A multi-layer refined network model for the identification of essential proteins
CoRR(2023)
摘要
The identification of essential proteins in protein-protein interaction
networks (PINs) can help to discover drug targets and prevent disease. In order
to improve the accuracy of the identification of essential proteins,
researchers attempted to obtain a refined PIN by combining multiple biological
information to filter out some unreliable interactions in the PIN.
Unfortunately, such approaches drastically reduce the number of nodes in the
PIN after multiple refinements and result in a sparser PIN. It makes a
considerable portion of essential proteins unidentifiable. In this paper, we
propose a multi-layer refined network (MR-PIN) that addresses this problem.
Firstly, four refined networks are constructed by respectively integrating
different biological information into the static PIN to form a multi-layer
heterogeneous network. Then scores of proteins in each network layer are
calculated by the existing node ranking method, and the importance score of a
protein in the MR-PIN is evaluated in terms of the geometric mean of its scores
in all layers. Finally, all nodes are sorted by their importance scores to
determine their essentiality. To evaluate the effectiveness of the multi-layer
refined network model, we apply 16 node ranking methods on the MR-PIN, and
compare the results with those on the SPIN, DPIN and RDPIN. Then the predictive
performances of these ranking methods are validated in terms of the
identification number of essential protein at top100 - top600, sensitivity,
specificity, positive predictive value, negative predictive value, F-measure,
accuracy, Jackknife, ROCAUC and PRAUC. The experimental results show that the
MR-PIN is superior to the existing refined PINs in the identification accuracy
of essential proteins.
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