volume 13, Page(s) 867946

Abstract: Drug-induced liver injury (DILI) is a class of adverse drug reactions (ADR) that causes problems in both clinical and research settings. It is the most frequent cause of acute liver failure in the majority of Western countries and is a major cause of ... ...

Abstract	Drug-induced liver injury (DILI) is a class of adverse drug reactions (ADR) that causes problems in both clinical and research settings. It is the most frequent cause of acute liver failure in the majority of Western countries and is a major cause of attrition of novel drug candidates. Manual trawling of the literature is the main route of deriving information on DILI from research studies. This makes it an inefficient process prone to human error. Therefore, an automatized AI model capable of retrieving DILI-related articles from the huge ocean of literature could be invaluable for the drug discovery community. In this study, we built an artificial intelligence (AI) model combining the power of natural language processing (NLP) and machine learning (ML) to address this problem. This model uses NLP to filter out meaningless text (e.g., stop words) and uses customized functions to extract relevant keywords such as singleton, pair, and triplet. These keywords are processed by an apriori pattern mining algorithm to extract relevant patterns which are used to estimate initial weightings for a ML classifier. Along with pattern importance and frequency, an FDA-approved drug list mentioning DILI adds extra confidence in classification. The combined power of these methods builds a DILI classifier (DILI
Language	English
Publishing date	2022-06-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2022.867946
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: dialogi: Utilising NLP With Chemical and Disease Similarities to Drive the Identification of Drug-Induced Liver Injury Literature.

Katritsis, Nicholas M / Liu, Anika / Youssef, Gehad / Rathee, Sanjay / MacMahon, Méabh / Hwang, Woochang / Wollman, Lilly / Han, Namshik

Frontiers in genetics

2022 Volume 13, Page(s) 894209

Abstract: Drug-Induced Liver Injury (DILI), despite its low occurrence rate, can cause severe side effects or even lead to death. Thus, it is one of the leading causes for terminating the development of new, and restricting the use of already-circulating, drugs. ... ...

Abstract	Drug-Induced Liver Injury (DILI), despite its low occurrence rate, can cause severe side effects or even lead to death. Thus, it is one of the leading causes for terminating the development of new, and restricting the use of already-circulating, drugs. Moreover, its multifactorial nature, combined with a clinical presentation that often mimics other liver diseases, complicate the identification of DILI-related (or "positive") literature, which remains the main medium for sourcing results from the clinical practice and experimental studies. This work-contributing to the "Literature AI for DILI Challenge" of the Critical Assessment of Massive Data Analysis (CAMDA) 2021- presents an automated pipeline for distinguishing between DILI-positive and negative publications. We used Natural Language Processing (NLP) to filter out the uninformative parts of a text, and identify and extract mentions of chemicals and diseases. We combined that information with small-molecule and disease embeddings, which are capable of capturing chemical and disease similarities, to improve classification performance. The former were directly sourced from the Chemical Checker (CC). For the latter, we collected data that encode different aspects of disease similarity from the National Library of Medicine's (NLM) Medical Subject Headings (MeSH) thesaurus and the Comparative Toxicogenomics Database (CTD). Following a similar procedure as the one used in the CC, vector representations for diseases were learnt and evaluated. Two Neural Network (NN) classifiers were developed: a baseline model that accepts texts as input and an augmented, extended, model that also utilises chemical and disease embeddings. We trained, validated, and tested the classifiers through a Nested Cross-Validation (NCV) scheme with 10 outer and 5 inner folds. During this, the baseline and extended models performed virtually identically, with F
Language	English
Publishing date	2022-08-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2022.894209
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Physicians' Agreement on and Implementation of the 2019 European Alliance of Associations for Rheumatology Vaccination Guideline: An International Survey.

Seo, Philip / Winthrop, Kevin / Sawalha, Amr Hakam / Choi, Serim / Hwang, Woochang / Park, Hyun Ah / Lee, Eun Bong / Park, Jin Kyun

Journal of rheumatic diseases

2022 Volume 30, Issue 1, Page(s) 18–25

Abstract: Objective: To evaluate the perspective of healthcare professionals towards the 2019 European Alliance of Associations for Rheumatology (EULAR) vaccination guideline in patients with autoimmune inflammatory rheumatic diseases (AIIRD).: Methods: ... ...

Abstract	Objective: To evaluate the perspective of healthcare professionals towards the 2019 European Alliance of Associations for Rheumatology (EULAR) vaccination guideline in patients with autoimmune inflammatory rheumatic diseases (AIIRD). Methods: Healthcare professionals who care for patients with AIIRD were invited to participate in an online survey regarding their perspective on the 2019 update of the EULAR recommendations for vaccination in adult patients with AIIRD. Level of agreement and implementation of the 6 overarching principles and 9 recommendations were rated on a 5-point Likert scale (1~5). Results: Survey responses of 371 healthcare professionals from Asia (42.2%) and North America (41.6%), Europe (13.8%), and other countries were analyzed. Only 16.3% of participants rated their familiarity with the 2019 EULAR guideline as 5/5 ("very well"). There was a high agreement (≥4/5 rating) with the overarching principles, except for the principles applying to live-attenuated vaccines. There was a high level of agreement with the recommendations regarding influenza and pneumococcal vaccinations; implementation of these recommendations was also high. Participants also reported a high level of agreement with the remaining recommendations but did not routinely implement these recommendations. Conclusion: The 2019 update of EULAR recommendations for the vaccination of adult patients with AIIRD is generally thought to be important by healthcare professionals, although implementation of adequate vaccination is often lacking. Better education of healthcare providers may be important to optimize the vaccination coverage for patients with AIIRD.
Language	English
Publishing date	2022-10-31
Publishing country	Korea (South)
Document type	Journal Article
ISSN	2233-4718
ISSN (online)	2233-4718
DOI	10.4078/jrd.22.0012
Database	MEDical Literature Analysis and Retrieval System OnLINE

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