Institution
University of Graz
Education•Graz, Steiermark, Austria•
About: University of Graz is a education organization based out in Graz, Steiermark, Austria. It is known for research contribution in the topics: Population & Quantum chromodynamics. The organization has 17934 authors who have published 37489 publications receiving 1110980 citations. The organization is also known as: Carolo Franciscea Graecensis & Karl Franzens Universität.
Papers published on a yearly basis
Papers
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University Hospital of Lausanne1, Harvard University2, Tel Aviv Sourasky Medical Center3, Tel Aviv University4, University of Illinois at Chicago5, Augsburg College6, University of Pittsburgh7, University of Virginia8, Northwestern University9, Claude Bernard University Lyon 110, Case Western Reserve University11, Medical College of Wisconsin12, University of Graz13, University of Kiel14, Cleveland Clinic15, Cornell University16, University of Hamburg17, Masaryk University18, Boston University19, University of Innsbruck20, University of Zurich21, Columbia University22, Tufts University23, NorthShore University HealthSystem24, Memorial Sloan Kettering Cancer Center25
TL;DR: No improvement in overall survival was demonstrated, however efficacy and activity with this chemotherapy-free treatment device appears comparable to chemotherapy regimens that are commonly used for recurrent glioblastoma.
659 citations
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TL;DR: It is concluded that neural efficiency might arise when individuals are confronted with tasks of (subjectively) low to moderate task difficulty and it is mainly observable for frontal brain areas.
654 citations
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TL;DR: Interactive machine learning (iML) is defined as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.”
Abstract: Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.” This “human-in-the-loop” can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.
651 citations
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National Institutes of Health1, University of Cambridge2, Wellcome Trust Sanger Institute3, Rockefeller University4, University of California, Davis5, Leibniz Association6, Seoul National University7, University of Southern California8, European Bioinformatics Institute9, Max Planck Society10, Dresden University of Technology11, Radboud University Nijmegen12, University of St Andrews13, University of Massachusetts Amherst14, University of Adelaide15, University of Missouri16, East Carolina University17, University of Queensland18, Clemson University19, University of Otago20, University of Arizona21, Natural History Museum22, Bangor University23, University of Konstanz24, Harvard University25, Northeastern University26, University of Antwerp27, National Museum of Natural History28, University of Graz29, University of Florida30, University of Basel31, University of California, Santa Cruz32, Zoological Society of San Diego33, Pacific Biosciences34, Pompeu Fabra University35, University of Maryland, College Park36, Harbin Institute of Technology37, University of Chicago38, Oregon Health & Science University39, Qatar Airways40, Monash University Malaysia Campus41, University of Milan42, Goethe University Frankfurt43, Pennsylvania State University44, University of Los Andes45, Norwegian University of Science and Technology46, University of Copenhagen47, Agency for Science, Technology and Research48, Royal Ontario Museum49, Smithsonian Institution50, Howard Hughes Medical Institute51, Walter Reed Army Institute of Research52, University of East Anglia53, University College Dublin54, University of Illinois at Urbana–Champaign55, La Trobe University56, University of California, San Diego57, Nova Southeastern University58
TL;DR: The Vertebrate Genomes Project (VGP) as mentioned in this paper is an international effort to generate high quality, complete reference genomes for all of the roughly 70,000 extant vertebrate species and to help to enable a new era of discovery across the life sciences.
Abstract: High-quality and complete reference genome assemblies are fundamental for the application of genomics to biology, disease, and biodiversity conservation. However, such assemblies are available for only a few non-microbial species1-4. To address this issue, the international Genome 10K (G10K) consortium5,6 has worked over a five-year period to evaluate and develop cost-effective methods for assembling highly accurate and nearly complete reference genomes. Here we present lessons learned from generating assemblies for 16 species that represent six major vertebrate lineages. We confirm that long-read sequencing technologies are essential for maximizing genome quality, and that unresolved complex repeats and haplotype heterozygosity are major sources of assembly error when not handled correctly. Our assemblies correct substantial errors, add missing sequence in some of the best historical reference genomes, and reveal biological discoveries. These include the identification of many false gene duplications, increases in gene sizes, chromosome rearrangements that are specific to lineages, a repeated independent chromosome breakpoint in bat genomes, and a canonical GC-rich pattern in protein-coding genes and their regulatory regions. Adopting these lessons, we have embarked on the Vertebrate Genomes Project (VGP), an international effort to generate high-quality, complete reference genomes for all of the roughly 70,000 extant vertebrate species and to help to enable a new era of discovery across the life sciences.
647 citations
Authors
Showing all 18136 results
Name | H-index | Papers | Citations |
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David Haussler | 172 | 488 | 224960 |
Russel J. Reiter | 169 | 1646 | 121010 |
Frederik Barkhof | 154 | 1449 | 104982 |
Philip Scheltens | 140 | 1175 | 107312 |
Christopher D.M. Fletcher | 138 | 674 | 82484 |
Jennifer S. Haas | 128 | 840 | 71315 |
Jelena Krstic | 126 | 839 | 73457 |
Michael A. Kamm | 124 | 637 | 53606 |
Frances H. Arnold | 119 | 510 | 49651 |
Gert Pfurtscheller | 117 | 507 | 62873 |
Georg Kresse | 111 | 430 | 244729 |
Manfred T. Reetz | 110 | 959 | 42941 |
Alois Fürstner | 108 | 459 | 43085 |
David N. Herndon | 108 | 1227 | 54888 |
David J. Williams | 107 | 2060 | 62440 |