The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
eResponseNet: a package prioritizing candidate disease genes through cellular pathways
Abstract
Motivation: Although genome-wide association studies (GWAS) have found many common genetic variants associated with human diseases, it remains a challenge to elucidate the functional links between associated variants and complex traits. Results: We developed a package called eResponseNet by implementing and extending the existing ResponseNet algorithm for prioritizing candidate disease genes through cellular pathways. Using type II diabetes (T2D) as a study case, we demonstrate that eResponseNet outperforms currently available approaches in prioritizing candidate disease genes. More importantly, the package is instrumental in revealing cellular pathways underlying disease-associated genetic variations. Availability: The eResponseNet package is freely downloadable at http://hanlab.genetics.ac.cn/eResponseNet. Contact: jdhan@picb.ac.cn Supplementary Information: Supplementary data are available at Bioinformatics online.
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eResponseNet analysis identifies subnetworks with the largest information flow between the source and effect gene sets.
“we employ eResponseNet analysis,121 which identifies subnetworks with the largest information flow between the source and effect gene sets (STAR Methods).”
From Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators (2024) · cites this paper as [121]
Citations
Cites
Human genetic variation and its contribution to complex traits
Frazer, K. A., Murray, S. S., Schork, N. J., & Topol, E. J. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2554 · Cited by 1,073
A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk
Cardiogenics Consortium, Heinig, M., Petretto, E., Wallace, C., Bottolo, L., Rotival, M., Lu, H., Li, Y., Sarwar, R., Langley, S. R., et al. (2010) · Nature · doi:10.1038/nature09386 · Cited by 283
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Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators
Tao, W., Yu, Z., & Han, J.-D. J. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.03.009 · Cited by 147
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Metadata
- Authors
- Huang, J., Liu, Y., Zhang, W., Yu, H., & Han, J.-D. J.
- Affiliations
- Chinese Academy of Sciences (CN)
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-06-23
- Volume
- 27
- Issue
- 16
- Pages
- 2319-2320
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2101017957
- Cited by
- 11
- References
- 2
- Citations by year
- 2024: 1 · 2021: 1 · 2020: 1 · 2019: 1 · 2017: 1 · 2015: 1 · 2014: 1 · 2013: 3 · 2012: 1
- FWCI
- 0.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- candidate gene prioritization, cellular pathways, type 2 diabetes, genetic variants, complex traits, R package
- MeSH terms
- Algorithms, Diabetes Mellitus, Type 2, Disease, Genes, Humans, Software, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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