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AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
Aho, A. V., Garey, M. R. & Ullman, J. D. The Transitive Reduction of a Directed Graph. 1972 SIAM J. Comput.   article URL  
BibTeX:
@article{Aho1972,
  author = {Alfred V. Aho and M. R. Garey and Jeffrey D. Ullman},
  title = {The Transitive Reduction of a Directed Graph.},
  journal = {SIAM J. Comput.},
  year = {1972},
  volume = {1},
  number = {2},
  pages = {131-137},
  url = {http://dblp.uni-trier.de/db/journals/siamcomp/siamcomp1.html#AhoGU72}
}
Bininda-Emonds, O. R. The Evolution of Supertrees 2004 Trends in Ecology and Evolution   article  
BibTeX:
@article{bininda-emonds2004,
  author = {Olaf R.P. Bininda-Emonds},
  title = {The Evolution of Supertrees},
  journal = {Trends in Ecology and Evolution},
  year = {2004},
  volume = {19},
  number = {6},
  pages = {315-322}
}
Constantinescu, M. & Sankoff, D. An Efficient Algorithm for Supertrees 1995 Journal of Classification   article  
Abstract: Given k rooted binary trees A t,A2,..,Ak, with labeled leaves, we generate C, a unique system of lineage constraints on common ancestors. We then present an algorithm for constructing the set of rooted binary trees B, compatible with all of A I,A2,..,Ak. The running time to obtain one such supertree is
BibTeX:
@article{constantinescu1995,
  author = {Mariana Constantinescu and David Sankoff},
  title = {An Efficient Algorithm for Supertrees},
  journal = {Journal of Classification},
  year = {1995},
  volume = {12},
  pages = {101-112}
}
Doolittle, W. F. Phylogenetic Classification and the Universal Tree 1999 Science   article DOIURL  
BibTeX:
@article{doolittle1999,
  author = {W. Ford Doolittle},
  title = {Phylogenetic Classification and the Universal Tree},
  journal = {Science},
  year = {1999},
  volume = {284},
  number = {5423},
  pages = {2124-2128},
  url = {http://www.sciencemag.org/cgi/content/abstract/284/5423/2124},
  doi = {http://dx.doi.org/10.1126/science.284.5423.2124}
}
Fensel, D., Ding, Y., Omelayenko, B., Schulten, E., Botquin, G., Brown, M. & Flett, A. Product Data Integration in B2B E-Commerce. 2001 IEEE Intelligent Systems   article URL  
BibTeX:
@article{fensel2001,
  author = {Dieter Fensel and Ying Ding and Borys Omelayenko and Ellen Schulten and Guy Botquin and Mike Brown and Alan Flett},
  title = {Product Data Integration in B2B E-Commerce.},
  journal = {IEEE Intelligent Systems},
  year = {2001},
  volume = {16},
  number = {4},
  pages = {54-59},
  url = {http://dblp.uni-trier.de/db/journals/expert/expert16.html#FenselDOSBBF01}
}
Henikoff, S., Greene, E. A., Pietrokovski, S., Bork, P., Attwood, T. K. & Hood, L. Gene Families: The Taxonomy of Protein Paralogs and Chimeras 1997 Science   article DOIURL  
BibTeX:
@article{henikoff1997,
  author = {Steven Henikoff and Elizabeth A. Greene and Shmuel Pietrokovski and Peer Bork and Teresa K. Attwood and Leroy Hood},
  title = {Gene Families: The Taxonomy of Protein Paralogs and Chimeras},
  journal = {Science},
  year = {1997},
  volume = {278},
  number = {5338},
  pages = {609-614},
  url = {http://www.sciencemag.org/cgi/content/abstract/278/5338/609},
  doi = {http://dx.doi.org/10.1126/science.278.5338.609}
}
Heymann, P. & Garcia-Molina, H. Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems 2006   techreport  
Abstract: Collaborative tagging systems---systems where many casual users annotate objects with free-form strings (tags) of their choosing---have recently emerged as a powerful way to label and organize large collections of data. During our recent investigation into these types of systems, we discovered a simple but remarkably effective algorithm for converting a large corpus of tags annotating objects in a tagging system into a navigable hierarchical taxonomy of tags. We first discuss the algorithm and then present a preliminary model to explain why it is so effective in these types of systems.
BibTeX:
@techreport{heymann2006,
  author = {Paul Heymann and Hector Garcia-Molina},
  title = {{C}ollaborative {C}reation of {C}ommunal {H}ierarchical {T}axonomies in {S}ocial {T}agging {S}ystems},
  year = {2006},
  number = {2006--10}
}
Klein, M. Combining and relating ontologies: an analysis of problems and solutions 2001 Workshop on Ontologies and Information Sharing, IJCAI'01   inproceedings  
BibTeX:
@inproceedings{klein2001,
  author = {M. Klein},
  title = {Combining and relating ontologies: an analysis of problems and solutions},
  booktitle = {Workshop on Ontologies and Information Sharing, IJCAI'01},
  year = {2001}
}
Orengo, C., Michie, A., Jones, S., Jones, D., Swindells, M. & Thornton, J. CATH - a hierarchic classification of protein domain structures 1997 Structure   article URL  
Abstract: Background: Protein evolution gives rise to families of structurally related proteins, within which sequence identities can be extremely low. As a result, structure-based classifications can be effective at identifying unanticipated relationships in known structures and in optimal cases function can also be assigned. The ever increasing number of known protein structures is too large to classify all proteins manually, therefore, automatic methods are needed for fast evaluation of protein structures. Results: We present a semi-automatic procedure for deriving a novel hierarchical classification of protein domain structures (CATH). The four main levels of our classification are protein class (C), architecture (A), topology (T) and homologous superfamily (H). Class is the simplest level, and it essentially describes the secondary structure composition of each domain. In contrast, architecture summarises the shape revealed by the orientations of the secondary structure units, such as barrels and sandwiches. At the topology level, sequential connectivity is considered, such that members of the same architecture might have quite different topologies. When structures belonging to the same T-level have suitably high similarities combined with similar functions, the proteins are assumed to be evolutionarily related and put into the same homologous superfamily. Conclusions: Analysis of the structural families generated by CATH reveals the prominent features of protein structure space. We find that nearly a third of the homologous superfamilies (H-levels) belong to ten major T-levels, which we call superfolds, and furthermore that nearly two-thirds of these H-levels cluster into nine simple architectures. A database of well-characterised protein structure families, such as CATH, will facilitate the assignment of structure-function/ evolution relationships to both known and newly determined protein structures.
BibTeX:
@article{orengo1997,
  author = {CA Orengo and AD Michie and S Jones and DT Jones and MB Swindells and JM Thornton},
  title = {CATH - a hierarchic classification of protein domain structures},
  journal = {Structure},
  year = {1997},
  volume = {5},
  number = {8},
  pages = {1093--1108},
  url = {http://www.sciencedirect.com/science/article/B6VSR-4CP0VB1-3/1/5c587435799d19f9d1a3d04f8810f644}
}
Some desiderata for liberal supertrees 2004 Phylogenetic Supertrees: Combining Information to Reveal the Tree of Life   incollection  
BibTeX:
@incollection{wilkinson2003,,
  title = {Some desiderata for liberal supertrees},
  booktitle = {Phylogenetic Supertrees: Combining Information to Reveal the Tree of Life},
  publisher = {Kluwer Academic},
  year = {2004}
}

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