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  1. Proceedings of the KDD-09 Workshop on Statistical and Relational Learning in Bioinformatics (StReBio '09)
  2. Handling missing values and censored data in PCA of pharmacological matrices
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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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Preprint

Handling missing values and censored data in PCA of pharmacological matrices

Content Provider ACM Digital Library
Author Ramon, Jan Costa, Fabrizio
Abstract Experimental results often present a substantial fraction of missing and censored values. Here we propose a strategy to perform principal component analysis under this specific incomplete information hypothesis. This allows the reconstruction of the missing information in a way consistent with the experimental observations.
Starting Page 32
Ending Page 34
Page Count 3
File Format PDF
ISBN 9781605586670
DOI 10.1145/1562090.1562097
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-06-28
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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