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ISMB
1993
13 years 6 months ago
Discovering Sequence Similarity by the Algorithmic Significance Method
The minimal-length encoding approach is applied to define concept of sequence similarity. Asequence is defined to be similar to another sequence or to a set of keywords if it can ...
Aleksandar Milosavljevic
PAKDD
2001
ACM
148views Data Mining» more  PAKDD 2001»
13 years 9 months ago
Scalable Hierarchical Clustering Method for Sequences of Categorical Values
Data clustering methods have many applications in the area of data mining. Traditional clustering algorithms deal with quantitative or categorical data points. However, there exist...
Tadeusz Morzy, Marek Wojciechowski, Maciej Zakrzew...
KDD
2006
ACM
156views Data Mining» more  KDD 2006»
14 years 5 months ago
Discovering significant OPSM subspace clusters in massive gene expression data
Order-preserving submatrixes (OPSMs) have been accepted as a biologically meaningful subspace cluster model, capturing the general tendency of gene expressions across a subset of ...
Byron J. Gao, Obi L. Griffith, Martin Ester, Steve...
WABI
2009
Springer
142views Bioinformatics» more  WABI 2009»
13 years 11 months ago
Back-Translation for Discovering Distant Protein Homologies
Background: Frameshift mutations in protein-coding DNA sequences produce a drastic change in the resulting protein sequence, which prevents classic protein alignment methods from ...
Marta Gîrdea, Laurent Noé, Gregory Ku...
BMCBI
2006
127views more  BMCBI 2006»
13 years 4 months ago
Using local gene expression similarities to discover regulatory binding site modules
Background: We present an approach designed to identify gene regulation patterns using sequence and expression data collected for Saccharomyces cerevisae. Our main goal is to rela...
Bartek Wilczynski, Torgeir R. Hvidsten, Andriy Kry...