The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight
Open accessAbstract
Systems biology approaches that are based on the genetics of gene expression have been fruitful in identifying genetic regulatory loci related to complex traits. We use microarray and genetic marker data from an F2 mouse intercross to examine the large-scale organization of the gene co-expression network in liver, and annotate several gene modules in terms of 22 physiological traits. We identify chromosomal loci (referred to as module quantitative trait loci, mQTL) that perturb the modules and describe a novel approach that integrates network properties with genetic marker information to model gene/trait relationships. Specifically, using the mQTL and the intramodular connectivity of a body weight-related module, we describe which factors determine the relationship between gene expression profiles and weight. Our approach results in the identification of genetic targets that influence gene modules (pathways) that are related to the clinical phenotypes of interest.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Subsequently, networks were modeled based on co-expression across a series of perturbations with the genes encoding the transcripts corresponding to the nodes and the correlations to the edges [67–69].”
From Multi-omics approaches to disease (2017) · cites this paper as [67]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
DAVID: Database for Annotation, Visualization, and Integrated Discovery
Dennis, G., Sherman, B. T., Hosack, D. A., Yang, J., Gao, W., Lane, H. C., & Lempicki, R. A. (2003) · Genome Biology · doi:10.1186/gb-2003-4-5-p3 · Cited by 9,327
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903
Error and attack tolerance of complex networks
Albert, R., Jeong, H., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35019019 · Cited by 7,534
Error and attack tolerance of complex networksPreprint
Albert, R., Jeong, H., & Barabási, A.-Ĺ. (2000) · Cited by 6,554
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684
Self-citationLethality and centrality in protein networks
Jeong, H., Mason, S., Barabási, A., & Oltvai, Z. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,327
The Large-Scale Organization of Metabolic Networks
Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35036627 · Cited by 4,972
Hierarchical Organization of Modularity in Metabolic Networks
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A.-L. (2002) · Science · doi:10.1126/science.1073374 · Cited by 4,224
From molecular to modular cell biology.
Hartwell, L. H., Hopfield, J. J., Leibler, S., & Murray, A. W. (1999) · Nature · doi:10.1038/35011540 · Cited by 3,632
Flagged first, then by citations
Cited by
Sorted by citations
Items 301–400 of 481
Kaisaki, P. J., Otto, G., Argoud, K., Collins, S. C., Wallis, R. H., Wilder, S. P., Yau, A. C. Y., Hue, C., Caldérari, S., Bihoreau, M., et al. (2016) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.033274 · Cited by 7
Feng, X., Zhang, M., Meng, J., Wang, Y., Liu, Y., Liang, C. Y., & Fan, S. (2020) · Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics · doi:10.3727/096504020x15791676105394 · Cited by 7
Gene correlation network analysis to identify regulatory factors in sciatic nerve injury
Li, L., Du, X., Ling, H., Li, Y., Wu, X., Jin, A., & Yang, M. (2021) · Journal of Orthopaedic Surgery and Research · doi:10.1186/s13018-021-02756-0 · Cited by 7
DISSECTION OF COMPLEX GENE EXPRESSION USING THE COMBINED ANALYSIS OF PLEIOTROPY AND EPISTASIS
Philip, V. M., Tyler, A. L., & Carter, G. W. (2013) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814583220_0021 · Cited by 7
Cluster and propensity based approximation of a network
Rañola, J. M. O., Langfelder, P., Lange, K. L., & Horvath, S. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-21 · Cited by 7
Self-citationIdentification of Key Modules and Hub Genes of Annulus Fibrosus in Intervertebral Disc Degeneration
Wang, H., Liu, W., Yu, B., Yu, X., & Chen, B. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2020.596174 · Cited by 7
Correlation and Gene Co-Expression Networks
Horvath, S. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8819-5_5 · Cited by 7
Self-citationHigh-dimensional data and linear models: a review
Brimacombe, M. B. (2014) · Open Access Medical Statistics · doi:10.2147/oams.s56499 · Cited by 7
Integrated genomics of susceptibility to alkylator-induced leukemia in mice
Cahan, P., & Graubert, T. A. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-638 · Cited by 8
Prioritizing hypothesis tests for high throughput data
Kim, S., & Schliekelman, P. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv608 · Cited by 7
Li, C., Mo, D., Li, M., Zheng, Y., Li, Q., Ou, S., & Zhang, Z. (2018) · Genes & Genetic Systems · doi:10.1266/ggs.17-00052 · Cited by 6
Sex Differences in the Age of Genetics
Arnold, A. P. (2009) · Elsevier eBooks · doi:10.1016/b978-008088783-8.00053-x · Cited by 7
Lusk, R., Saba, L. M., Vanderlinden, L. A., Zı́dek, V., Šilhavý, J., Pravenec, M., Hoffman, P. L., & Tabakoff, B. (2018) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.13763 · Cited by 7
oCEM: Automatic detection and analysis of overlapping co-expressed gene modules
Nguyen, Q., & Le, D. (2022) · BMC Genomics · doi:10.1186/s12864-021-08072-5 · Cited by 6
Zhong, J., & Li, X. (2022) · Land · doi:10.3390/land11112008 · Cited by 6
Bianchetti, G., Clouet, V., Legeai, F., Baron, C., Gazengel, K., Vu, B. L., Baud, S., To, A., Manzanares-Dauleux, M. J., Buitink, J., et al. (2024) · Physiologia Plantarum · doi:10.1111/ppl.14130 · Cited by 6
Dynamical properties of gene regulatory networks involved in long-term potentiation
Nido, G. S., Ryan, M. M., Beňušková, Ľ., & Williams, J. M. (2015) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2015.00042 · Cited by 6
Landry, A., Balas, M., Spears, J., & Zador, Z. E. (2019) · PLoS ONE · doi:10.1371/journal.pone.0220121 · Cited by 6
Transcriptome differences in adipose stromal cells derived from pre- and postmenopausal women
Xie, Y., Fang, B., Liu, W., Li, G., Huang, R., Zhang, L., He, J., Zhou, S., Liu, K., & Li, Q. (2020) · Stem Cell Research & Therapy · doi:10.1186/s13287-020-01613-x · Cited by 6
Gene Expression Signatures Identify Biologically Homogenous Subgroups of Grade 2 Meningiomas
Zador, Z. E., Landry, A., Saha, A., & Cusimano, M. D. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.541928 · Cited by 6
Integrated Weighted Correlation Network Analysis of Mouse Liver Gene Expression Data
Horvath, S. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8819-5_12 · Cited by 6
Self-citationHamdan, H. Z. (2024) · Placenta · doi:10.1016/j.placenta.2024.12.008 · Cited by 5
Enhancing the Functional Content of Eukaryotic Protein Interaction Networks
Pandey, G. S., Arora, S., Manocha, S., & Whalen, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0109130 · Cited by 5
Network reconstruction based on synthetic data generated by a Monte Carlo approach
Novine, M., Mattsson, C. C., & Groth, D. (2022) · Human Biology and Public Health · doi:10.52905/hbph2021.3.26 · Cited by 5
Network Medicine: New Paradigm in the -Omics Era
Guo, N. L. (2011) · Anatomy & Physiology · doi:10.4172/2161-0940.1000e106 · Cited by 5
Strategies for eQTL mapping in allopolyploid organisms
Fan, K.-H., Devos, K. M., & Schliekelman, P. (2020) · Theoretical and Applied Genetics · doi:10.1007/s00122-020-03612-1 · Cited by 6
Hou, J., Ye, X., Wang, Y., & Li, C. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.610087 · Cited by 4
Wu, B., Yu, J., Liu, Y., Dou, G., Hou, Y., Zhang, Z., Pan, X., Wang, H., Zhou, P., & Zhu, D. Z. (2021) · World Neurosurgery · doi:10.1016/j.wneu.2021.11.014 · Cited by 5
Ensemble Clustering for Biological Datasets
Pirim, H., & Şeker, Ş. E. (2012) · InTech eBooks · doi:10.5772/49956 · Cited by 5
Kang, E. Y., Shpitser, I., Ye, C., & Eskin, E. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02008-7_33 · Cited by 5
A Genetical Genomics Approach to Genome Scans Increases Power for QTL Mapping
Sun, G., & Schliekelman, P. (2011) · Genetics · doi:10.1534/genetics.110.123968 · Cited by 5
Zador, Z. E., Landry, A., Saha, A., & Cusimano, M. D. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/493031 · Cited by 5
Sreedasyam, A., Plott, C., Hossain, M. S., Lovell, J. T., Grimwood, J., Jenkins, J. W., Daum, C. G., Barry, K., Carlson, J. W., Shu, S., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.30.510380 · Cited by 5
WGCNA revisited: Module indetification
Yan, S., & Wu, G. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1955/1/012108 · Cited by 4
Lv, T., Zhang, B., Xu, X. Z., Jiang, C., Zheng, D., He, D., Zhou, Y., & Yang, J. (2023) · Human Genomics · doi:10.1186/s40246-023-00500-9 · Cited by 4
Self-citationChen, L., Guo, L., Yu, X., Huo, S., Hoffmann, A. A., Zhou, J., Sun, J., & Hong, X. (2024) · Molecular Ecology · doi:10.1111/mec.17521 · Cited by 4
Zehnalová, Š., Horak, Z., Kudělka, M., & Snåšel, V. (2013) · International Conference on Intelligent Networking and Collaborative Systems (INCoS) · doi:10.1109/incos.2013.44 · Cited by 4
Inferring Gene Regulatory Networks from Genetical Genomics Data
Liu, B., Hoeschele, I., & de la Fuente, A. (2010) · IGI Global eBooks · doi:10.4018/978-1-60566-685-3.ch004 · Cited by 4
Applying network and genetic analysis to the potato metabolome
Levina, A. V., Hoekenga, O. A., Gordin, M. L., Broeckling, C. D., & Jong, W. S. D. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1108351 · Cited by 4
Juigné, C., Becker, E., & Gondret, F. (2023) · BMC Genomics · doi:10.1186/s12864-023-09751-1 · Cited by 4
Processing Large-Scale, High-Dimension Genetic and Gene Expression Data
Molony, C. M., Sieberts, S. K., & Schadt, E. E. (2009) · Springer eBooks · doi:10.1007/978-3-540-69264-5_11 · Cited by 4
Self-citationPardy, C., & Wilson, S. C. (2011) · ANZIAM Journal · doi:10.21914/anziamj.v52i0.3959 · Cited by 4
Li, L., Yang, M., & Jin, A. (2020) · Medical Science Monitor · doi:10.12659/msm.925474 · Cited by 4
Xu, S., Yuan, H., Li, L., Bai, F., Yang, K., & Zhao, L. (2021) · Microbiology and Immunology · doi:10.1111/1348-0421.12926 · Cited by 4
Enhancing the functional content of protein interaction networksPreprint
Pandey, G. S., Manocha, S., Atluri, G., & Kumar, V. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1210.6912 · Cited by 4
Systems Approaches for Pharmacogenetics and Pharmacogenomics
Bader, J. S. (2008) · Pharmacogenomics · doi:10.2217/14622416.9.3.257 · Cited by 3
A network medicine approach to psychiatric genetics
Lasky-Su, J. A. (2013) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32191 · Cited by 2
Quantitative Trait Specific Differential Expression (qtDE)
Sarkar, M., & Majumder, A. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.02.131 · Cited by 3
Genes and Pathways Contributing to Obesity
Drake, T. A. (2010) · Progress in molecular biology and translational science · doi:10.1016/b978-0-12-375003-7.00002-9 · Cited by 3
Self-citationJha, M., Malhotra, A. G., Singh, S., & Pandey, K. M. (2016) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-016-0131-8 · Cited by 4
Ainsworth, H. F., & Cordell, H. J. (2016) · BMC Proceedings · doi:10.1186/s12919-016-0009-x · Cited by 3
Chen, X., Han, M., Li, Y., Li, X., Zhang, J., & Zhu, Y. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1082032 · Cited by 3
Locational memory of macrovessel vascular cells is transcriptionally imprinted
Spanjersberg, T. C. F., Oosterhoff, L. A., Kruitwagen, H. S., van den Dungen, N. A. M., Vernooij, J. C. M., Asselbergs, F. W., Mokrý, M., Spee, B., Harakaľová, M., & van Steenbeek, F. G. (2023) · Scientific Reports · doi:10.1038/s41598-023-38880-6 · Cited by 2
Systems Biology Approaches to Studying Diet x Genome Interactions
Yang, X., Tu, Z., & Zhu, J. (2011) · CRC Press eBooks · doi:10.1201/b11294-7 · Cited by 2
Networks and Fundamental Concepts
Horvath, S. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8819-5_1 · Cited by 3
Self-citationDiscovery Approaches to UPR in Athero-Susceptible Endothelium In Vivo
Civelek, M., Manduchi, E., Grant, G. R., Stoeckert, C. J., & Davies, P. F. (2011) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-385116-1.00007-8 · Cited by 3
Detection of Quantitative Trait Associated Genes Using Cluster Analysis
Jia, Z., Tang, S., Mercola, D., & Xu, S. (2008) · Springer eBooks · doi:10.1007/978-3-540-78757-0_8 · Cited by 3
Identifying Functional Modules Using MST-Based Weighted Gene Co-Expression Networks
Chanthaphan, A., Prom–on, S., Meechai, A., & Chan, J. (2009) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2009.35 · Cited by 2
Network based analysis of microarray gene expression profiles in response to electroacupuncture
Mohammadnejad, A., Li, S., Duan, H., & Tan, Q. (2019) · Journal of Traditional and Complementary Medicine · doi:10.1016/j.jtcme.2019.09.001 · Cited by 2
Fazli, M., Bertram, R., & Striegel, D. A. (2024) · Bulletin of Mathematical Biology · doi:10.1007/s11538-024-01335-8 · Cited by 3
He, L., Jiang, Z., Gao, Y., Zeng, Y., Ge, W., Yu, Y., & Xie, X. (2024) · Egyptian Journal of Medical Human Genetics · doi:10.1186/s43042-024-00574-7 · Cited by 2
Clustering Procedures and Module Detection
Horvath, S. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8819-5_8 · Cited by 2
Self-citationProtein Interactions for Functional Genomics
Mínguez, P., & Dopazo, J. (2012) · International Journal of Knowledge Discovery in Bioinformatics · doi:10.4018/ijkdb.2012100102 · Cited by 2
Discovery of Regulatory Mechanisms from Gene Expression Variation by eQTL Analysis
Przytycka, T. M., Zheng, J., & Huang, Y. (2009) · Chapman & Hall/CRC data mining and knowledge discovery series · doi:10.1201/9781420086850.ch10 · Cited by 2
Evaluating Whether a Module is Preserved in Another Network
Horvath, S. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8819-5_9 · Cited by 2
Self-citationZhou, Y., & You, M. (2019) · Oncotarget · doi:10.18632/oncotarget.26554 · Cited by 2
Functional homogeneity and specificity of topological modules in human proteome
Kaalia, R., & Rajapakse, J. C. (2019) · BMC Bioinformatics · doi:10.1186/s12859-018-2549-8 · Cited by 2
Understanding Tamoxifen Resistance of Breast Cancer Based on Integrative Bioinformatics Approaches
Dai, Y., & Huang, L. J. (2012) · Springer eBooks · doi:10.1007/978-1-4614-5647-6_14 · Cited by 2
DrGA: cancer driver gene analysis in a simpler manner
Nguyen, Q., Nguyen, T., & Le, D. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04606-0 · Cited by 2
Yam, P., VerHague, M., Albright, J., Gertz, E., de Villena, F. P., & Bennett, B. J. (2022) · Genes & Nutrition · doi:10.1186/s12263-022-00714-x · Cited by 2
GSGS: A Computational Framework to Reconstruct Signaling Pathways from Gene SetsPreprint
Acharya, L. R., Judeh, T., Duan, Z., Rabbat, M., & Zhu, D. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1101.3983 · Cited by 2
Li, M., Feng, X., Jian, Q., Jing, M., Wang, X., Cui, Z.-H., Zhang, R., Sun, J. G., Liu, Y., Liao, X., et al. (2025) · BMC Microbiology · doi:10.1186/s12866-025-04386-6 · Cited by 1
Xie, J., Zheng, S., Chen, J., Shi, Z., Chen, H. T., Wei, H., & Jiaen, Z. (2026) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2026.120351 · Cited by 1
D_CDF test of negative log transformed p-values with application to genetic pathway analysis
Dai, H. D., & Charnigo, R. J. (2014) · Statistics and Its Interface · doi:10.4310/sii.2014.v7.n2.a4 · Cited by 1
Large-Scale Regulatory Network Analysis from Microarray Data
Basu, A., & Sarkar, A. (2015) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-4666-6611-5.ch004 · Cited by 1
Mohammadnejad, A., Li, S., Duan, H., Lund, J., Li, W., Baumbach, J., & Tan, Q. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621258 · Cited by 1
Transcriptomic profiling identifies host-derived biomarker panels for assessing cerebral malaria
Asiedu, E., Larbi, A. A., Adankwah, E., Yambah, J. K., Sakyi, S. A., Kwarteng, E. V. S., Obiri‐Yeboah, D., & Kwarteng, A. (2022) · Gene Reports · doi:10.1016/j.genrep.2022.101650 · Cited by 1
VC‐PCR: A prediction method based on variable selection and clustering
Marion, R., Lederer, J., Goevarts, B., & von Sachs, R. (2024) · Statistica Neerlandica · doi:10.1111/stan.12358 · Cited by 1
Effects of hybridization and gene flow on gene co-expression networks
Runghen, R., & Bolnick, D. I. (2025) · Genetics · doi:10.1093/genetics/iyaf057 · Cited by 1
Linear covariance selection model via ℓ1-penalization
Bak, K., & Park, S. (2025) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2025.108176 · Cited by 2
TraitCorr as a workbench for correlating gene expression measurements with phenotypic data
Nußbaumer, T., Debnath, O., Wagner, C., & Heidari, P. (2020) · Gene Reports · doi:10.1016/j.genrep.2020.100649 · Cited by 1
Colorectal Cancer and Its Molecular Subsystems: Construction, Interpretation, and Validation
Patel, V. N., & Chance, M. R. (2013) · Wiley eBooks · doi:10.1002/9783527665471.ch7 · Cited by 1
Vaquero, A. R. (2013) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.5.2013.tde-28052013-102800 · Cited by 1
Genetics and genomics of social behaviour in a chicken modelPreprint
Johnsson, M., Henriksen, R., Fogelholm, J., Höglund, A., Jensen, P. H. K., & Wright, D. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/165167 · Cited by 1
“Skull base meningiomas have a distinct immune landscape.”Preprint
Zador, Z. E., Landry, A., Balas, M., & Cusimano, M. D. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/525444 · Cited by 1
Zheng, J., D’Amour, A., & Franks, A. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.07973 · Cited by 4
Li, B., Duan, H., Wang, S., Wu, J., & Li, Y. (2022) · Research Square · doi:10.21203/rs.3.rs-860927/v2 · Cited by 1
Self-citationMechanisms underlying the atherosclerosis risk associated with apolipoprotein E isoforms in humans
Altenburg, M. K. (2007) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/c6pc-dx08
Analysis of trans eSNPs infers regulatory network architecture
Kreimer, A. (2014) · PubMed · doi:10.7916/d8348hjr
Model selection procedures for high dimensional genomic data
Motyer, A., Galbraith, S., & Wilson, S. R. (2011) · ANZIAM Journal · doi:10.21914/anziamj.v52i0.3970
Trends and Statistical Challenges in Genomewide Association Studies
Sun, N., & Zhao, H. (2010) · doi:10.1002/9780470567647.ch12
AN INTEGRATIVE COMPUTATIONAL FRAMEWORK FOR DEFINING ASTHMA ENDOTYPES
Howrylak, J. A. (2013) · D-Scholarship@Pitt (University of Pittsburgh)
Miller, J. A., & Geschwind, D. H. (2008) · Wiley eBooks · doi:10.1002/9783527622467.ch1
Data mining techniques for enhancing protein function prediction
Kumar, V., & Pandey, G. S. (2010)
Integrated Genomics Of Susceptiblity To Therapy-Related Leukemia
Cahan, P. (2009) · Open Scholarship Institutional Repository (Washington University in St. Louis) · doi:10.7936/k7zg6q7v
Larsen, P. E., Almasri, E., Chen, G., & Dai, Y. D. (2008) · Springer eBooks · doi:10.1007/978-3-540-79450-9_40
Osherovich, L. Z. (2009) · Science-Business eXchange · doi:10.1038/scibx.2009.561
Can pharmacology possibly have a role for bioinformatics?
Whittaker, P. A. (2007) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2.2.271
Association Study between Gene Expression and Multiple Relevant Phenotypes with Cluster Analysis
Jia, Z., Wang, Y., Ye, K., Li, Q., Tang, S., Xu, S., & Mercola, D. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01184-9_1
Metadata
- Authors
- Ghazalpour, A., Doss, S., Zhang, B., Wang, S., Plaisier, C., Castellanos, R., Brozell, A., Schadt, E. E., Drake, T. A., Lusis, A. J., & Horvath, S.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Horvath, Steve
- ORCID
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2006-08-18
- Volume
- 2
- Issue
- 8
- Pages
- e130
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- OpenAlex
- W1973651601
- Cited by
- 462
- References
- 52
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 15 · 2023: 14 · 2022: 25 · 2021: 38 · 2020: 27 · 2019: 27 · 2018: 22 · 2017: 14 · 2016: 17 · 2015: 18 · 2014: 20 · 2013: 26 · 2012: 32
- FWCI
- 11.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (HL3 0568, HL28481, P01HL030568, P01HL028481)
- National Human Genome Research Institute (T32HG002536)
- National Institute of General Medical Sciences (T32GM007104, GM08234, GM 07104)
- National Institute of Allergy and Infectious Diseases (U19AI063603)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- mouse body weight, gene co-expression network, gene modules, hepatic gene expression, complex traits, systems biology
- MeSH terms
- Animals, Body Weight, Chromosome Mapping, Crosses, Genetic, Female, Liver, Mice, Inbred C3H, Mice, Inbred C57BL, Models, Genetic, Cluster Analysis, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide, Gene Expression Profiling, Quantitative Trait Loci, Mice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review