检索结果-内蒙古大学图书馆

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： YoungJae Hur Kihyun Lee Jongsik Chun Interdisciplinary Program in Bioinformatics Seoul National University School of Biological SciencesSeoul National University

Universally conserved 16S rRNA gene sequences generated using high-throughput sequencing technique has become powerful tool to analysis the robust diversity and characterizing microbial ***,even full-length 16S rRNA sequence can be obtained from PacBio(R)SMRT sequencer with high yield and *** partial region sequences have been used as sequence tags in microbial community analysis with sequencing bias and absence of taxonomic classification below genus level,the analysis using full-length 16S rRNA is expected to improve the result *** this study,soil metagenome,fecal metagenome and a synthetic mock community DNA were profiled for bacterial 16S with SMRT sequencing using P6/*** 16S rDNA PCR and its representing SMRT sequencing were performed five times *** SMRT cell of full-length 16S rDNA reads analyzed using three different CCS filtering condition,CCS with minimum 6 full passes,minimum 90%,and 99%predicted *** sorting and 12~18kbp length filtering was followed by primer trimming *** checked as error correction and UCHIME from USEARCH program used for detect ***-chimeric CCS reads analyzed for community profiling through in house *** reads accuracy evaluated by number of mismatches and insertion/deletion *** community profile evaluated in classification rate at taxonomic levels and its accuracy,taxon *** soil and fecal data,we were able to sort out non-chimeric sequences based on the reproduction of highly similar sequences from multiple PCR ***,we demonstrate the usefulness of full-length 16S rRNA gene amplicon sequencing in microbial ecology,and suggest the optimal method for generation and analysis of barcoded full-length 16S rDNA sequence data.

关键词： PacBio 16S rDNA Microbial Communities Metagenomes

来源：评论

学校读者我要写书评

暂无评论

Pixelsteganalysis: Destroying hidden information with a low degree of visual degradation

arXiv

引用

arXiv 2019年

作者： Jung, Dahuin Bae, Ho Choi, Hyun-Soo Yoon, Sungroh Electrical and Computer Engineering Seoul National University Interdisciplinary Program in Bioinformatics Seoul National University

Steganography is the science of unnoticeably concealing a secret message within a certain image, called a cover image. The cover image with the secret message is called a stego image. Steganography is commonly used for illegal purposes such as terrorist activities and pornography. To thwart covert communications and transactions, attacking algorithms against steganography, called steganalysis, exist. Currently, there are many studies implementing deep learning to the steganography algorithm. However, conventional steganalysis is no longer effective for deep learning based steganography algorithms. Our framework is the first one to disturb covert communications and transactions via the recent deep learning-based steganography algorithms. We first extract a sophisticated pixel distribution of the potential stego image from the auto-regressive model induced by deep learning. Using the extracted pixel distributions, we detect whether an image is the stego or not at the pixel level. Each pixel value is adjusted as required and the adjustment induces an effective removal of the secret image. Because the decoding method of deep learning-based steganography algorithms is approximate (lossy), which is different from the conventional steganography, we propose a new quantitative metric that is more suitable for measuring the accurate effect. We evaluate our method using three public benchmarks in comparison with a conventional steganalysis method and show up to a 20% improvement in terms of decoding rate. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Designing metabolic engineering strategies with genome-scale metabolic flux modeling

引用

Advances in Genomics and Genetics 2015年 5卷 93页

作者： Yen, Jiun Y Tanniche, Imen Fisher, Amanda K Gillaspy, Glenda E Bevan, David R Senger, Ryan S Department of Biological Systems Engineering Virginia Tech Department of Biochemistry Virginia Tech Genomics Bioinformatics and Computational Biology Interdisciplinary Program Virginia Tech

New in silico tools that make use of genome-scale metabolic flux modeling are improving the design of metabolic engineering strategies. This review highlights the latest developments in this area, explains the interface between these in silico tools and the experimental implementation tools of metabolic engineers, and provides a way forward so that in silico predictions can better mimic reality and more experimental methods can be considered in simulation studies. The several methodologies for solving genome-scale models (eg, flux balance analysis [FBA], parsimonious FBA, flux variability analysis, and minimization of metabolic adjustment) all have unique advantages and applications. There are two basic approaches to designing metabolic engineering strategies in silico, and both have demonstrated success in the literature. The first involves: 1) making a genetic manipulation in a model; 2) testing for improved performance through simulation; and 3) iterating the process. The second approach has been used in more recently designed in silico tools and involves: 1) comparing metabolic flux profiles of a wild-type and ideally engineered state and 2) designing engineering strategies based on the differences in these flux profiles. Improvements in genome-scale modeling are anticipated in areas such as the inclusion of all relevant cellular machinery, the ability to understand and anticipate the results of combinatorial enrichment experiments, and constructing dynamic and flexible biomass equations that can respond to environmental and genetic manipulations.

关键词： genome-scale modeling flux balance analysis flux variability analysis minimization of metabolic adjustment metabolic bottleneck pathway optimization

来源：评论

学校读者我要写书评

暂无评论

Peak analysis of cell-free RNA finds recurrently protected narrow regions with clinical potential

引用

Genome biology 2025年第1期26卷 119页

作者： Pengfei Bao Taiwei Wang Xiaofan Liu Shaozhen Xing Hanjin Ruan Hongli Ma Yuhuan Tao Qing Zhan Efres Belmonte-Reche Lizheng Qin Zhengxue Han Minghui Mao Mengtao Li Zhi John Lu MOE Key Laboratory of Bioinformatics State Key Lab of Green Biomanufacturing Center for Synthetic and Systems Biology School of Life Sciences Tsinghua University Beijing 100084 China. Institute for Precision Medicine Tsinghua University Beijing 100084 China. Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program School of Life Sciences Tsinghua University Beijing China. Department of Rheumatology and Clinical Immunology Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100730 China. National Clinical Research Center for Dermatologic and Immunologic Diseases (Ministry of Science & Technology) MOE Key Laboratory of Rheumatology and Clinical Immunology State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College Hospital Beijing 100730 China. Academy for Advanced Interdisciplinary Studies (AAIS)and Sciences Joint Graduate Program (PTN) Peking University Beijing China. Department of Oral and Maxillofacial & Head and Neck Oncology Beijing Stomatological Hospital Capital Medical University Beijing China. Centre for Genomics and Oncological Research (GENYO) Avenida de La Ilustración 114 Granada 18016 Spain. Department of Biochemistry and Molecular Biology II Faculty of Pharmacy University of Granada Granada Spain. Instituto de Investigación Biosanitaria Ibs.GRANADA Hospital Virgen de Las Nieves Granada Spain. Department of Oral and Maxillofacial & Head and Neck Oncology Beijing Stomatological Hospital Capital Medical University Beijing China. mmh_hover@***. Department of Rheumatology and Clinical Immunology Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100730 China. mengtao.li@***. National Clinical Research Center for Dermatologic and Immunologic Diseases (Ministry of Science & Technology) MOE Key Laboratory of Rheumatology and Clinical Immunology State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College Hospita

BACKGROUND:Cell-free RNAs (cfRNAs) can be detected in biofluids and have emerged as valuable disease biomarkers. Accurate identification of the fragmented cfRNA signals, especially those originating from pathological cells, is crucial for understanding their biological functions and clinical value. However, many challenges still need to be addressed for their application, including developing specific analysis methods and translating cfRNA fragments with biological support into clinical applications. RESULTS:We present cfPeak, a novel method combining statistics and machine learning models to detect the fragmented cfRNA signals effectively. When test in real and artificial cfRNA sequencing (cfRNA-seq) data, cfPeak shows an improved performance compared with other applicable methods. We reveal that narrow cfRNA peaks preferentially overlap with protein binding sites, vesicle-sorting sites, structural sites, and novel small non-coding RNAs (sncRNAs). When applied in clinical cohorts, cfPeak identified cfRNA peaks in patients' plasma that enable cancer detection and are informative of cancer types and metastasis. CONCLUSIONS:Our study fills the gap in the current small cfRNA-seq analysis at fragment-scale and builds a bridge to the scientific discovery in cfRNA fragmentomics. We demonstrate the significance of finding low abundant tissue-derived signals in small cfRNA and prove the feasibility for application in liquid biopsy.

关键词： Cell-free RNA CfRNA fragment Liquid biopsy biomarker Noninvasive cancer detection Peak calling SncRNA Tissue-of-origin

来源：评论

学校读者我要写书评

暂无评论

Hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification

arXiv

引用

arXiv 2017年

作者： Rhee, Sungmin Seo, Seokjun Kim, Sun Department of Computer Science and Engineering Seoul National University Bioinformatics Institute Seoul National University Seoul Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University

Network biology has been successfully used to help reveal complex mechanisms of disease, especially cancer. On the other hand, network biology requires in-depth knowledge to construct diseasespecific networks, but our current knowledge is very limited even with the recent advances in human cancer biology. Deep learning has shown an ability to address the problem like this. However, it conventionally used grid-like structured data, thus application of deep learning technologies to the human disease subtypes is yet to be explored. To overcome the issue, we propose a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN). Experimental results on synthetic data and breast cancer data demonstrate that our proposed method shows better performances than existing methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Prediction of Drug Classes with a Deep Neural Network using Drug Targets and Chemical Structure Data

Prediction of Drug Classes with a Deep Neural Network using ...

引用

IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Jeonghee Jo Hyun-Soo Choi Sungroh Yoon Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea Seoul National University Seoul South Korea Interdisciplinary Program in Bioinformatics ASRI INMC ISRC Institute of Engineering Research Seoul National University Seoul National University Seoul South Korea

ISBN: (纸本)9781728118680

Drugs are classified according to their biological and chemical reactions, and the systems that they target. Thus, an accurate and efficient prediction method for drug class discovery would reveal key properties of candidate drugs, significantly conserving time and resources in drug repositioning and design. Previous approaches, based on data mining or statistics, required complicated feature construction in advance. Knowing that deep learning can identifying patterns in high-dimensional datasets without elaborate feature selection or engineering, we constructed a model for predicting drug classes using deep neural networks - with biological and chemical structure data. Our proposed model outperforms previous learning-based methods in terms of prediction accuracy.

关键词： chemical engineering computing data mining drugs feature selection learning (artificial intelligence) neural nets Neural network Pharmaceutical Preparations data mining Feature extraction Drug Liberation Drug Repositioning chemical structure Biological Prediction methods

来源：评论

学校读者我要写书评

暂无评论

Unbalanced sample size effect on the genome-wide population differentiation studies

Unbalanced sample size effect on the genome-wide population ...

引用

IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Kyunghee Han Kyee-zu Kim Taesung Park Department of Statistics Seoul National University South Korea Interdisciplinary program in Bioinformatics Seoul National University South Korea

The fixation index (Fst) is one of the most widely used measurements of genetic distance between populations. The data set from the international HapMap project has been served as a reference data set for population differentiation studies. Fst is commonly used in order to compare the sample data with HapMap data. In this study, however, we show that Fst without consideration of sample sizes may mislead the result. In particular, we first demonstrate that Fst suffers from imbalance of sample sizes through simulation studies and through the analysis of a large scale Korean genome-wide association (GWA) data. Then, we propose a modified version of Fst which is shown to be more robust to imbalance of sample size. In addition, the chi-square test commonly used for homogeneity test is shown to perform similarly to the modified version of Fst.

关键词： bioinformatics Genomics Indexes Estimation Frequency estimation Correlation

来源：评论

学校读者我要写书评

暂无评论

Competitive pathway analysis using structural equation models (CPA-SEM) for gene expression data

Competitive pathway analysis using structural equation model...

引用

IEEE International Conference on bioinformatics and Biomedicine

作者： Sungkyoung Choi Sungyoung Lee Iksoo Huh Heungsun Hwang Taesung Park Interdisciplinary Program in Bioinformatics Seoul National University Department of Statistics Seoul National University Department of Psychology McGill University

ISBN: (纸本)9781467368001

There is an increasing interest in the pathway analysis of multiple genes and complex traits in association studies. Recently, a number of methods of pathway analysis have been developed to detect the novel pathways associated with human complex traits. In this paper, we propose a novel statistical approach for competitive pathway analysis based on Structural Equation Modeling (CPA-SEM), taking advantage of prior knowledge on existing relationships between genes in a pathway. Our CPA-SEM identifies pathways associated with traits of interest. The CPA-SEM approach is different from the previous SEM-based approaches in that it considers all possible sub-pathways into account and performs permutation based robust analysis. We applied the proposed CPA-SEM method to gene expression data of gastric cancer (GSE27342), and found that mTOR signaling pathway was significantly associated with gastric cancer. This pathway has previously been reported to be associated with gastric cancer. In conclusion, our CPA-SEM analysis provides a better understanding of biological mechanism by identifying pathways associated with a trait of interest.

关键词： Pathway analysis Structural equation modeling Prior biological knowledge Competitive approach Permutation test

来源：评论

学校读者我要写书评

暂无评论

GWAS-GMDR: A program package for genome-wide scan of gene-gene interactions with covariate adjustment based on multifactor dimensionality reduction

GWAS-GMDR: A program package for genome-wide scan of gene-ge...

引用

IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Min-Seok Kwon Kyunga Kim Sungyoung Lee Wonil Chung Sung-Gon Yi Junghyun Namkung Taesung Park Interdisciplinary program in Bioinformatics Seoul National University South Korea Department of Statistics Seoul National University South Korea

Multifactor dimensionality reduction (MDR) has been successfully applied to identification of gene-gene interactions for the complex traits. Generalized MDR (GMDR) was its extension that allows adjustment for covariates. The current GMDR software mainly focuses on candidate gene association studies with a relatively small number of genetic markers and has some limitations to be extended to genome-wide association studies (GWAS) with a large number of genetic markers. We develop GWAS-GMDR, an effective parallel computing program package with special features for GWAS with a large number of genetic markers by using distributed job scheduling method and/or CUDA-enabled high-performance graphic processing units (GPU). First, GWAS-GMDR implements an effective memory handling algorithm and efficient procedures for GMDR to make joint analysis of multiple genes feasible for GWAS. Second, a weighted version of cross-validation consistency based on `top-K selection' (WCVC K ) is proposed to report multiple candidates for causal gene-gene interactions. Third, various performance measures are implemented to evaluate MDR classifiers, including balanced accuracy, tau-b, likelihood ratio and normalized mutual information. Fourth, some popular methods for handling missing genotypes are implemented. Finally, our applications support both CPU-based and GPU-based parallel computing system. We applied our applications using a real genome wide data set from WTCCC Crohn's disease dataset to identify two-way interaction models in genome-wide scale. The GWAS-GMDR package is a powerful tool for the gene-gene interaction analysis in a genome-wide scale. High-performance implementations are provided as native binaries for Linux, Mac OS X and Windows systems.

关键词： bioinformatics Graphics processing unit Genomics Diseases Parallel processing

来源：评论

学校读者我要写书评

暂无评论

Inference of large-scale gene regulatory networks using regression-based network approach

引用

Journal of bioinformatics and Computational Biology 2009年第4期7卷 717-735页

作者： Kim, Haseong Lee, Jae K. Park, Taesung Interdisciplinary Program in Bioinformatics Seoul National University Seoul San 56-1 Shilim-dong South Korea Division of Biostatistics and Epidemiology University of Virginia Charlottesville VA 22908 United States Department of Statistics Seoul National University Seoul San 56-1 Shilim-dong South Korea

The gene regulatory network modeling plays a key role in search for relationships among genes. Many modeling approaches have been introduced to find the causal relationship between genes using time series microarray data. However, they have been suffering from high dimensionality, overfitting, and heavy computation time. Further, the selection of a best model among several possible competing models is not guaranteed that it is the best one. In this study, we propose a simple procedure for constructing large scale gene regulatory networks using a regression-based network approach. We determine the optimal out-degree of network structure by using the sum of squared coefficients which are obtained from all appropriate regression models. Through the simulated data, accuracy of estimation and robustness against noise are computed in order to compare with the vector autoregressive regression model. Our method shows high accuracy and robustness for inferring large-scale gene networks. Also it is applied to Caulobacter crecentus cell cycle data consisting of 1472 genes. It shows that many genes are regulated by two transcription factors, ctrA and gcrA, that are known for global regulators. © 2009 Imperial College Press.

关键词： Large-scale gene regulatory networks Linear regression Scale-free networks Weighted voting

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：