Publications

How to cite GeneWeaver

Erich J. Baker, Jeremy J. Jay, Jason A. Bubier, Michael A. Langston, and Elissa J. Chesler. GeneWeaver: a web-based system for integrative functional genomics. Nucleic Acids Research; (2012) 40(D1): D1067-D1076

Publications Describing GeneWeaver

• Erich J. Baker, Jeremy J. Jay, Jason A. Bubier, Michael A. Langston, and Elissa J. Chesler. GeneWeaver: a web-based system for integrative functional genomics. Nucleic Acids Research; (2012) 40(D1): D1067-D1076.

• Jason A. Bubier, Michael A. Langston, Erich J. Baker, and Elissa J. Chesler. Integrative functional genomics for systems genetics in GeneWeaver.org. Methods Mol Biol.; (2017). 1488:131-152.

• Jeremy J. Jay; Cross species integration of functional genomics experiments. International Review of Neurobiology; (2012) 104:1-24.

• Jeremy J. Jay, John D. Eblen, Yun Zhang, Mikael Benson, Andy D. Perkins, Arnold M. Saxton, Brynn H. Voy and Elissa J. Chesler and Michael A. Langston; A systematic comparison of genome-scale clustering algorithms. BMC Bioinformatics; (2012) Jun 25;13 Supplement 10:S7.

• Erich J. Baker, Jeremy J. Jay, Vivek M. Philip, Yun Zhang, Zuopan Li, Roumyana Kirova, Michael A. Langston and Elissa J. Chesler; Ontological Discovery Environment: a system for integrating gene-phenotype associations. Genomics; (2009) Dec;94(6):377-87.

Other Relevant GeneWeaver Citations

• Ahmadi Adl, A., & Qian, X. (2015). Tumor stratification by a novel graph-regularized bi-clique finding algorithm. Computational Biology and Chemistry, 57, 3-11. 10.1016/j.compbiolchem.2015.02.010

• Baker, E.J. (2012). Biological databases for behavioral neurobiology. International Review of Neurobiology, 103:19-38.

• Baker, E., Culpepper, C., Philips, C., Bubier, J., Langston, M., & Chesler, E. (2014). Identifying common components across biological network graphs using a bipartite data model.- BMC Proceedings; Oct 13; 8.

• Baker E., Bubier J.A., Reynolds T., Langston M.A., & Chesler E.J. (2016). GeneWeaver: data driven alignment of cross-species genomics in biology and disease. Nucleic Acids Res., 44(D1):D555-9.

• Bettembourg, C., Diot, C., & Dameron, O. (2014). Semantic particularity measure for functional characterization of gene sets using gene ontology. PLoS ONE, 9:e86525.

• Bhandari, P., Hill, J.S., Farris ,S.P., Costin, B., Martin, I., Chan, C-L., Alaimo, J.T., Bettinger, J.C., Davies, A.G., Miles, M.F., & Grotewiel, M. (2012). Chloride intracellular channels modulate acute ethanol behaviors in Drosophila, Caenorhabditis elegans and mice. Genes, Brain, and Behavior; Jun; 11(4):387-97.

• Bhattacharyya, T., Gregorova, S., Mihola, O., Anger, M., Sebestova, J., Denny, P., Simecek, P., & Forejt, J. (2013). Mechanistic basis of infertility of mouse intersubspecific hybrids. PNAS; 2013 110 (6) E468-E477.

• Bubier, J.A., & Chesler, E.J. (2012). Accelerating discovery for complex neurological and behavioral disorders through systems genetics and integrative genomics in the laboratory mouse. Neurotherapeutics; Apr; 9(2):338-48.

• Bubier, J.A., Jay, J.J., Baker, C.L., Bergeson , S.E., Ohno, H., Metten, P., Crabbe, J.C., & Chesler, E.J. (2014). Identification of a QTL in Mus musculus for Alcohol Preference, Withdrawal, and Ap3m2 Expression Using Integrative Functional Genomics and Precision Genetics- Genetics; Jun 11;197(4):1377-1393.

• Bubier, J.A., & Chesler, E.J. (2015). GeneWeaver: finding consilience in heterogeneous cross-species functional genomics data. -' Mammalian Genome'; Oct 26.

• Cesar, A.S., Regitano, L.C., Mourão, G.B., Tullio, R.R., Lanna, D.P., Nassu, R.T., Mudado, M.A., Oliveira, P.S., do Nascimento, M.L., Chaves, A.S., Alencar, M.M., Sonstegard, T.S., Garrick, D.J., Reecy, J.M., & Coutinho, L.L. (2014). Genome-wide association study for intramuscular fat deposition and composition in Nellore cattle.- BMC Genetics; Mar 25; 15:39.

• Chan, R.F. (2013). Genetic analysis of ethanol sensitivity and tolerance in drosophila. Master's Thesis.

• Chen, H.C., Zou, W., Lu, T.P., & Chen, J.J. (2014). A composite model for subgroup identification and prediction via bicluster analysis. PLoS ONE , 9 (e111318). 10.1371/journal.pone.0111318

• Chesler, E.J., Plitt, A., Fisher, D., Hurd, B., Lederle, L., Bubier, J.A., Kiselycznyk, C., & Holmes, A. (2012). Quantitative trait loci for sensitivity to ethanol intoxication in a C57BL/6J×129S1/SvImJ inbred mouse cross. Mammalian Genome; Jun;23(5-6):305-21.

• Chesler, E.J., Logan, R.W. (2013). Opportunities for bioinformatics in the classification of behavior and psychiatric disorders- International Review of Neurobiology; 104:183-211

• Devera, S.M., Costina, B.N., Xub, R., El-Hagea, N., Balinang, J., Samoshkin, A., ... Hausera, K.F. (2014). Differential expression of the alternatively spliced OPRM1 isoform μ -opioid receptor-1K in HIV-infected individuals'. AIDS, 28, 19-30. 10.1097/QAD.0000000000000113

• Eblen, J.D., Phillips, C.A., Rogers, G.L., & Langston, M.A. (2012). The maximum clique enumeration problem: algorithms, applications, and implementations.- BMC Bioinformatics; Jun 25;13 Suppl 10:S5.

• Farris, S.P. (2016).Moving toward understanding the proteome involved in substance abuse. Biological Psychiatry, 79 , 422-424. 10.1016/j.biopsych.2016.01.003

• Groshaus, M., & Montero, L. (2016). Tight lower bounds on the number of bicliques in false-twin-free graphs. Theoretical Computer Science, 636, 77-84. 10.1016/j.tcs.2016.05.027

• Grubb, S.C., Bult, C.J., & Bogue, M.A. (2014). Mouse Phenome Database.Nucleic Acids Research; 42:D825-D834.

• Ha, T.J., Swanson, D.J., Kirova, R., Yeung, J., Choi, K., Tong, Y., Chesler, E.J., & Goldowitz, D. (2012). Genome-wide microarray comparison reveals downstream genes of Pax6 in the developing mouse cerebellum.- European Journal of Neuroscience; Oct 36(7); 2888-98.

• Haendel, M.A., & Chesler, E.J. (2012). Lost and found in behavioral informatics.- International Review of Neurobiology; 103:1-18.

• Hait, N.C., Wise, L.E., Allegood, J.C., O'Brien, M., Avni, D., Reeves, T.M., Knapp, P.E., Lu, J., Luo, C., Miles, M.F., Milstien, S., Lichtman, A.H., & Spiegel, S. (2014). Active, phosphorylated fingolimod inhibits histone deacetylases and facilitates fear extinction memory.' Nature Neuroscience.'

• Han, B.W., & Chen, Y.Q. (2013). Potential pathological and functional links between long noncoding RNAs and hematopoiesis.' Science Signaling'; 6.

• Jay, J.J., & Chesler, E.J. (2014). Performing integrative functional genomics analysis in GeneWeaver.org.' Methods in Molecular Biology'; pp 13-29.

• Jegga, A.G. (2014). Candidate gene discovery and prioritization in rare diseases.Methods in Molecular Biology; 1168:295-312.

• Kang, H., Choi, I., Cho, S., Ryu, D., Lee, S., & Kim, W. (2014). GsGator: An integrated web platform for cross-species gene set analysis. BMC Bioinformatics, 15 (13). 10.1186/1471-2105-15-13

• Kershenbaum, A., Cutillo, A., Darabos, C., Murray, K., Schiaffino, R., & Moore, J.H. (2016). Bicliques in graphs with correlated edges: From artificial to biological networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (138-155). 10.1007/978-3-319-31204-0_10

• Logan, R.W., Robledo, R.F., Recla, J.M., Philip, V.M., Bubier, J.A., Jay, J.J., Harwood, C., Wilcox, T., Gatti, D.M., Bult, C.J., Churchill, G.A., & Chesler, E.J. (2013). High-precision genetic mapping of behavioral traits in the diversity outbred mouse population- Genes, Brain and Behavior; Jun; 12(4):424-37.

• Louati, A., Haddad, J.E., & Pinson, S. (2015). A multi-agent approach for trust-based service discovery and selection in social networks. Scalable Computing, 16, 381-402. doi: 10.12694/scpe.v16i4.1129

• McWhite, C.D., Liebeskind, B.J., & Marcotte, E.M. (2015). Applications of comparative evolution to human disease genetics. Current Opinion in Genetics and Development, 35, 16-24. doi: 10.1016/j.gde.2015.08.004

• Meehan, T.F., Carr, C.J., Jay, J.J., Bult, C.J., Chesler, E.J., & Blake, J.C. (2011). Autism candidate genes via mouse phenomics. Journal of Biomedical Informatics; Dec; 44 Supplement 1:S5-11.

• Mooney, M., & McWeeney, S. (2014). Data integration and reproducibility for high-throughput transcriptomics. In International Review of Neurobiology (55-71). doi: 10.1016/B978-0-12-801105-8.00003-5

• Nestor, C.E., Barrenäs, F., Wang, H., Lentini, A., Zhang, H., Bruhn, S., Jörnsten, R., Langston, M.A., Rogers, G., Gustafsson, M., & Benson, M. (2014). DNA methylation changes separate allergic patients from healthy controls and may reflect altered CD4+ T-cell population structure- PLoS Genetics; Jan;10(1):e1004059.

• O'Brien, M.A., Costin, B.N., & Miles, M.F. (2012). Using genome-wide expression profiling to define gene networks relevant to the study of complex traits: from RNA integrity to network topology.- International Review of Neurobiology;104:91-133.

• Oren, Y., Nachshon, A., Frishberg, A., Wilentzik, R., & Gat-Viks, I. (2015). Linking traits based on their shared molecular mechanisms. eLife , 2015 (e04346). 10.7554/eLife.04346

• Padula, A.E., Griffin, W.C. 3rd, Lopez, M.F., Nimitvilai, S., Cannady, R., McGuier, N.S., Chesler, E.J., Miles, M.F., Williams, R.W., Randall, P.K., Woodward, J.J., Becker, H.C., & Mulholland, P.J. (2015). KCNN Genes that Encode Small-Conductance Ca2+-Activated K+ Channels Influence Alcohol and Drug Addiction.- Neuropsychopharmacology; Feb 9.

• Recla, J.M., Robledo, R.F., Gatti, D.M., Bult, C.J., Churchill, G.A., & Chesler, E.J. (2014). Precise genetic mapping and integrative bioinformatics in Diversity Outbred mice reveals Hydin as a novel pain gene.' Mammalian Genome'; 25:211-222.

• Rojas-Muñoz, A., Maurya, M.R., Lo, F., & Willems, E. (2014). Integrating omics into the cardiac differentiation of human pluripotent stem cells Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 6, 247-264. doi: 10.1002/wsbm.1268

• Roth, A., Kyzar, E.J., Cachet, J., Stewart, A.M., Green, J., Gaikwad, S., O'Leary, T.P., Tabakoff, B., Brown, R.E., & Kalueff, A.V. (2013). Potential translational targets revealed by linking mouse grooming behavioral phenotypes to gene expression using public databases. Progress in Neuro-Psychopharmacology & Biological Psychiatry; 40:313-325.

• Saccone, S.F. (2012). Applying in silico integrative genomics to genetic studies of human disease. In International Review of Neurobiology (133-156). doi: 10.1016/B978-0-12-388408-4.00007-1

• Smith, C.L., & Eppig, J.T. (2012). The Mammalian Phenotype Ontology as a unifying standard for experimental and high-throughput phenotyping data. Mammalian Genome, 23, 653-668. doi: 10.1007/s00335-012-9421-3

• Talishinsky, A., & Rosen, G.D. (2012). Systems Genetics of the Lateral Septal Nucleus in Mouse: Heritability, Genetic Control, and Covariation with Behavioral and Morphological Traits. PLoS ONE; 7(8): e44236.

• Tsiliki, G., Tsaramirsis, K., & Kossida, S. (2014). AmalgamScope: merging annotations data across the human genome.- BioMed Research International.; Epub 2014 May 20.

• Van Steen, K., & Malats, N. (2014). Perspectives on data integration in human complex disease analysis. In Big Data Analytics in Bioinformatics and Healthcare (284-322). 10.4018/978-1-4666-6611-5.ch013

• Williams, R.W. (2011). Genetic and genomic web resources for research on alcohol use and abuse. Alcohol Research and Health, 34 , 378-380.

• Williams, R.W., & Mulligan, M.K. (2012). Genetic and molecular network analysis of behavior. International Review of Neurobiology; 104:135-57.

• Wiltshire, T., Ervin, R.B., Duan, H., Bogue, M.A., Zamboni, W.C., Cook, S., Chung, W., Zou, F., & Tarantino, L.M. (2015). Initial locomotor sensitivity to cocaine varies widely among inbred mouse strains. - Genes, Brain and Behavior, 14, 271-280. 10.1111/gbb.12209

• Wittkop, T., TerAvest, E., Evani, U.S., Fleisch, K.M., Berman, A.E., Powell, C., Shah, N.H., & Mooney, S.D. (2013). STOP using just GO: A multi-ontology hypothesis generation tool for high throughput experimentation. BMC Bioinformatics, 14.

• Zhang, Y., Phillips, C.A., Rogers, G.L., Baker, E.J., Chesler, E.J., & Langston, M.A. (2014). On finding bicliques in bipartite graphs: a novel algorithm and its application to the integration of diverse biological data types. BMC Bioinformatics, 15.

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