检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Bag of Tricks for Electrocardiogram Classification With Deep Neural Networks

Bag of Tricks for Electrocardiogram Classification With Deep...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

A probabilistic model for pathway-guided gene set selection

A probabilistic model for pathway-guided gene set selection

引用

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

来源：评论

学校读者我要写书评

暂无评论

Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

Human endogenous retroviruses (HERVs) associated with glioblastoma risk and prognosis

引用

Cancer Gene Therapy 2025年 1-11页

作者： Mazumder, Harun Lin, Hui-Yi Baddoo, Melody Gałan, Wojciech Polania-Villanueva, Diana Hicks, Chindo Otohinoyi, David Peruzzi, Francesca Madeja, Zbigniew Belancio, Victoria P. Flemington, Erik K. Reiss, Krzysztof Rak, Monika Biostatistics Program Louisiana State University Health Sciences Center School of Public Health New Orleans LA United States Louisiana Cancer Research Center New Orleans LA United States Tulane Cancer Center Tulane Health Sciences Center New Orleans LA United States Stanley S. Scott Cancer Center Louisiana State University Health Sciences Center New Orleans LA United States Department of Genetics and the Bioinformatics and Genomics Program Louisiana State University Health Sciences Center School of Medicine New Orleans LA United States Department of Medicine Louisiana State University Health Sciences Center School of Medicine New Orleans LA United States Department of Cell Biology Jagiellonian University in Kraków Faculty of Biochemistry Biophysics and Biotechnology Kraków Poland Department of Structural and Cellular Biology Tulane University School of Medicine New Orleans LA United States Department of Pathology and Laboratory Medicine Tulane University School of Medicine New Orleans LA United States Department of Interdisciplinary Oncology Louisiana State University Health Sciences Center School of Medicine New Orleans LA United States

Emerging evidence suggests expression from human endogenous retrovirus (HERV) loci likely contributes to, or is a biomarker of, glioblastoma multiforme (GBM) disease progression. However, the relationship between HERV expression and GBM malignant phenotype is unclear. Applying several in silico analyses based on data from The Cancer Genome Atlas (TCGA), we derived a locus-specific HERV transcriptome for glioma that revealed 211 HERVs significantly dysregulated in the comparisons of GBM vs. normal brain (NB), GBM vs. low-grade glioma (LGG), and LGG vs. NB. Our analysis supported development of a unique HERV scoring algorithm that segregated GBM, LGG, and NB. Interestingly, lower HERV scores showed correlation with lower survival in GBM. However, HERV scores were less robust in predicting LGG survival or LGG progression to GBM. Functional prediction analysis linked the 211 HERV loci with 18 voltage-gated potassium channel genes. The functional link between dysregulated HERVs and specific potassium channel genes may contribute to better understanding of GBM pathogenesis, disease progression, and possibly drug resistance. © The Author(s) 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Post-COVID-19 Effects on Chronic Gastritis and Gastric Cellular and Molecular Characteristics in Male Mice

引用

Cellular and Molecular Gastroenterology and Hepatology 2025年第8期19卷 101511-101511页

作者： Jeong, Haengdueng Kim, Sung-Hee Kim, Jiseon Jeon, Donghun Uhm, Chanyang Oh, Heeju Cho, Kyungrae Park, In Ho Oh, Jooyeon Kim, Jeong Jin Jeong, Sang-Ho Park, Ji-Ho Park, Jun Won Yun, Jun-Won Seo, Jun-Young Shin, Jeon-Soo Goldenring, James R. Seong, Je Kyung Nam, Ki Taek Department of Biomedical Sciences Brain Korea 21 PLUS Project for Medical Science Yonsei University College of Medicine Seoul South Korea Epithelial Biology Center and Department of Surgery Vanderbilt University School of Medicine Nashville TN United States Institute of Immunology and Immunological Diseases Yonsei University College of Medicine Seoul South Korea Department of Microbiology Yonsei University College of Medicine Seoul South Korea Department of Surgery Gyongsang National University Hospital Jinju South Korea Korea Mouse Phenotyping Center Seoul National University Seoul South Korea Laboratory of Developmental Biology and Genomics Research Institute for Veterinary Science Brain Korea 21 PLUS Program for Creative Veterinary Science Research College of Veterinary Medicine Seoul National University Seoul South Korea Laboratory Animal Medicine College of Veterinary Medicine Seoul National University Seoul South Korea Laboratory of Veterinary Toxicology College of Veterinary Medicine Seoul National University Seoul South Korea BIO-MAX Institute Seoul National University Seoul South Korea Interdisciplinary Program for Bioinformatics Seoul National University Seoul South Korea

Backgrounds & Aims: Since the Omicron variant emerged as a major severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, COVID-19-associated mortality has decreased remarkably. Nevertheless, patients with a history of SARS-CoV-2 infection have been suffering from an aftereffect commonly known as ‘long COVID,’ affecting diverse organs. However, the effect of SARS-CoV-2 on gastric cells and disease progression was not previously known. We aimed to investigate whether SARS-CoV-2 infection affects stomach cells and if post-COVID-19 conditions can lead to severe gastric disease. Methods: Stomach specimens obtained from male K18-hACE2 mice 7 days after SARS-CoV-2 infection were subjected to a transcriptomic analysis for molecular profiling. To investigate the putative role of SARS-CoV-2 in gastric carcinogenesis, K18-hACE2 mice affected by nonlethal COVID-19 were also inoculated with Helicobacter pylori SS1. Results: Despite the lack of viral dissemination and pathologic traits in the stomach, SARS-CoV-2 infection caused dramatic changes to the molecular profile and some immune subsets in this organ. Notably, the gene sets related to metaplasia and gastric cancer were significantly enriched after viral infection. As a result, chronic inflammatory responses and preneoplastic transitions were promoted in these mice. Conclusion: SARS-CoV-2 infection indirectly leads to profound and post-acute COVID-19 alterations in the stomach at the cellular and molecular levels, resulting in adverse outcomes following co-infection with SARS-CoV-2 and H pylori. Our results show that 2 prevalent pathogens of humans elicit a negative synergistic effect and provide evidence of the risk of severe chronic gastritis in the post-COVID-19 era. © 2025 The Authors

关键词： Chronic Gastritis Helicobacter pylori Post-acute COVID-19 Syndrome Virus and Diseases

来源：评论

学校读者我要写书评

暂无评论

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

引用

Enzyme Research 2011年第1期2011卷 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：

来源：评论

学校读者我要写书评

暂无评论

DNA steganalysis using deep recurrent neural networks

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：