Affiliations: Bioinformatics and Computational Biology; University
of Idaho, Moscow, ID 83843, USA | School of Molecular Biosciences; Washington State
University, Pullman, WA 99164, USA. E-mail:
[email protected], [email protected]
Note: [] Corresponding author
Abstract: A G2/M genetic network simulation is trained with tumor incidence
data from knockout experiments. The genetic network is implemented using a
neural network; knockout genotypes are simulated by removing nodes in the
neural network. Two analyses are used to interpret the resulting network
weights. We use a novel approach of fixing the network topology that allows
knockout TSG (tumor suppressor gene) data from multiple studies to overlap and
indirectly inform one another. The trained simulation is validated by
reproducing qualitative mammary cancer susceptibilities of ATM,
BRCA1, and p53 TSGs. The work described is valuable because it allows
TSG mammary cancer susceptibility to be quantified using genetic network
topology and in vivo knockout data.
