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» Using Determining Sets to Distinguish Kneser Graphs
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ICML
2007
IEEE
14 years 8 months ago
Learning to combine distances for complex representations
The k-Nearest Neighbors algorithm can be easily adapted to classify complex objects (e.g. sets, graphs) as long as a proper dissimilarity function is given over an input space. Bo...
Adam Woznica, Alexandros Kalousis, Melanie Hilario
WABI
2001
Springer
142views Bioinformatics» more  WABI 2001»
13 years 12 months ago
Pattern Matching and Pattern Discovery Algorithms for Protein Topologies
We describe algorithms for pattern matching and pattern learning in TOPS diagrams (formal descriptions of protein topologies). These problems can be reduced to checking for subgrap...
Juris Viksna, David Gilbert
ISBRA
2009
Springer
14 years 2 months ago
Untangling Tanglegrams: Comparing Trees by Their Drawings
A tanglegram is a pair of trees on the same set of leaves with matching leaves in the two trees joined by an edge. Tanglegrams are widely used in biology – to compare evolutiona...
Balaji Venkatachalam, Jim Apple, Katherine St. Joh...
ICML
2004
IEEE
14 years 8 months ago
Boosting margin based distance functions for clustering
The performance of graph based clustering methods critically depends on the quality of the distance function, used to compute similarities between pairs of neighboring nodes. In t...
Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall
BMCBI
2007
166views more  BMCBI 2007»
13 years 7 months ago
Optimization of filtering criterion for SEQUEST database searching to improve proteome coverage in shotgun proteomics
Background: In proteomic analysis, MS/MS spectra acquired by mass spectrometer are assigned to peptides by database searching algorithms such as SEQUEST. The assignations of pepti...
Xinning Jiang, Xiaogang Jiang, Guanghui Han, Mingl...