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2018 ; 34
(13
): i484-i493
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Classifying tumors by supervised network propagation
#MMPMID29949979
Zhang W
; Ma J
; Ideker T
Bioinformatics
2018[Jul]; 34
(13
): i484-i493
PMID29949979
show ga
MOTIVATION: Network propagation has been widely used to aggregate and amplify the
effects of tumor mutations using knowledge of molecular interaction networks.
However, propagating mutations through interactions irrelevant to cancer leads to
erosion of pathway signals and complicates the identification of cancer subtypes.
RESULTS: To address this problem we introduce a propagation algorithm,
Network-Based Supervised Stratification (NBS2), which learns the mutated
subnetworks underlying tumor subtypes using a supervised approach. Given an
annotated molecular network and reference tumor mutation profiles for which
subtypes have been predefined, NBS2 is trained by adjusting the weights on
interaction features such that network propagation best recovers the provided
subtypes. After training, weights are fixed such that mutation profiles of new
tumors can be accurately classified. We evaluate NBS2 on breast and glioblastoma
tumors, demonstrating that it outperforms the best network-based approaches in
classifying tumors to known subtypes for these diseases. By interpreting the
interaction weights, we highlight characteristic molecular pathways driving
selected subtypes. AVAILABILITY AND IMPLEMENTATION: The NBS2 package is freely
available at: https://github.com/wzhang1984/NBSS. SUPPLEMENTARY INFORMATION:
Supplementary data are available at Bioinformatics online.
free
free
free
