@article{lee2024enhancing,

title = {Enhancing intestinal health with germinated oats: Bioinformatics and compound profiling insights into a novel approach for managing inflammatory bowel disease},

author = {Hyeon Jae Lee and Kyeong Jin Kim and Soo-yeon Park and Kwanyong Choi and Jaeho Pyee and Sunyong Yoo and Ji Yeon Kim},

url = {https://www.sciencedirect.com/science/article/pii/S221242922401263X},

doi = {10.1016/j.fbio.2024.104833},

year  = {2024},

date = {2024-10-01},

urldate = {2024-10-01},

journal = {Food Bioscience},

volume = {61},

pages = {104833},

publisher = {Elsevier},

abstract = {Oats are widely recognized for their numerous health benefits, particularly regarding their anti-inflammatory properties. However, research exploring their specific effects on intestinal permeability and tight junction (TJ) integrity in the context of inflammatory bowel disease (IBD) has been limited. This study aimed to investigate the therapeutic efficacy of germinated oat extract (GOE) in managing IBD, a condition marked by persistent gastrointestinal inflammation and increasing global prevalence. The identified compounds were used to predict target biomarkers and mechanisms related to IBD via bioinformatics analysis and validated using in vitro models. In this study, we used network biology and chemical informatics approaches to predict target biomarkers and their molecular mechanisms. The predicted biomarkers were validated for their effectiveness using a cellular model of intestinal inflammation. The effectiveness of treatment with GOE was validated via in vitro studies, which demonstrated significant enhancement in transepithelial electrical resistance (TEER) and a reduction in fluorescein isothiocyanate (FITC) permeability. Analysis of the mRNA expression of IBD-associated biomarkers in Caco-2 cells demonstrated a significant increase in the mRNA levels of TJ proteins, including TJP1, TJP2, occludin, claudin-1 and claudin-3 compared to the inflammatory group. Furthermore, treatment with GOE markedly reduced the mRNA expression levels of proinflammatory cytokines such as TNF-α, IL-6, and CXCL8. The combination of COCONUT and chemical profiling analysis provided insights into the fundamental molecular mechanisms of GOE. These results underscore the potential of systematically using big data-driven network biology to analyze the effect of food components, highlighting GOE as a promising dietary intervention for IBD.},

note = {Correspondence to Ji Yeon Kim},

keywords = {Bioinformatics, Gut permeability, Inflammatory bowel disease, Network analysis},

pubstate = {published},

tppubtype = {article}

}

@article{kim2021network,

title = {Network analysis to identify the risk of epidemic spreading},

author = {Kiseong Kim and Sunyong Yoo and Sangyeon Lee and Doheon Lee and Kwang-Hyung Lee},

url = {https://www.mdpi.com/2076-3417/11/7/2997},

doi = {10.3390/app11072997},

year  = {2021},

date = {2021-03-26},

urldate = {2021-03-26},

journal = {Applied Sciences},

volume = {11},

number = {7},

pages = {2997},

publisher = {MDPI},

abstract = {Several epidemics, such as the Black Death and the Spanish flu, have threatened human life throughout history; however, it is unclear if humans will remain safe from the sudden and fast spread of epidemic diseases. Moreover, the transmission characteristics of epidemics remain undiscovered. In this study, we present the results of an epidemic simulation experiment revealing the relationship between epidemic parameters and pandemic risk. To analyze the time-dependent risk and impact of epidemics, we considered two parameters for infectious diseases: the recovery time from infection and the transmission rate of the disease. Based on the epidemic simulation, we identified two important aspects of human safety with regard to the threat of a pandemic. First, humans should be safe if the fatality rate is below 100%. Second, even when the fatality rate is 100%, humans would be safe if the average degree of human social networks is below a threshold value. Nevertheless, certain diseases can potentially infect all nodes in the human social networks, and these diseases cause a pandemic when the average degree is larger than the threshold value. These results indicated that certain infectious diseases lead to human extinction and can be prevented by minimizing human contact.},

keywords = {Disease spread, Epidemic disease, Network analysis, Pandemic},

pubstate = {published},

tppubtype = {article}

}

@article{jung2021precise,

title = {Precise Characterization of Genetic Interactions in Cancer via Molecular Network Refining Processes},

author = {Jinmyung Jung and Yongdeuk Hwang and Hongryul Ahn and Sunjae Lee and Sunyong Yoo},

url = {https://www.mdpi.com/1422-0067/22/20/11114},

doi = {10.3390/ijms222011114},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {International journal of molecular sciences},

volume = {22},

number = {20},

pages = {11114},

publisher = {MDPI},

abstract = {Genetic interactions (GIs), such as the synthetic lethal interaction, are promising therapeutic targets in precision medicine. However, despite extensive efforts to characterize GIs by large-scale perturbation screening, considerable false positives have been reported in multiple studies. We propose a new computational approach for improved precision in GI identification by applying constraints that consider actual biological phenomena. In this study, GIs were characterized by assessing mutation, loss of function, and expression profiles in the DEPMAP database. The expression profiles were used to exclude loss-of-function data for nonexpressed genes in GI characterization. More importantly, the characterized GIs were refined based on Kyoto Encyclopedia of Genes and Genomes (KEGG) or protein–protein interaction (PPI) networks, under the assumption that genes genetically interacting with a certain mutated gene are adjacent in the networks. As a result, the initial GIs characterized with CRISPR and RNAi screenings were refined to 65 and 23 GIs based on KEGG networks and to 183 and 142 GIs based on PPI networks. The evaluation of refined GIs showed improved precision with respect to known synthetic lethal interactions. The refining process also yielded a synthetic partner network (SPN) for each mutated gene, which provides insight into therapeutic strategies for the mutated genes; specifically, exploring the SPN of mutated BRAF revealed ELAVL1 as a potential target for treating BRAF-mutated cancer, as validated by previous research. We expect that this work will advance cancer therapeutic research.},

keywords = {Cancer therapeutics, Genetic interaction, Network analysis, Refining process},

pubstate = {published},

tppubtype = {article}

}

@article{10.3389/fphar.2020.584875,

title = {A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds},

author = {Sunyong Yoo and Hyung Chae Yang and Seongyeong Lee and Jaewook Shin and Seyoung Min and Eunjoo Lee and Minkeun Song and Doheon Lee},

url = {https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2020.584875},

doi = {10.3389/fphar.2020.584875},

issn = {1663-9812},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Frontiers in Pharmacology},

volume = {11},

pages = {584875},

abstract = {Medicinal plants and their extracts have been used as important sources for drug discovery. In particular, plant-derived natural compounds, including phytochemicals, antioxidants, vitamins, and minerals, are gaining attention as they promote health and prevent disease. Although several in vitro methods have been developed to confirm the biological activities of natural compounds, there is still considerable room to reduce time and cost. To overcome these limitations, several in silico methods have been proposed for conducting large-scale analysis, but they are still limited in terms of dealing with incomplete and heterogeneous natural compound data. Here, we propose a deep learning-based approach to identify the medicinal uses of natural compounds by exploiting massive and heterogeneous drug and natural compound data. The rationale behind this approach is that deep learning can effectively utilize heterogeneous features to alleviate incomplete information. Based on latent knowledge, molecular interactions, and chemical property features, we generated 686 dimensional features for 4,507 natural compounds and 2,882 approved and investigational drugs. The deep learning model was trained using the generated features and verified drug indication information. When the features of natural compounds were applied as input to the trained model, potential efficacies were successfully predicted with high accuracy, sensitivity, and specificity.},

keywords = {Bioinformatics, Chemical property, Deep learning, Molecular interaction, Natural product, Network analysis, Text mining},

pubstate = {published},

tppubtype = {article}

}

@article{yoo2018discovering,

title = {Discovering health benefits of phytochemicals with integrated analysis of the molecular network, chemical properties and ethnopharmacological evidence},

author = {Sunyong Yoo and Kwansoo Kim and Hojung Nam and Doheon Lee},

url = {https://www.mdpi.com/2072-6643/10/8/1042},

doi = {10.3390/nu10081042},

year  = {2018},

date = {2018-08-08},

urldate = {2018-08-08},

journal = {Nutrients},

volume = {10},

number = {8},

pages = {1042},

publisher = {MDPI},

abstract = {Identifying the health benefits of phytochemicals is an essential step in drug and functional food development. While many in vitro screening methods have been developed to identify the health effects of phytochemicals, there is still room for improvement because of high cost and low productivity. Therefore, researchers have alternatively proposed in silico methods, primarily based on three types of approaches; utilizing molecular, chemical or ethnopharmacological information. Although each approach has its own strength in analyzing the characteristics of phytochemicals, previous studies have not considered them all together. Here, we apply an integrated in silico analysis to identify the potential health benefits of phytochemicals based on molecular analysis and chemical properties as well as ethnopharmacological evidence. From the molecular analysis, we found an average of 415.6 health effects for 591 phytochemicals. We further investigated ethnopharmacological evidence of phytochemicals and found that on average 129.1 (31%) of the predicted health effects had ethnopharmacological evidence. Lastly, we investigated chemical properties to confirm whether they are orally bio-available, drug available or effective on certain tissues. The evaluation results indicate that the health effects can be predicted more accurately by cooperatively considering the molecular analysis, chemical properties and ethnopharmacological evidence.},

keywords = {Bioinformatics, Chemical property, Ethnopharmacology, Herbal medicine, Molecular analysis, Network analysis, Phytochemical},

pubstate = {published},

tppubtype = {article}

}

@article{yoo2018phenotype,

title = {Phenotype-oriented network analysis for discovering pharmacological effects of natural compounds},

author = {Sunyong Yoo and Hojung Nam and Doheon Lee},

url = {https://www.nature.com/articles/s41598-018-30138-w},

doi = {10.1038/s41598-018-30138-w},

year  = {2018},

date = {2018-08-03},

urldate = {2018-08-03},

journal = {Scientific Reports},

volume = {8},

number = {1},

pages = {11667},

publisher = {Nature Publishing Group UK London},

abstract = {Although natural compounds have provided a wealth of leads and clues in drug development, the process of identifying their pharmacological effects is still a challenging task. Over the last decade, many in vitro screening methods have been developed to identify the pharmacological effects of natural compounds, but they are still costly processes with low productivity. Therefore, in silico methods, primarily based on molecular information, have been proposed. However, large-scale analysis is rarely considered, since many natural compounds do not have molecular structure and target protein information. Empirical knowledge of medicinal plants can be used as a key resource to solve the problem, but this information is not fully exploited and is used only as a preliminary tool for selecting plants for specific diseases. Here, we introduce a novel method to identify pharmacological effects of natural compounds from herbal medicine based on phenotype-oriented network analysis. In this study, medicinal plants with similar efficacy were clustered by investigating hierarchical relationships between the known efficacy of plants and 5,021 phenotypes in the phenotypic network. We then discovered significantly enriched natural compounds in each plant cluster and mapped the averaged pharmacological effects of the plant cluster to the natural compounds. This approach allows us to predict unexpected effects of natural compounds that have not been found by molecular analysis. When applied to verified medicinal compounds, our method successfully identified their pharmacological effects with high specificity and sensitivity.},

keywords = {Ethnopharmacology, Natural product, Network analysis},

pubstate = {published},

tppubtype = {article}

}

@article{yoo2018silico,

title = {In silico profiling of systemic effects of drugs to predict unexpected interactions},

author = {Sunyong Yoo and Kyungrin Noh and Moonshik Shin and Junseok Park and Kwang-Hyung Lee and Hojung Nam and Doheon Lee},

url = {https://www.nature.com/articles/s41598-018-19614-5},

doi = {10.1038/s41598-018-19614-5},

year  = {2018},

date = {2018-01-25},

urldate = {2018-01-25},

journal = {Scientific Reports},

volume = {8},

number = {1},

pages = {1612},

publisher = {Nature Publishing Group UK London},

abstract = {Identifying unexpected drug interactions is an essential step in drug development. Most studies focus on predicting whether a drug pair interacts or is effective on a certain disease without considering the mechanism of action (MoA). Here, we introduce a novel method to infer effects and interactions of drug pairs with MoA based on the profiling of systemic effects of drugs. By investigating propagated drug effects from the molecular and phenotypic networks, we constructed profiles of 5,441 approved and investigational drugs for 3,833 phenotypes. Our analysis indicates that highly connected phenotypes between drug profiles represent the potential effects of drug pairs and the drug pairs with strong potential effects are more likely to interact. When applied to drug interactions with verified effects, both therapeutic and adverse effects have been successfully identified with high specificity and sensitivity. Finally, tracing drug interactions in molecular and phenotypic networks allows us to understand the MoA.},

keywords = {ADR, Bioinformatics, Drugs, Network analysis},

pubstate = {published},

tppubtype = {article}

}