P-Finder: Reconstruction of Signaling Networks from Protein-Protein Interactions and GO Annotations

Abstract

Because most complex genetic diseases are caused by defects of cell signaling, illuminating a signaling cascade is essential for understanding their mechanisms. We present three novel computational algorithms to reconstruct signaling networks between a starting protein and an ending protein using genome-wide protein-protein interaction (PPI) networks and gene ontology (GO) annotation data. A signaling network is represented as a directed acyclic graph in a merged form of multiple linear pathways. An advanced semantic similarity metric is applied for weighting PPIs as the preprocessing of all three methods. The first algorithm repeatedly extends the list of nodes based on path frequency towards an ending protein. The second algorithm repeatedly appends edges based on the occurrence of network motifs which indicate the link patterns more frequently appearing in a PPI network than in a random graph. The last algorithm uses the information propagation technique which iteratively updates edge orientations based on the path strength and merges the selected directed edges. Our experimental results demonstrate that the proposed algorithms achieve higher accuracy than previous methods when they are tested on well-studied pathways of S. cerevisiae. Furthermore, we introduce an interactive web application tool, called P-Finder, to visualize reconstructed signaling networks.

Modeling a Gene Structure Behavior Analysis based on the Correlation Ontology

“…The issue of annotation in the work of (G. Yu, G. Fu, J. Wang and Y. Zhao, 2018), I s handled by a predictive model for the annotation ontology using a graph method for encoding [19]. The issue of the networks signalling is widely studied by (Y. Cho, Y. Xin and G. Speegle, 2015) by presenting the network of signal using directed acyclic graph (DAG) as well developed a web-based tool for the visualization of the signaling network [20].The collaborative gene regulation is studied by ( J. Luo, G. Xiang and C. Pan, 2017) by using a optimized method of matrix factorization to combine varied expression profile and introduces a penalty factor to minimize sparsity using mathematical model [21]. The use of larger non coded RNAs for prediction of important function is represented as a network in the work of (Z. Zhang, J. Zhang, C. Fan, Y. Tang and L. Deng, 2019), for three different and heterogenous networks by computation of the similarity and its effectiveness is validated against the conventional methods of that time using F-score [22].…”

“…Approach [19] Predictive modeling for annotation ontology assessment [20] Approach to visualize signaling network [21] Minimizing sparsity based on mathematical approach [22] Predictive model for important functions of proteins behaviour [23] Method to analysis HIV infection [24] Assessment of gene expression data…”

Network Analyses in Plant Pathogens

“…Signaling is a series of chemical and/or energetic transmissions from an external stimulus to the cell. Signaling networks reconstruct the interaction path of the signal-carrying elements (usually proteins) to the organelle that requires the decision of maintaining or changing a state of homeostasis (Cho et al, 2015 ). These networks are also represented in a directed manner (Figure 1A ) and with highly conserved and specific topologies.…”

“…Usually, these types of networks also include transcription factors and PPIN to reconstruct the signaling cascade. The most likely edges of signal conduction are also weighted by the strongest or most reliable directed path (Cho et al, 2015 ). From the computational point of view, the weighting of the vertices constitutes a great challenge due to the inherent subjectivity of this process.…”

Alignment of PPI Networks Using Semantic Similarity for Conserved Protein Complex Prediction

“…Over the last decade, the major focus in the new field of computational systems biology has been on studying modularity of PPI networks in a broad range of resolution. A variety of computational approaches have been presented to identify functional modules, i.e., protein complexes and signaling pathways as specific terms, from genome-wide PPI networks [2][3] [4].…”