Institution
University of Turku
Education•Turku, Finland•
About: University of Turku is a education organization based out in Turku, Finland. It is known for research contribution in the topics: Population & Galaxy. The organization has 16296 authors who have published 45124 publications receiving 1505428 citations. The organization is also known as: Turun yliopisto & Åbo universitet.
Topics: Population, Galaxy, Poison control, Health care, Pregnancy
Papers published on a yearly basis
Papers
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Harvard University1, Broad Institute2, University of Helsinki3, Aalto University4, VTT Technical Research Centre of Finland5, Steno Diabetes Center6, Jorvi Hospital7, University of Tartu8, University Medical Center Groningen9, University of Eastern Finland10, University of Turku11, RMIT University12, National Institutes of Health13
TL;DR: Trends in the development of the human infant gut microbiome along with specific alterations that precede T1D onset and distinguish T2D progressors from nonprogressors are identified.
872 citations
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Iris M. Heid1, Anne U. Jackson2, Joshua C. Randall3, Tthomas W. Winkler1 +352 more•Institutions (90)
TL;DR: A meta-analysis of genome-wide association studies for WHR adjusted for body mass index provides evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.
Abstract: Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10⁻⁹ to P = 1.8 × 10⁻⁴⁰) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10⁻³ to P = 1.2 × 10⁻¹³). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.
869 citations
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TL;DR: The majority of children at risk of type 1 diabetes who had multiple islet autoantibody seroconversion progressed to diabetes over the next 15 years, and future prevention studies should focus on this high-risk population.
Abstract: Results Progression to type 1 diabetes at 10-year follow-up after islet autoantibody seroconversion in 585 children with multiple islet autoantibodies was 69.7% (95% CI, 65.1%-74.3%), and in 474 children with a single islet autoantibody was 14.5% (95% CI, 10.3%-18.7%). Risk of diabetes in children who had no islet autoantibodies was 0.4% (95% CI, 0.2%-0.6%) by the age of 15 years. Progression to type 1 diabetes in the children with multiple islet autoantibodies was faster for children who had islet autoantibody seroconversion younger than age 3 years (hazard ratio [HR], 1.65 [95% CI, 1.30-2.09; P .001]; 10-year risk, 74.9% [95% CI, 69.7%-80.1%]) vs children 3 years or older (60.9% [95% CI, 51.5%-70.3%]); for children with the human leukocyte antigen (HLA) genotype DR3/DR4-DQ8 (HR, 1.35 [95% CI, 1.09-1.68; P=.007]; 10-year risk, 76.6% [95% CI, 69.2%-84%]) vs other HLA genotypes (66.2% [95% CI, 60.2%-72.2%]); and for girls (HR, 1.28 [95% CI, 1.04-1.58; P=.02];10year risk, 74.8% [95% CI, 68.0%-81.6%]) vs boys (65.7% [95% CI, 59.3%72.1%]).
867 citations
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TL;DR: This paper presents four applications of PCNM analysis to ecological data representing combinations of: transect or surface data, regular or irregular sampling schemes, univariate or multivariate data, and new ecological knowledge was obtained through this analysis.
Abstract: Spatial structures may not only result from ecological interactions, they may also play an essential functional role in organizing the interactions. Modeling spatial patterns at multiple spatial and temporal scales is thus a crucial step to understand the functioning of ecological communities. PCNM (principal coordinates of neighbor matrices) analysis achieves a spectral decomposition of the spatial relationships among the sampling sites, creating variables that correspond to all the spatial scales that can be perceived in a given data set. The analysis then finds the scales to which a data table of interest responds. The significant PCNM variables can be directly interpreted in terms of spatial scales, or included in a procedure of variation decomposition with respect to spatial and environmental components. This paper presents four applications of PCNM analysis to ecological data representing combinations of: transect or surface data, regular or irregular sampling schemes, univariate or multivariate data. The data sets include Amazonian ferns, tropical marine zooplankton, chlorophyll in a marine lagoon, and oribatid mites in a peat bog. In each case, new ecological knowledge was obtained through PCNM analysis.
864 citations
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Indiana University1, Buck Institute for Research on Aging2, University of California, San Francisco3, Colorado State University4, University of California, Santa Cruz5, University of Colorado Denver6, Icahn School of Medicine at Mount Sinai7, University of California, Berkeley8, European Bioinformatics Institute9, University of Bologna10, University of Missouri11, University of Bristol12, University of Helsinki13, University College London14, Centre for Development of Advanced Computing15, Purdue University16, Baylor College of Medicine17, Royal Holloway, University of London18, Technische Universität München19, University of Turku20, Queen's University21, University UCINF22, Max Planck Society23, Imperial College London24, Wageningen University and Research Centre25, Nestlé26, Fudan University27, University of Padua28, Temple University29, Swiss Institute of Bioinformatics30, University of Geneva31, Hebrew University of Jerusalem32, Miami University33
TL;DR: Today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets, and there is considerable need for improvement of currently available tools.
Abstract: Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.
859 citations
Authors
Showing all 16461 results
Name | H-index | Papers | Citations |
---|---|---|---|
Kari Alitalo | 174 | 817 | 114231 |
Mika Kivimäki | 166 | 1515 | 141468 |
Jaakko Kaprio | 163 | 1532 | 126320 |
Veikko Salomaa | 162 | 843 | 135046 |
Markus W. Büchler | 148 | 1545 | 93574 |
Eugene C. Butcher | 146 | 446 | 72849 |
Steven Williams | 144 | 1375 | 86712 |
Terho Lehtimäki | 142 | 1304 | 106981 |
Olli T. Raitakari | 142 | 1232 | 103487 |
Pim Cuijpers | 136 | 982 | 69370 |
Jeroen J. Bax | 132 | 1306 | 74992 |
Sten Orrenius | 130 | 447 | 57445 |
Aarno Palotie | 129 | 711 | 89975 |
Stefan W. Hell | 127 | 577 | 65937 |
Carlos López-Otín | 126 | 494 | 83933 |