Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Partition Decoupling for Multi-gene Analysis of Gene Expression
  Profiling Data   We present the extention and application of a new unsupervised statistical
learning technique--the Partition Decoupling Method--to gene expression data.
Because it has the ability to reveal non-linear and non-convex geometries
present in the data, the PDM is an improvement over typical gene expression
analysis algorithms, permitting a multi-gene analysis that can reveal
phenotypic differences even when the individual genes do not exhibit
differential expression. Here, we apply the PDM to publicly-available gene
expression data sets, and demonstrate that we are able to identify cell types
and treatments with higher accuracy than is obtained through other approaches.
By applying it in a pathway-by-pathway fashion, we demonstrate how the PDM may
be used to find sets of mechanistically-related genes that discriminate
phenotypes.

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The categories of this paper are: chem-ph
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The categories of this paper are: nlin
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The categories of this paper are: supr-con
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The categories of this paper are: q-fin
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: chao-dyn
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The categories of this paper are: math
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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci
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The categories of this paper are: cs
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The categories of this paper are: hep-ex