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Article overview
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Rank Projection Trees for Multilevel Neural Network Interpretation | | Jonathan Warrell
; Hussein Mohsen
; Mark Gerstein
; | | Date: |
1 Dec 2018 | | Abstract: | A variety of methods have been proposed for interpreting nodes in deep neural
networks, which typically involve scoring nodes at lower layers with respect to
their effects on the output of higher-layer nodes (where lower and higher
layers are closer to the input and output layers, respectively). However, we
may be interested in picking out a prioritized collection of subsets of the
inputs across a range of scales according to their importance for an output
node, and not simply a prioritized ranking across the inputs as singletons.
Such a situation may arise in biological applications, for instance, where we
are interested in epistatic effects between groups of genes in determining a
trait of interest. Here, we outline a flexible framework which may be used to
generate multiscale network interpretations, using any previously defined
scoring function. We demonstrate the ability of our method to pick out
biologically important genes and gene sets in the domains of cancer and
psychiatric genomics. | | Source: | arXiv, 1812.0172 | | Services: | Forum | Review | PDF | Favorites | |
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