ArticleComparative and functional genomics2011
Applications of different weighting schemes to improve pathway-based analysis.
Article in Comparative and functional genomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.PLoS computational biology · 2021Article
- Gene Expression of Proresolving Lipid Mediator Pathways Is Associated With Clinical Outcomes in Trauma Patients.Critical care medicine · 2015Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Conventionally, pathway-based analysis assumes that genes in a pathway equally contribute to a biological function, thus assigning uniform weight to genes. However, this assumption has been proved incorrect, and applying uniform weight in the pathway analysis may not be an appropriate approach for the tasks like molecular classification of diseases, as genes in a functional group may have different predicting power. Hence, we propose to use different weights to genes in pathway-based analysis and devise four weighting schemes. We applied them in two existing pathway analysis methods using both real and simulated gene expression data for pathways. Among all schemes, random weighting scheme, which generates random weights and selects optimal weights minimizing an objective function, performs best in terms of P value or error rate reduction. Weighting changes pathway scoring and brings up some new significant pathways, leading to the detection of disease-related genes that are missed under uniform weight.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.