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Institution

Ludwig Maximilian University of Munich

EducationMunich, Germany
About: Ludwig Maximilian University of Munich is a education organization based out in Munich, Germany. It is known for research contribution in the topics: Population & Transplantation. The organization has 83850 authors who have published 161504 publications receiving 5792158 citations. The organization is also known as: LMU & University of Munich.


Papers
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Proceedings Article
02 Aug 1996
TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
Abstract: Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.

17,056 citations

Journal ArticleDOI
16 Apr 2020-Cell
TL;DR: It is demonstrated that SARS-CoV-2 uses the SARS -CoV receptor ACE2 for entry and the serine protease TMPRSS2 for S protein priming, and it is shown that the sera from convalescent SARS patients cross-neutralized Sars-2-S-driven entry.

15,362 citations

Proceedings Article
01 Jan 1996
TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.
Abstract: Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLARANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.

14,297 citations

Journal ArticleDOI
Claude Amsler1, Michael Doser2, Mario Antonelli, D. M. Asner3  +173 moreInstitutions (86)
TL;DR: This biennial Review summarizes much of particle physics, using data from previous editions.

12,798 citations

Journal ArticleDOI
TL;DR: A new program called Clustal Omega is described, which can align virtually any number of protein sequences quickly and that delivers accurate alignments, and which outperforms other packages in terms of execution time and quality.
Abstract: Multiple sequence alignments are fundamental to many sequence analysis methods. Most alignments are computed using the progressive alignment heuristic. These methods are starting to become a bottleneck in some analysis pipelines when faced with data sets of the size of many thousands of sequences. Some methods allow computation of larger data sets while sacrificing quality, and others produce high-quality alignments, but scale badly with the number of sequences. In this paper, we describe a new program called Clustal Omega, which can align virtually any number of protein sequences quickly and that delivers accurate alignments. The accuracy of the package on smaller test cases is similar to that of the high-quality aligners. On larger data sets, Clustal Omega outperforms other packages in terms of execution time and quality. Clustal Omega also has powerful features for adding sequences to and exploiting information in existing alignments, making use of the vast amount of precomputed information in public databases like Pfam.

12,489 citations


Authors

Showing all 84483 results

NameH-indexPapersCitations
Albert Hofman2672530321405
Paul M. Ridker2331242245097
Rudolf Jaenisch206606178436
David Miller2032573204840
Dennis J. Selkoe177607145825
Gregory Y.H. Lip1693159171742
Stanley B. Prusiner16874597528
Philippe Froguel166820118816
James G. Fujimoto1651115116451
Stephen L. Buchwald164108790994
Carlos Bustamante161770106053
Jens J. Holst1601536107858
Kaj Blennow1601845116237
Christian Gieger157617113657
Jerome I. Rotter1561071116296
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023316
20221,126
20218,436
20208,056
20197,521
20186,803