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  1. Proceedings of the KDD-09 Workshop on Statistical and Relational Learning in Bioinformatics (StReBio '09)
  2. Lift-based search for significant dependencies in dense data sets
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Using random forests to uncover bivariate interactions in high dimensional small data sets
Identification of structurally important amino acids in proteins by graph-theoretic measures
Lift-based search for significant dependencies in dense data sets
Finding optimal parameters for edit distance based sequence classification is NP-hard
Multi-class protein fold recognition using large margin logic based divide and conquer learning
Protein sequence alignment and structural disorder: a substitution matrix for an extended alphabet
Handling missing values and censored data in PCA of pharmacological matrices
Comparing graph-based representations of protein for mining purposes
Can we improve on the identification of transcription factor binding sites?

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Lift-based search for significant dependencies in dense data sets

Content Provider ACM Digital Library
Author Hämäläinen, W.
Abstract Dependency analysis is an important but computationally demanding problem in all empirical science. It is especially problematic in bioinformatics, where data sets are often high dimensional, dense and/or strongly correlated. As a solution, we introduce a new algorithm which searches the most significant association rules expressing positive dependencies. The algorithm uses several effective pruning principles, which enable search without any minimum frequency thresholds. According to our initial experiments, the algorithm suits especially well for typical biological and medical data sets.
Starting Page 12
Ending Page 16
Page Count 5
File Format PDF
ISBN 9781605586670
DOI 10.1145/1562090.1562093
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-06-28
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Statistical significance Dependency analysis Redundancy Depth-first search Association rules
Content Type Text
Resource Type Article
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