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» Clustering Genes Using Heterogeneous Data Sources
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ICDM
2007
IEEE
149views Data Mining» more  ICDM 2007»
14 years 5 months ago
Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization
Consensus clustering and semi-supervised clustering are important extensions of the standard clustering paradigm. Consensus clustering (also known as aggregation of clustering) ca...
Tao Li, Chris H. Q. Ding, Michael I. Jordan
KDD
2005
ACM
149views Data Mining» more  KDD 2005»
14 years 4 months ago
A distributed learning framework for heterogeneous data sources
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Srujana Merugu, Joydeep Ghosh
BIBM
2008
IEEE
107views Bioinformatics» more  BIBM 2008»
14 years 5 months ago
A Functional Network of Yeast Genes Using Gene Ontology Information
In the post-genomic era, the organization of genes into networks has played an important role in characterizing the functions of individual genes and the interplay between them. I...
Erliang Zeng, Giri Narasimhan, Lisa Schneper, Kala...
BMCBI
2005
89views more  BMCBI 2005»
13 years 10 months ago
Theme discovery from gene lists for identification and viewing of multiple functional groups
Background: High throughput methods of the genome era produce vast amounts of data in the form of gene lists. These lists are large and difficult to interpret without advanced com...
Petri Pehkonen, Garry Wong, Petri Töröne...
BMCBI
2010
115views more  BMCBI 2010»
13 years 11 months ago
Integration of multiple data sources to prioritize candidate genes using discounted rating system
Background: Identifying disease gene from a list of candidate genes is an important task in bioinformatics. The main strategy is to prioritize candidate genes based on their simil...
Yongjin Li, Jagdish Chandra Patra