A modified PageRank algorithm for biological pathway ranking

Zhang, Qingyang

doi:10.1002/sta4.204

Cited by 3 publications

(1 citation statement)

References 27 publications

(31 reference statements)

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“…Although Google has updated this algorithm to increase its accuracy, we describe the original PR algorithm with which we implemented our bioinformatic tool UPEFinder. The mathematical foundation of PR has been used in other research fields, , including molecular biology applications for ranking genes, proteins, functions, and biological pathways in the analysis of high-throughput experiments − and the prediction of protein functions using the analysis of protein–protein interaction networks. − Moreover, several implementations are available in different programming languages optimized to score the nodes of huge graphs extremely fast and accurately…”

Section: Methodsmentioning

confidence: 99%

UPEFinder: A Bioinformatic Tool for the Study of Uncharacterized Proteins Based on Gene Expression Correlation and the PageRank Algorithm

et al. 2020

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The Human Proteome Project (HPP) is leading the international effort to characterize the human proteome. Although the main goal of this project was first focused on the detection of missing proteins, a new challenge arose from the need to assign biological functions to the uncharacterized human proteins and describe their implications in human diseases. Not only the proteins with experimental evidence (uPE1 proteins) but also the uncharacterized missing proteins (uMPs) were the objects of study in this challenge, neXt-CP50. In this work, we developed a new bioinformatic approach to infer biological annotations for the uPE1 proteins and uMPs based on a “guilt-by-association” analysis using public RNA-Seq data sets. We used the correlation of these proteins with the well-characterized PE1 proteins to construct a network. In this way, we applied the PageRank algorithm to this network to identify the most relevant nodes, which were the biological annotations of the uncharacterized proteins. All of the generated information was stored in a database. In addition, we implemented the web application UPEFinder () to facilitate the access to this new resource. This information is especially relevant for the researchers of the HPP who are interested in the generation and validation of new hypotheses about the functions of these proteins. Both the database and the web application are publicly available ().

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Section: Methodsmentioning

confidence: 99%

UPEFinder: A Bioinformatic Tool for the Study of Uncharacterized Proteins Based on Gene Expression Correlation and the PageRank Algorithm

et al. 2020

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Systems Pharmacology: Enabling Multidimensional Therapeutics

Maudsley¹,

Leysen²,

Gastel³

et al. 2022

Comprehensive Pharmacology

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Network-adjusted Kendall’s Tau Measure for Feature Screening with Application to High-dimensional Survival Genomic Data

Wang

Chen

2021

Bioinformatics

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Motivation In high-dimensional genetic/genomic data, the identification of genes related to clinical survival trait is a challenging and important issue. In particular, right-censored survival outcomes and contaminated biomarker data make the relevant feature screening difficult. Several independence screening methods have been developed, but they fail to account for gene–gene dependency information, and may be sensitive to outlying feature data. Results We improve the inverse probability-of-censoring weighted (IPCW) Kendall’s tau statistic by using Google’s PageRank Markov matrix to incorporate feature dependency network information. Also, to tackle outlying feature data, the nonparanormal approach transforming the feature data to multivariate normal variates are utilized in the graphical lasso procedure to estimate the network structure in feature data. Simulation studies under various scenarios show that the proposed network-adjusted weighted Kendall’s tau approach leads to more accurate feature selection and survival prediction than the methods without accounting for feature dependency network information and outlying feature data. The applications on the clinical survival outcome data of diffuse large B-cell lymphoma and of The Cancer Genome Atlas lung adenocarcinoma patients demonstrate clearly the advantages of the new proposal over the alternative methods. Supplementary information Supplementary data are available at Bioinformatics online.

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A modified PageRank algorithm for biological pathway ranking

Cited by 3 publications

References 27 publications

UPEFinder: A Bioinformatic Tool for the Study of Uncharacterized Proteins Based on Gene Expression Correlation and the PageRank Algorithm

UPEFinder: A Bioinformatic Tool for the Study of Uncharacterized Proteins Based on Gene Expression Correlation and the PageRank Algorithm

Systems Pharmacology: Enabling Multidimensional Therapeutics

Network-adjusted Kendall’s Tau Measure for Feature Screening with Application to High-dimensional Survival Genomic Data

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