TY  - JOUR
A1  - Nikoloski, Zoran
A1  - Grimbs, Sergio
A1  - Klie, Sebastian
A1  - Selbig, Joachim
T1  - Complexity of automated gene annotation
JF  - Biosystems : journal of biological and information processing sciences
N2  - Integration of high-throughput data with functional annotation by graph-theoretic methods has been postulated as promising way to unravel the function of unannotated genes. Here, we first review the existing graph-theoretic approaches for automated gene function annotation and classify them into two categories with respect to their relation to two instances of transductive learning on networks - with dynamic costs and with constant costs - depending on whether or not ontological relationship between functional terms is employed. The determined categories allow to characterize the computational complexity of the existing approaches and establish the relation to classical graph-theoretic problems, such as bisection and multiway cut. In addition, our results point out that the ontological form of the structured functional knowledge does not lower the complexity of the transductive learning with dynamic costs - one of the key problems in modern systems biology. The NP-hardness of automated gene annotation renders the development of heuristic or approximation algorithms a priority for additional research.
KW  - Complexity
KW  - Gene function prediction
KW  - External structural measures
KW  - Transductive learning
Y1  - 2011
U6  - https://doi.org/10.1016/j.biosystems.2010.12.003
SN  - 0303-2647
VL  - 104
IS  - 1
SP  - 1
EP  - 8
PB  - Elsevier
CY  - Oxford
ER  -