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Effect of fermentation with Monascus pilosus on the antioxidant activities and phenolic acid contents of adzuki bean(Vigna angularis)

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Journal of Coastal Life Medicine 2015年第4期 276-283页

作者： Jinhua Cheng Sung-Kwon Lee Sasikumar Arunachalam Palaniyandi Joo-Won Suh Seung Hwan Yang Center for Nutraceutical and Pharmaceutical Materials Myongji UniversityCheoin-guYonginGyeonggi-Do 449-728Korea Interdisciplinary Program of Biomodulation Myongji UniversityCheoin-guYonginGyeonggi-Do 449-728Korea Division of Bioscience and Bioinformatics College of Natural ScienceMyongji UniversityCheoin-guYonginGyeonggi-Do 449-728Korea

Objective:To enhance physiological activities of adzuki bean(Vigna angularis)via fermentation with Monascus pilosus(***).Methods:The adzuki bean fermentation conditions with *** were optimized,and the effect of Monascus-fermentation on the antioxidant capacity and phenolic acid contents of adzuki bean was ***:Optimal fermentation conditions were determined by the production of monacolin *** highest monacolin K production was observed in 5%inoculum sized on day 15 in *** and bound phenolic acids were isolated from native and fermented adzuki bean.A 1.9-fold decrease was observed in bound p-coumaric acid content,whereas the contents of bound ferulic and sinapic acids were increased by 28-and 1.7-fold,***,the contents of free phenolic acids such as p-coumaric,ferulic,and sinapic acids were increased by 2.6-,5.2-,and 7.2-fold,*** fermentation of adzuki bean by *** enhanced the activities of DPPH■radical scavenging,ferrous ion-chelating,nitric oxide scavenging,and ferric antioxidant reducing activities 2.2-,1.7-,1.2-,and 1.8-fold,***:Results from our study suggest that the contents of p-coumaric,ferulic,and sinapic acids in adzuki bean were highly increased by fermentation with ***,resulting in enhanced various antioxidant activities.

关键词： Adzuki bean Monascus pilosus Fermentation Monacolin K Phenolic acids Antioxidant activity

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Network-Based Metric Space for Phenotypic Stratification of Samples Using Transcriptome Profiles

Research Square

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Research Square 2021年

作者： Sung, Inyoung Lee, Dohoon Lee, Sangseon Kim, Sun Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea Republic of BK21 FOUR Intelligence Computing Seoul National University Seoul Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of Institute of Engineering Research Seoul National University Seoul Korea Republic of Bioinformatics Institute Seoul National University Seoul Korea Republic of

With the advancements of high-throughput sequencing technology, several recent studies addressed the clinical/phenotypic stratification of samples by utilizing transcriptome data. However, existing stratification methods lack efficient utilization of gene interaction information, and furthermore, handling more than 20,000 genes causes the curse of high dimensionality that hinders elucidating the linkage between genetic profiles and clinical/phenotypic differences. To overcome these challenges, we propose a network-based two-step computational framework. We first reduce dimensions of transcriptome to a few tens of dimensions by mapping transcriptome to protein interaction network followed by performing network propagation algorithm and clustering analysis. Then, each network is converted into a single numeric metric by utilizing information theoretic quantification of gene expression abnormality, which results in a single sample mapping to a metric space generated by each subnetwork in the form of vectors. The proposed network-based stratification method was used to analyses Pan-Caner dataset and Oryza sativa dataset. Extensive experiments showed that our method generates a metric space that captures data-specific biological functions and improves the stratification performance compared to existing methods. Therefore, the proposed method successfully stratified the samples, addressing the problem in the complex gene space. The proposed method is implemented in Python and available at https://***/Sunginyoung/net_stratification. © 2021, CC BY.

关键词： Gene expression

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Bag of Tricks for Electrocardiogram Classification With Deep Neural Networks

Bag of Tricks for Electrocardiogram Classification With Deep...

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Computers in Cardiology (CinC)

作者： Seonwoo Min Hyun-Soo Choi Hyeongrok Han Minji Seo Jin-Kook Kim Junsang Park Sunghoon Jung Il-Young Oh Byunghan Lee Sungroh Yoon Seoul National University Seoul South Korea T3K SK Telecom Seoul South Korea HUINNO Co. Ltd. Seoul South Korea Division of Cardiology Seoul National University Bundang Hospital Seongnam South Korea Seoul National University of Science and Technology Seoul South Korea Interdisciplinary Program in Bioinformatics Interdisciplinary Program in Artificial Intelligence ASRI INMC Institute of Engineering Research Seoul National University Seoul South Korea

ISBN: (数字)9781728173825

ISBN: (纸本)9781728111056

Recent algorithmic advances in electrocardiogram (ECG) classification are largely contributed to deep learning. However, these methods are still based on a relatively straightforward application of deep neural networks (DNNs), which leaves incredible room for improvement. In this paper, as part of the PhysioNet / Computing in Cardiology Challenge 2020, we developed an 18-layer residual convolutional neural network to classify clinical cardiac abnormalities from 12-lead ECG recordings. We focused on examining a collection of data pre-processing, model architecture, training, and post-training procedure refinements for DNN-based ECG classification. We showed that by combining these refinements, we can improve the classification performance significantly. Our team, DSAIL_SNU, obtained a 0.695 challenge score using 10-fold cross-validation, and a 0.420 challenge score on the full test data, placing us 6th in the official ranking.

关键词： Training Deep learning Neural networks Electrocardiography Data models Cardiology Convolutional neural networks

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Inference of Mixed Graphical Models for Dichotomous Phenotypes using Markov Random Field Model

arXiv

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arXiv 2022年

作者： Park, Jaehyun Won, Sungho Interdisciplinary Program in Bioinformatics College of Natural Sciences Seoul National University Seoul Korea Republic of Department of Public Health Sciences Seoul National University Seoul Korea Republic of Institute of Health and Environment Seoul National University Seoul Korea Republic of RexSoft Corp Seoul Korea Republic of

Summary: In this article, we propose a new method named fused mixed graphical model (FMGM), which can infer network structures for dichotomous phenotypes. We assumed that the interplay of different omics markers is associated with disease status and proposed an FMGM-based method to detect the associated omics marker network difference. The statistical models of the networks were based on a pairwise Markov random field model, and penalty functions were added to minimize the effect of sparseness in the networks. The fast proximal gradient method (PGM) was used to optimize the target function. Method validity was measured using synthetic datasets that simulate power-law network structures, and it was found that FMGM showed superior performance, especially in terms of F1 scores, compared with the previous method inferring the networks sequentially (0.392 and 0.546). FMGM performed better not only in identifying the differences (0.217 and 0.410) but also in identifying the networks (0.492 and 0.572). The proposed method was applied to multi-omics profiles of 6-month-old infants with and without atopic dermatitis (AD), and different correlations were found between the abundance of microbial genes related to carotenoid biosynthesis and RNA degradation according to disease status, suggesting the importance of metabolism related to oxidative stress and microbial RNA *** Codes 92B15 (Primary) 62P10 62H10 62-08 (Secondary) © 2022, CC BY.

关键词： Image segmentation

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A probabilistic model for pathway-guided gene set selection

A probabilistic model for pathway-guided gene set selection

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Inyoung Kim Sangseon Lee Youngkuk Kim Hugh Namkoong Sun Kim Artificial Intelligence Institute Seoul National University Seoul Korea Institute of Computer Technology Seoul National University Seoul Korea Seoul National University Seoul Korea Institute of Engineering Research Seoul National University Seoul Korea Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea

ISBN: (纸本)9781665429825

Breast cancer is classified into five intrinsic subtypes, with differing treatment methods and prognoses. Therefore, accurate identification of subtypes from patient transcriptome data is essential. Many gene signatures, including PAM50, have been developed to classify breast cancer subtypes. However, existing gene selection methods do not utilize biological pathways. Gene signature selection using biological pathways can explain signature genes in terms of biological functions. Thus, we propose a probabilistic model for pathway-guided gene set selection using gene expression data. First, we defined gene and pathway factors based on gene expression and pathway activation levels, and calculated the posterior probability. Second, we adopted the prediction strength to guide gene set selection. Third, the gene set was selected using the posterior probability and prediction strength values. Finally, on evaluating the selected gene set, it was experimentally confirmed that our gene set performed better on classification tasks than the PAM50 gene set, a gene set produced by the XGBoost classifier, and a random gene set. Among the genes selected by our method, it was confirmed that the genes included in the cell cycle and circadian rhythm pathways showed different expression patterns for each breast cancer subtype. Our selected gene set exhibited biological significance in terms of pathway activation.

关键词： Biological system modeling Conferences Probability Probabilistic logic Breast cancer Data models Circadian rhythm

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Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification

arXiv

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arXiv 2021年

作者： Piao, Yinhua Lee, Sangseon Lee, Dohoon Kim, Sun Department of Computer Science and Engineering Seoul National University Korea Republic of Institute of Computer Technology Seoul National University Korea Republic of Bioinformatics Institute Seoul National University Korea Republic of AIGENDRUG Co. Ltd Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

Recently, graph neural networks (GNNs) have been widely used for document classification. However, most existing methods are based on static word co-occurrence graphs without sentence-level information, which poses three challenges:(1) word ambiguity, (2) word synonymity, and (3) dynamic contextual dependency. To address these challenges, we propose a novel GNN-based sparse structure learning model for inductive document classification. Specifically, a document-level graph is initially generated by a disjoint union of sentence-level word co-occurrence graphs. Our model collects a set of trainable edges connecting disjoint words between sentences, and employs structure learning to sparsely select edges with dynamic contextual dependencies. Graphs with sparse structures can jointly exploit local and global contextual information in documents through GNNs. For inductive learning, the refined document graph is further fed into a general readout function for graph-level classification and optimization in an end-to-end manner. Extensive experiments on several real-world datasets demonstrate that the proposed model outperforms most state-of-the-art results, and reveal the necessity to learn sparse structures for each document. © 2021, CC BY.

关键词： Graph neural networks

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OrthoANI: an improved algorithm and software for genome-based species definition of Bacteria and Archaea

OrthoANI: an improved algorithm and software for genome-base...

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第七届全国微生物资源学术暨国际微生物系统与分类学研讨会

作者： Imchang Lee YeongOuk Kim Sang-Cheol Park Jongsik Chun Interdisciplinary Program in Bioinformatics Seoui National UniversitySeoul 151-742Republic of Korea Interdisciplinary Program in Bioinformatics Seoui National UniversitySeoul 151-742Republic of Korea School of Biological SciencesSeoui National UniversitySeoul 151-742Republic of Korea Institute of Molecular Biology & Genetics Seoui National University Seoul 151-742Republic of Korea Institute of Molecular Biology & Genetics Seoui National UniversitySeoul 151-742Republic of Korea School of Biological Sciences Seoui National UniversitySeoul 151-742Republic of Korea Institute of Molecular Biology & Genetics Seoui National University Seoul 151-742Republic of Korea

In genome era,average nucleotide identity(ANI)is the "gold standard" of prokaryotic taxonomy to delineation of *** is a mean of nucleotide identity between conserved regions shared by two *** introduction of a DNA-DNA hybridization(DDH)value of 70%as the threshold for the microbial species boundary,95-96%of ANI values which correlate well with the thresholdis a robust measure of evolutionary *** is an issue,however,which difference between identities of query genome to reference genomeeould be a serious problem in the calculation of the mean *** average nucleotide identities of 63,690 genome pairs with 14,745 strains in 10 big *** study found over 55%of total samples got more than 0.1%difference,and up to 1,101 genome pairs got difference not less than 1%.We develop a new method that similar with ANI algorithm to calculate identity between sets of truly orthologous sequences shared by two microbial *** our study,we introduce modified ANI algorithm as name of *** identity of nucleotide calculated via new algorithm hadone ANI value through tested version that had a 0.00042%average discrepancy between bidirectional calculations.

关键词： ANI Average Nucleotide Identity Taxonomy OrthoANI

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Computational approach to identify enzymes that are potential therapeutic candidates for psoriasis

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Enzyme Research 2011年第1期2011卷 826784-826784页

作者： Park, Daeui Jeong, Hyoung Oh Kim, Byoung-Chul Ha, Young Mi Young Chung, Hae Interdisciplinary Research Program of Bioinformatics and Longevity Science Pusan National University Kumjeong-Gu Busan 609-735 South Korea Molecular Inflammation Research Center for Aging Intervention (MRCA) College of Pharmacy Pusan National University Kumjeong-Gu Busan 609-735 South Korea

Psoriasis is well known as a chronic inflammatory dermatosis. The disease affects persons of all ages and is a burden worldwide. Psoriasis is associated with various diseases such as arthritis. The disease is characterized by well-demarcated lesions on the skin of the elbows and knees. Various genetic and environmental factors are related to the pathogenesis of psoriasis. In order to identify enzymes that are potential therapeutic targets for psoriasis, we utilized a computational approach, combining microarray analysis and protein interaction prediction. We found 6,437 genes (3,264 upregulated and 3,173 downregulated) that have significant differences in expression between regions with and without lesions in psoriasis patients. We identified potential candidates through protein-protein interaction predictions made using various protein interaction resources. By analyzing the hub protein of the networks with metrics such as degree and centrality, we detected 32 potential therapeutic candidates. After filtering these candidates through the ENZYME nomenclature database, we selected 5 enzymes: DNA helicase (RUVBL2), proteasome endopeptidase complex (PSMA2), nonspecific protein-tyrosine kinase (ZAP70), I-kappa-B kinase (IKBKE), and receptor protein-tyrosine kinase (EGFR). We adopted a computational approach to detect potential therapeutic targets;this approach may become an effective strategy for the discovery of new drug targets for psoriasis. © 2011 Daeui Park et al.

关键词： MICROBIAL enzymes PSORIASIS SKIN diseases PROTEIN-protein interactions PROTEIN microarrays BIOCHEMISTRY

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DNA steganalysis using deep recurrent neural networks

arXiv

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arXiv 2017年

作者： Bae, Ho Lee, Byunghan Kwon, Sunyoung Yoon, Sungroh Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Electrical and Computer Engineering Seoul National University Seoul08826 Electronic and IT Media Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Clova AI Research NAVER Corp. Seongnam13561 ASRI INMC Seoul National University Seoul08826

Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are vari-ous methods that exist in other domains to detect hidden messages in conventional covert channels. However, they have not been applied to DNA steganography. The current most common detection approaches, namely frequency analysis-based methods, often overlook important signals when directly applied to DNA steganography because those methods de-pend on the distribution of the number of sequence characters. To address this limitation, we propose a general sequence learning-based DNA steganalysis framework. The proposed approach learns the intrinsic distribution of coding and non-coding sequences and detects hidden messages by exploiting distribution variations after hiding these messages. Using deep recurrent neural networks (RNNs), our framework identifies the distribution varia-tions by using the classification score to predict whether a sequence is to be a coding or non-coding sequence. We compare our proposed method to various existing methods and biological sequence analysis methods implemented on top of our framework. According to our experimental results, our approach delivers a robust detection performance compared to other tools. Copyright © 2017, The Authors. All rights reserved.

关键词： Steganography

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Topological data analysis can extract subgroups with high incidence rates of Type 2 diabetes

Topological data analysis can extract subgroups with high in...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Hyung Sun Kim Changwoo Yi Yongkang Kim Uhnmee Park Woong Kook Hyuk Kim Taesung Park Interdisciplinary Program in bioinformatics Seoul National University The Research Institute of Basic Sciences College of Natural Sciences Seoul National University Department of Statistics Seoul National University Department of Bioscience and biotechnology University of Suwon Department of Mathematical Sciences and Institute for Mathematical Data Analytics Research Center Seoul National University Department of Mathematical Sciences Seoul National University

ISBN: (纸本)9781538654897;9781538654880

Type 2 diabetes (T2D) is now a rapidly increasing, worldwide scourge, and the identification of genetic contributors is vital. However, current analyses of multiple, disease-contributing factors, and their combined interactions, remains quite difficult, using traditional approaches. Topological data analysis (TDA) shows what shape a data set can have, facilitating clustering analysis, by determining which components are close to each other. Thus, TDA can generate a network, using single-nucleotide polymorphism (SNP) data, revealing the genetic relatedness of specific individuals, and can derive multiple ordered subgroups, from one with a low patient concentration, to one with a high patient concentration. Since it is widely accepted that T2D pathogenesis is affected by multiple genetic factors, we performed TDA on T2D data from the Korea Association REsource (KARE) project, a population-based, genome-wide association study of the Korean adult population. Since KARE data contains follow-up information about the incidence of T2D, we compared the T2D status of each individual, at baseline, with that of 10 years later. For the TDA network-driven subgroups, ordered by prevalence, we compared the T2D incidence rate, after 10 years, for individuals initially without T2D. As a result, we found that the TDA network-driven, ordered subgroups had significantly increased incidence rates, linearly correlated with prevalence (p-value = 0.006914). Our results demonstrate the usefulness of TDA in both identifying genetic contributors (e.g., SNPs), and their interrelationships, in the pathology of complex diseases.

关键词： Data analysis Genetics bioinformatics Diabetes Diseases Conferences Data mining

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