A
Anders Krogh
Researcher at University of Copenhagen
Publications - 198
Citations - 67554
Anders Krogh is an academic researcher from University of Copenhagen. The author has contributed to research in topics: Hidden Markov model & Genome. The author has an hindex of 78, co-authored 190 publications receiving 62502 citations. Previous affiliations of Anders Krogh include Technical University of Denmark & University of California, Santa Cruz.
Papers
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Journal ArticleDOI
Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes
TL;DR: A new membrane protein topology prediction method, TMHMM, based on a hidden Markov model is described and validated, and it is discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C-in topologies.
Journal ArticleDOI
Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence
Stewart T. Cole,Roland Brosch,Julian Parkhill,Thierry Garnier,Carol Churcher,David Harris,Stephen V. Gordon,Karin Eiglmeier,S. Gas,Clifton E. Barry,Fredj Tekaia,K. Badcock,D. Basham,D. Brown,Tracey Chillingworth,R. Connor,Robert L. Davies,K. Devlin,Theresa Feltwell,S. Gentles,N. Hamlin,S. Holroyd,T. Hornsby,Kay Jagels,Anders Krogh,J. McLean,Sharon Moule,Lee Murphy,K. Oliver,J. Osborne,Michael A. Quail,Marie-Adèle Rajandream,Jane Rogers,S. Rutter,K. Seeger,Jason Skelton,Rob Squares,S. Squares,John Sulston,K. Taylor,Sally Whitehead,Bart Barrell +41 more
TL;DR: The complete genome sequence of the best-characterized strain of Mycobacterium tuberculosis, H37Rv, has been determined and analysed in order to improve the understanding of the biology of this slow-growing pathogen and to help the conception of new prophylactic and therapeutic interventions.
Book
Introduction To The Theory Of Neural Computation
TL;DR: This book is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning.
Book
Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
TL;DR: This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis.
Proceedings Article
A Hidden Markov Model for Predicting Transmembrane Helices in Protein Sequences
TL;DR: The transmembrane HMM, TMHMM, correctly predicts the entire topology for 77% of the sequences in a standard dataset of 83 proteins with known topology, and the same accuracy was achieved on a larger dataset of 160 proteins.