WIPER: Weighted in‐Path Edge Ranking for biomolecular association networks

Abstract

Background: In network biology researchers generate biomolecular networks with candidate genes or proteins experimentally-derived from high-throughput data and known biomolecular associations. Current bioinformatics research focuses on characterizing candidate genes/proteins, or nodes, with network characteristics, e.g., betweenness centrality. However, there have been few research reports to characterize and prioritize biomolecular associations ("edges"), which can represent gene regulatory events essential to biological processes. Method: We developed Weighted In-Path Edge Ranking (WIPER), a new computational algorithm which can help evaluate all biomolecular interactions/associations ("edges") in a network model and generate a rank order of every edge based on their in-path traversal scores and statistical significance test result. To validate whether WIPER worked as we designed, we tested the algorithm on synthetic network models. Results: Our results showed WIPER can reliably discover both critical "well traversed in-path edges", which are statistically more traversed than normal edges, and "peripheral in-path edges", which are less traversed than normal edges. Compared with other simple measures such as betweenness centrality, WIPER provides better biological interpretations. In the case study of analyzing postanal pig hearts gene expression, WIPER highlighted new signaling pathways suggestive of cardiomyocyte regeneration and proliferation. In the case study of Alzheimer's disease genetic disorder association, WIPER reports SRC:APP, AR:APP, APP:FYN, and APP:NES edges (gene-gene associations) both statistically and biologically important from PubMed co-citation. Conclusion: We believe that WIPER will become an essential software tool to help biologists discover and validate essential signaling/regulatory events from high-throughput biology data in the context of biological networks. Availability: The free WIPER API is described at the website (discovery.informatics.uab.edu/wiper/).

PAGER Web APP: An Interactive, Online Gene Set and Network Interpretation Tool for Functional Genomics

“…We ranked the pathways based on the ( Yue et al, 2019a ). As reported the highest PubMed score in Table 2 , the photodynamic therapy has been frequently reported for melanoma treatment in recent years ( Shivashankarappa and Sanjay, 2019 ; Turkoglu et al, 2019 ; Abramova et al, 2021 ; Yordi et al, 2021 ).…”

“…We applied the NCBI e-utils application programming interface (API) that implements semantic searches of PubMed abstracts to report biomedical literature citations ( Sayers, 2008 ). We implied that the likelihood of observing articles co-mentioning disease names and the keywords from pathways is statistically higher than random using the PubMed score ( Yue et al, 2019a ). In this study, the background citations using the word “disease” denoted as , the citations of the specific disease using the word “melanoma” represented as , the citations of the keywords from a pathway denoted as , and the joint citations of “melanoma” and the keywords from a pathway represented as .…”

Polar Gini Curve: A Technique to Discover Gene Expression Spatial Patterns from Single-Cell RNA-Seq Data

“…Meanwhile, to draw the curves, Polar Gini Curve would need to compute hundreds to thousands of Gini coefficients, which depends on the desired curve resolution, to characterize one gene in one cluster. Due to the long computational time, we were not able to create multiple simulations, which is the ideal approach, and run to compute the statistical [36] P value for the RMSD score. Therefore, we decided to reapply the estimation presented to compute the P value.…”

“…This could be done by dividing RMSD for each gene (as in Equation (3) ) by the largest RMSD among all genes in each cluster. Then, we applied the estimated P value calculation in [36] to assign a P value for each gene in each cluster. Briefly, we computed the mean µ and standard deviation σ of the 200 uniformly simulated RMSDs, such that the simulated cluster shapes, number of points, percentage of expressing genes are the same to the real gene-cluster.…”

Biological Network Mining

“…The biomedical community has created many well-established network analytics tools that are widely used by biomedical researchers, such as the weighted gene co-expression network analysis (WGCNA) [1] R package for weighted correlation network analysis. Moreover, new network analytic methods such as step-level differential response (SLDR) [2], Biomedical Entity Expansion, Ranking and Explorations (BEERE) [3], Weighted In-Path Edge Ranking (WIPER) [4], Weighted In-Network Node Expansion and Ranking (WINNER) [5] and distance-bounded energy-field minimization algorithm (DEMA) [6] are being constantly developed to open new opportunities to find more insights from biological networks. For example, WIPER is a recent method that allows a node ranking method (that is usually used to find vital biomolecules) to be directly applied to rank edges (i.e., interactions between biomolecules).…”