检索结果-内蒙古大学图书馆

Proceedings of the 16th Annual UT-KBRIN bioinformatics Summit 2016: proceedings

BMC Proceedings 2017年第9期11卷 1-6页

作者： L. Leon Dent Sammed N. Mandape Siddharth Pratap Jianan Dong Jamaine Davis Jennifer A. Gaddy Kofi Amoah Steve Damo Dana R. Marshall Jacob Jones Toni Brandt Gilberto Diaz Qingguo Wang Todd Gary Ashwini Yenamandra Marina Z. Ghattas Marwa Elrakaiby Ramy K. Aziz Hamdallah H. Zedan Moamen Elmassry Marwa ElRakaiby Mariam Lotfy Jarrad Marcel Rania A. Khattab Maha M. Abdelfattah Jack A. Gilbert Pouya Dini Shavahn C. Loux Kirsten E. Scoggin Alejandro Esteller-Vico Edward L. Squires Mats H. T. Troedsson Barry A. Ball Peter Daels Kalpani De Silva Ernest Bailey Joel C. Stephens Theodore S. Kalbfleisch Christine E. Dolin Lauren G. Poole Gavin E. Arteel Michael L. Merchant Daniel W. Wilkey Michelle T. Barati Eric C. Rouchka Richard M. Higashi Teresa W-M Fan Hunter Moseley Andrew N Lane Department of Surgery Meharry Medical College Nashville TN 37208 USA Bioinformatics Core Meharry Medical College Nashville TN 37208 USA Department of Biochemistry and Cancer Biology Meharry Medical College Nashville TN 37208 USA Department of Medicine Vanderbilt University Medical Center Nashville TN 37232 USA Department of Life and Physical Sciences Fisk University Nashville TN 37208 USA Department of Pathology Anatomy and Cell Biology Meharry Medical College Nashville TN 37208 USA College of Computing and Technology Lipscomb University 1 University Park Drive Nashville TN 37204 USA Vanderbilt University Medical Center Nashville TN 37232 USA Office of Research Middle Tennessee State University 1301 East Main St Murfreesboro TN 37132 USA Department of Microbiology and Immunology Faculty of Pharmacy Cairo University Cairo Egypt Department of Biological Sciences Texas Tech University Lubbock TX 79409 USA Clinical Operations Department Ray-Clinical Research Organization Giza Egypt Genomic and Systems Biology Bioscience Division Argonne National Laboratory Argonne IL 60439 USA Research Institute of Ophthalmology Giza Egypt Department of Surgery University of Chicago Chicago IL 60637 USA Microbial Ecology Group Argonne National Laboratory Argonne IL 60439 USA Department of Veterinary Science University of Kentucky Lexington KY 40536 USA Department of Reproduction Faculty of Veterinary Medicine University of Gent Merelbeke Belgium Interdisciplinary Studies Program: Specialization in Bioinformatics University of Louisville Louisville KY 40292 USA Department of Veterinary Science University of Kentucky Lexington KY 40506 USA Genomics GPS Guilford CT 06437 USA Department of Biochemistry and Molecular Genetics School of Medicine University of Louisville Louisville KY 40292 USA Department of Pharmacology & Toxicology University of Louisville Louisville KY 40202 USA Department of Medicine University of Louisville

来源：评论

学校读者我要写书评

暂无评论

Front Cover: Synthesis and Immunomodulatory Activity of Fluorine-Containing Bisphosphonates (ChemMedChem 4/2019)

引用

ChemMedChem 2019年第4期14卷 360-360页

作者： Dr. Satoshi Mizuta Mohammed S. O. Tagod Dr. Masashi Iwasaki Prof. Yoichi Nakamura Dr. Hiroaki Senju Prof. Hiroshi Mukae Prof. Craig T. Morita Prof. Yoshimasa Tanaka Center for Bioinformatics and Molecular Medicine Graduate School of Biomedical Sciences Nagasaki University 1-12-4 Sakamoto Nagasaki 852-8523 Japan Program for Nurturing Global Leaders in Tropical and Emerging Infectious Diseases Graduate School of Biomedical Sciences Nagasaki University 1-12-4 Sakamoto Nagasaki 852-8523 Japan Center for Innovation in Immunoregulative Technology and Therapeutics Graduate School of Medicine Kyoto University Yoshidakonoe-cho Kyoto 606-8501 Japan Department of Respiratory Medicine Nagasaki University School of Medicine 1-7-1 Sakamoto Nagasaki 852-8501 Japan Department of Internal Medicine and the Interdisciplinary Graduate Program in Immunology University of Iowa Carver College of Medicine Iowa City Veterans Affairs Health Care System Iowa City IA 52246 USA Hyogo College of Medicine 1-1 Mukogawa Nishinomiya Hyogo 663-8501 Japan

来源：评论

学校读者我要写书评

暂无评论

Proceedings of the 16th Annual UT-KBRIN bioinformatics Summit 2016: bioinformatics

引用

BMC bioinformatics 2017年第9期18卷 1-9页

作者： Eric C. Rouchka Juw Won Park Julia H. Chariker David A. Tieri Shreedharkumar Rajurkar Vikas Singh Nishchal K. Verma Yan Cui Mark Farman Bradford Condon Jolanta Jaromczyk Neil Moore Jerzy Jaromczyk Daniel Harris Patrick Calie Eun Kyong Shin Robert L. Davis Arash Shaban-Nejad Joshua M. Mitchell Hunter N. B. Moseley Robert M. Flight Qing Jun Wang Richard M. Higashi Teresa W-M Fan Andrew N. Lane Liangqun Lu Bernie J Daigle Jr. Xi Chen Andrey Smelter Jerzy W. Jaromczyk Bailey K. Phan Nathaniel J. Serpico Ethan G. Toney Li Chen Caroline E. Melton Jennifer R. Mandel Bernie J. Daigle Jr. Hao Chen Kazi I. Zaman Ramin Homayouni Patrick J. Trainor Andrew P. DeFilippis Samantha M. Carlisle Shesh N. Rai Department of Computer Engineering and Computer Science University of Louisville Duthie Center for Engineering Louisville KY 40292 USA Kentucky Biomedical Research Infrastructure (KBRIN) Bioinformatics Core 522 East Gray Street Louisville KY 40292 USA Department of Psychological and Brain Sciences University of Louisville Louisville KY 40292 USA Department of Anatomical Sciences and Neurobiology University of Louisville Louisville KY 40292 USA Department of Electrical Engineering IIT Kanpur India Department of Microbiology Immunology and Biochemistry University of Tennessee Health Science Center Memphis TN 38103 USA Center for Integrative and Translational Genomics University of Tennessee Health Science Center Memphis TN 38103 USA Department of Plant Pathology University of Kentucky Lexington KY 40546 USA UK Healthcare Genomics University of Kentucky Lexington KY 40536 USA Department of Entomology & Plant Pathology University of Tennessee Knoxville TN 37996 USA Department of Forestry University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40508 USA Department of Biological Sciences Eastern Kentucky University Richmond KY 40475 USA University of Tennessee Health Science Center – Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics Department of Pediatrics Memphis TN 38103 USA Department of Molecular & Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Markey Cancer Center University of Kentucky Lexington KY 40536 USA Center for Environmental and Systems Biochemistry University of Kentucky Lexington KY 40536 USA Institute for Biomedical Informatics University of Kentucky Lexington KY 40536 USA Department of Molecular and Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Department of Statistics University of Kentucky Louisville KY

来源：评论

学校读者我要写书评

暂无评论

Chronic obstructive pulmonary disease and related phenotypes: polygenic risk scores in population-based and case-control cohorts

引用

The Lancet Respiratory Medicine 2020年第7期8卷 696-708页

作者： Moll, Matthew Sakornsakolpat, Phuwanat Shrine, Nick Hobbs, Brian D DeMeo, Dawn L John, Catherine Guyatt, Anna L McGeachie, Michael J Gharib, Sina A Obeidat, Ma'en Lahousse, Lies Wijnant, Sara R A Brusselle, Guy Meyers, Deborah A Bleecker, Eugene R Li, Xingnan Tal-Singer, Ruth Manichaikul, Ani Rich, Stephen S Won, Sungho Kim, Woo Jin Do, Ah Ra Washko, George R Barr, R Graham Psaty, Bruce M Bartz, Traci M Hansel, Nadia N Barnes, Kathleen Hokanson, John E Crapo, James D Lynch, David Bakke, Per Gulsvik, Amund Hall, Ian P Wain, Louise Soler Artigas, María Jackson, Victoria E Strachan, David P Hui, Jennie James, Alan L Kerr, Shona M Polasek, Ozren Vitart, Veronique Marten, Jonathan Rudan, Igor Kähönen, Mika Surakka, Ida Gieger, Christian Karrasch, Stefan Rawal, Rajesh Schulz, Holger Deary, Ian J Harris, Sarah E Enroth, Stefan Gyllensten, Ulf Imboden, Medea Probst-Hensch, Nicole M Lehtimäki, Terho Raitakari, Olli T Langenberg, Claudia Luan, Jian'an Wareham, Nick Zhao, Jing Hua Hayward, Caroline Murray, Alison Porteous, David J Smith, Blair H Jarvelin, Marjo-Riitta Wielscher, Matthias Joshi, Peter K Kentistou, Katherine A Timmers, Paul RHJ Wilson, James F Cook, James P Lind, Lars Mahajan, Anubha Morris, Andrew P Ewert, Ralf Homuth, Georg Stubbe, Beate Weiss, Stefan Zeggini, Eleftheria Weiss, Scott T Silverman, Edwin K Dudbridge, Frank Tobin, Martin D Cho, Michael H Channing Division of Network Medicine Brigham and Women's Hospital Boston MA United States Division of Pulmonary and Critical Care Medicine Brigham and Women's Hospital Boston MA United States Department of Medicine Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand Genetic Epidemiology Group Department of Health Sciences University of Leicester Leicester United Kingdom Computational Medicine Core Center for Lung Biology Department of Medicine University of Washington Seattle WA United States Division of Pulmonary Critical Care and Sleep Medicine Department of Medicine University of Washington Seattle WA United States Cardiovascular Health Research Unit Department of Medicine University of Washington Seattle WA United States University of British Columbia Center for Heart Lung Innovation St Paul's Hospital Vancouver BC Canada Department of Epidemiology Erasmus Medical Centre Rotterdam Netherlands Department of Respiratory Medicine Erasmus Medical Centre Rotterdam Netherlands Department of Bioanalysis Faculty of Pharmaceutical Sciences Ghent University Ghent Belgium Department of Respiratory Medicine Ghent University Hospital Ghent Belgium Department of Medicine University of Arizona Tucson AZ United States GlaxoSmithKline Research and Development Collegeville PA United States Center for Public Health Genomics University of Virginia Charlottesville VA United States Department of Public Health Sciences University of Virginia Charlottesville VA United States Department of Public Health Sciences Graduate School of Public Health Seoul National University Seoul South Korea Interdisciplinary Program of Bioinformatics College of National Sciences Seoul National University Seoul South Korea Institute of Health and Environment Seoul National University Seoul South Korea Department of Internal Medicine Kangwon National University Chuncheon South Korea Department of Medicine and Department of Epidemiology Columbia Universit

Background: Genetic factors influence chronic obstructive pulmonary disease (COPD) risk, but the individual variants that have been identified have small effects. We hypothesised that a polygenic risk score using additional variants would predict COPD and associated phenotypes. Methods: We constructed a polygenic risk score using a genome-wide association study of lung function (FEV₁ and FEV₁/forced vital capacity [FVC]) from the UK Biobank and SpiroMeta. We tested this polygenic risk score in nine cohorts of multiple ethnicities for an association with moderate-to-severe COPD (defined as FEV₁/FVC <0·7 and FEV₁ <80% of predicted). Associations were tested using logistic regression models, adjusting for age, sex, height, smoking pack-years, and principal components of genetic ancestry. We assessed predictive performance of models by area under the curve. In a subset of studies, we also studied quantitative and qualitative CT imaging phenotypes that reflect parenchymal and airway pathology, and patterns of reduced lung growth. Findings: The polygenic risk score was associated with COPD in European (odds ratio [OR] per SD 1·81 [95% CI 1·74–1·88] and non-European (1·42 [1·34–1·51]) populations. Compared with the first decile, the tenth decile of the polygenic risk score was associated with COPD, with an OR of 7·99 (6·56–9·72) in European ancestry and 4·83 (3·45–6·77) in non-European ancestry cohorts. The polygenic risk score was superior to previously described genetic risk scores and, when combined with clinical risk factors (ie, age, sex, and smoking pack-years), showed improved prediction for COPD compared with a model comprising clinical risk factors alone (AUC 0·80 [0·79–0·81] vs 0·76 [0·75–0·76]). The polygenic risk score was associated with CT imaging phenotypes, including wall area percent, quantitative and qualitative measures of emphysema, local histogram emphysema patterns, and destructive emphysema subtypes. The po

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Open-Source Knowledge Graph Ecosystem for the Life Sciences

arXiv

引用

arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Whole genome sequencing reveals the impact of recent artificial selection on red sea bream reared in fish farms

引用

Scientific reports 2020年第1期10卷 1625页

作者： Bo-Hye Nam DongAhn Yoo Young-Ok Kim Jung Youn Park Younhee Shin Ga-Hee Shin Chan-Il Park Heebal Kim Woori Kwak Biotechnology Research Division National Institute of Fisheries Science Busan 46083 Republic of Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea. Research and Development Center Insilicogen Inc. Gyeonggi-do 16954 Republic of Korea. Department of Marine Biology & Aquaculture Gyeongsang National University Tongyeong 53064 Republic of Korea. C&K genomics 26 Beobwon-ro 9-gil H business Park Bldg. C #1008 Songpa-gu Seoul Republic of Korea. Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences 7 Seoul National University Seoul Republic of Korea. C&K genomics 26 Beobwon-ro 9-gil H business Park Bldg. C #1008 Songpa-gu Seoul Republic of Korea. asleo@***.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Immunity geneIFITM3variant: relation to cognition and Alzheimer pathology

引用

Alzheimer's & Dementia 2023年第S1期19卷

作者： Jung-Min Pyun Young Ho Park Moon-ku Han Angela Hodges Jae-Won Jang Paula Bice SangYun Kim Andrew J. Saykin Kwangsik Nho Seoul National University Bundang Hospital Seongnam Korea Republic of South Korea Soonchunhyang University Seoul Hospital Seoul Korea Republic of South Korea King’s College London London United Kingdom Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Chuncheon Korea Republic of South Korea Indiana University School of Medicine Indianapolis IN USA Seoul National University Bundang Hospital Seoul National University College of Medicine Seongnam Korea Republic of South Korea Indiana Alzheimer’s Disease Research Center Indiana University School of Medicine Indianapolis IN USA Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis IN USA Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis IN USA Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN USA Indiana Alzheimer’s Disease Research Center Indianapolis IN USA

Background IFITM3, an innate immune response protein and inhibitor of viral infection, was reported to modulate amyloid-β production in Alzheimer’s disease (AD). We aimed to identify single-nucleotide polymorphisms (SNPs) in IFITM3 associated with cognition and AD biomarkers. Method We used genetic, longitudinal cognition and AD biomarker data from Alzheimer’s Disease Neuroimaging Initiative (ADNI; N = 1,565) and AddNeuroMed (N = 633) as discovery and replication samples, respectively. First, we performed gene-based association analysis of SNPs in IFITM3 with cognitive performance. Second, we performed SNP-based association analysis in IFITM3 with cognitive decline and AD biomarkers from amyloid positron emission tomography (PET), cerebrospinal fluid (CSF), and magnetic resonance imaging (MRI). Result Gene-based association analysis showed that IFITM3 was significantly associated with cognitive performance (permutation-corrected p = 1.25×10 −3 ). Particularly, among two SNPs (rs10751647, rs2091850) in IFITM3 significantly associated with cognitive performance, rs10751647 was associated with cognitive decline in ADNI, which was replicated in AddNeuroMed. In addition, rs10751647 was significantly associated with amyloid-β deposition measured by amyloid PET scan, CSF phosphorylated tau levels, and entorhinal cortical thickness measured by MRI scan in ADNI. The association of rs10751647 with entorhinal cortical thickness was replicated in AddNeuroMed. Participants with minor alleles (C) of rs10751647 have less cognitive decline, less amyloid and tau burden, and less brain atrophy. eQTL analysis showed that rs10751647 is associated with IFITM3 expression levels in blood and brain. Conclusion This suggests that rs10751647 in IFITM3 is associated with less vulnerability for cognitive decline and AD biomarkers, providing mechanistic insight regarding involvement of immune activity and infection in AD.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Association between known Alzheimer’s disease risk loci andin vivobrain amyloid pathology in a Korean Cohort

引用

Alzheimer's & Dementia 2023年第S24期19卷

作者： Hyejin Ahn Min Soo Byun Dahyun Yi Gijung Jung Yen-Ning Huang Anthony J. Griswold Margaret A. A Pericak-Vance Yu Kyeong Kim Yun-Sang Lee Chul-Ho Sohn Koung Mi Kang Jun-Young Lee Andrew J. Saykin Kwangsik Nho Dong Young Lee Interdisciplinary program of cognitive science Seoul National University College of Humanities Seoul Korea Republic of (South) HyejinAhn Interdisciplinary program of cognitive science Seoul National University College of Humanities Seoul Korea Republic of (South). Department of Psychiatry Seoul National University College of Medicine Seoul Korea Republic of (South) Seoul National University Dementia Research Center Seoul Korea Republic of (South) Department of Neuropsychiatry Seoul National University Hospital Seoul Korea Republic of (South) Institute of Human Behavioral Medicine Medical Research Center Seoul National University Seoul Korea Republic of (South) Center for Neuroimaging Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis IN USA Indiana Alzheimer Disease Research Center Indiana University School of Medicine Indianapolis IN USA University of Miami Miller School of Medicine Miami FL USA Department of Nuclear Medicine SMG-SNU Boramae Medical Center Seoul Korea Republic of (South) Department of Nuclear Medicine Seoul National University College of Medicine Seoul Korea Republic of (South) Department of Radiology Seoul National University Hospital Seoul Korea Republic of (South) Department of Neuropsychiatry SMG-SNU Boramae Medical Center Seoul Korea Republic of (South) Indiana Alzheimer’s Disease Research Center Indiana University School of Medicine Indianapolis IN USA Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis IN USA Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN USA

Background Most genome-wide association studies (GWAS) of Alzheimer’s disease (AD) have been conducted in populations of European ancestry. It is important to determine if known AD-related variants from these studies are associated with AD biomarkers. Therefore, we investigated the association between known AD risk loci and in vivo brain Aβ deposition in older Korean adults. Method Participants (n = 503) from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s disease (KBASE) were analyzed. Older adults who were cognitively normal (CN), had mild cognitive impairment (MCI) or AD dementia (264 CN, 148 MCI, 91 AD) underwent comprehensive assessments including 11 C-PiB-PET/MRI and blood sampling. Among 76 AD-related target single nucleotide polymorphisms (SNPs) with genetic evidence compiled by the AD Sequencing Project (ADSP), 38 SNPs with minor allele frequency (MAF)>1% were extracted from TOPMed-based imputed GWAS genotyping data in KBASE. We employed exploratory univariable logistic regression analysis with Aβ positivity as the dependent variable for each SNP. Then, SNPs with a significance level of p<0.05 were subjected to confirmatory multivariable logistic regression analysis, adjusting for age and sex, with Bonferroni correction. Result In the first step of exploratory analyses, we discovered significant differences in Aβ positivity rates between carriers and noncarriers of minor allele of several loci, including rs16824536 in MME , rs11218343 in SORL1 , rs4985556 in IL34 , rs4277405 in ACE and rs3851179, located in the intergenic region between EED and PICALM . The subsequent confirmatory analysis found an association between these variants and Aβ positivity after adjusting age and sex (Table 1). Notably, the negative association of rs3851179 with Aβ positivity remained significant after correcting for multiple comparisons and after controlling for APOE4 carrier status, age, and sex (OR 0.529, 95% CI 0.345–0.813). Conclusion We id

关键词：

来源：评论

学校读者我要写书评

暂无评论

Correction to: Comparative genome analyses of four rice-infecting Rhizoctonia solani isolates reveal extensive enrichment of homogalacturonan modification genes

引用

BMC genomics 2021年第1期22卷 306页

作者： Da-Young Lee Jongbum Jeon Ki-Tae Kim Kyeongchae Cheong Hyeunjeong Song Gobong Choi Jaeho Ko Stephen O Opiyo James C Correll Shimin Zuo Sheshu Madhav Guo-Liang Wang Yong-Hwan Lee Department of Plant Pathology The Ohio State University Columbus OH 43210 USA. Fungal Bioinformatics Laboratory Seoul National University Seoul 08826 South Korea. Interdisciplinary Program in Agricultural Genomics Seoul National University Seoul 08826 South Korea. Department of Agricultural Life Science Sunchon National University Suncheon 57922 South Korea. Department of Agricultural Biotechnology Seoul National University Seoul 08826 South Korea. Ohio Agricultural Research and Development Center (OARDC) Molecular & Cellular Imaging Center (MCIC)-Columbus The Ohio State University Columbus OH 43210 USA. Department of Entomology & Plant Pathology University of Arkansas Fayetteville AK 72701 USA. Key Laboratory of Plant Functional Genomics of the Ministry of Education/ Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding Agricultural College of Yangzhou University Yangzhou China. Indian Council of Agricultural Research-Indian Institute of Rice Research (ICAR-IIRR) Hyderabad Telangana 500030 India. Department of Plant Pathology The Ohio State University Columbus OH 43210 USA. wang.620@osu.edu. Fungal Bioinformatics Laboratory Seoul National University Seoul 08826 South Korea. yonglee@snu.ac.kr. Interdisciplinary Program in Agricultural Genomics Seoul National University Seoul 08826 South Korea. yonglee@snu.ac.kr. Department of Agricultural Biotechnology Seoul National University Seoul 08826 South Korea. yonglee@snu.ac.kr. Center for Fungal Genetic Resources Plant Immunity Research Center Plant Genomics and Breeding Institute and Research Institute of Agriculture and Life Sciences Seoul National University Seoul 08826 South Korea. yonglee@snu.ac.kr.

来源：评论

学校读者我要写书评

暂无评论

scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs

引用

Cell systems 2023年第4期14卷 302-311.e4页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Yu-Te Lin Cristhian Roman-Vicharra Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Department of Nutrition and the Program in Integrative Nutrition & Complex Diseases Texas A&M University College Station TX 77843 USA. Electronic address: r-chapkin@tamu.edu. Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA. Electronic address: jcai@tamu.edu.

We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data correspondences to embed ligand and receptor genes expressed in interacting cells into a unified latent space. Neural networks are employed to minimize the distance between corresponding genes while preserving the structure of gene regression networks. We apply scTenifoldXct to real datasets for testing and demonstrate that our method detects interactions with high consistency compared with other methods. More importantly, scTenifoldXct uncovers weak but biologically relevant interactions overlooked by other methods. We also demonstrate how scTenifoldXct can be used to compare different samples, such as healthy vs. diseased and wild type vs. knockout, to identify differential interactions, thereby revealing functional implications associated with changes in cellular communication status.

关键词： cell-cell interaction cellular communication gene regression network machine learning manifold alignment neural networks scRNA-seq single-cell RNA sequencing

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：