- Alexandre, P. A., Kogelman, L. J., Santana, M. H., Passarelli, D., Pulz, L. H., Fantinato-Neto, P., Silva, P. L., Leme, P. R., Strefezzi, R. F. & Coutinho, L. L. (2015). Liver transcriptomic networks reveal main biological processes associated with feed efficiency in beef cattle. BMC Genomics, 16(1), 1073.
- Chang, H.-H. & Mcgeachie, M. (2011). Phenotype prediction by integrative network analysis of SNP and gene expression microarrays. In: Proceedings of Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, 30 Aug - 03 Sep 2011, Boston Marriott Copley Place Hotel, Boston, MA, USA, pp. 6849-6852.
- Csardi, G. & Nepusz, T. (2006). The igraph software package for complex network research. Inter Journal, Complex Systems, 1695(5), 1-9.
- Elo, L. L., Lahti, L., Skottman, H., Kyläniemi, M., Lahesmaa, R. & Aittokallio, T. (2005). Integrating probe-level expression changes across generations of Affymetrix arrays. Nucleic Acids Research, 33(22), e193-e193.
- Fortes, M., Snelling, W., Reverter, A., Nagaraj, S., Lehnert, S., Hawken, R., Deatley, K., Peters, S., Silver, G. & Rincon, G. (2012). Gene network analyses of first service conception in Brangus heifers: use of genome and trait associations, hypothalamic-transcriptome information, and transcription factors. Journal of Animal Science, 90(9), 2894-2906.
- Friedman, N. (2004). Inferring cellular networks using probabilistic graphical models. Science, 303(5659), 799-805.
- Gene Expression Omnibus. (2017). NCBI: National Center for Biotechnology Information, Retrieved November 14, 2017, from https://www.ncbi.nlm.nih.gov/geo/
- Ghaderi-Zefrehei, M., Dolatabady, M. & Rowghani, E. (2015). Simple gene regulatory network of immune system candidate genes in dairy cattle. Research Opinions in Animal and Veterinary Sciences, 5(12), 499-506.
- Girard, A., Dufort, I., Douville, G. & Sirard, M. A. (2015). Global gene expression in granulosa cells of growing, plateau and atretic dominant follicles in cattle. Reproductive Biology and Endocrinology, 13(1), 17.
- Hageman, R. S., Leduc, M. S., Korstanje, R., Paigen, B. & Churchill, G. A. (2011). A Bayesian framework for inference of the genotype–phenotype map for segregating populations. Genetics, 187(4), 1163-1170.
- Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M. & Haussler, D. (2002). The human genome browser at UCSC. Genome Research, 12(6), 996-1006.
- Kogelman, L. J., Cirera, S., Zhernakova, D. V., Fredholm, M., Franke, L. & Kadarmideen, H. N. (2014). Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model. BMC Medical Genomics, 7(1), 57.
- Komolka, K., Ponsuksili, S., Albrecht, E., Kühn, C., Wimmers, K. & Maak, S. (2016). Gene expression profile of Musculus longissimus dorsi in bulls of a Charolais × Holstein F 2-cross with divergent intramuscular fat content. Genomics Data, 7131-133.
- Langfelder, P. & Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 9, 559.
- Liu, F., Zhang, S.-W., Guo, W.-F., Wei, Z.-G. & Chen, L. (2016). Inference of gene regulatory network based on local bayesian networks. PLoS Computational Biology, 12(8), e1005024.
- Malovini, A., Nuzzo, A., Ferrazzi, F., Puca, A. A. & Bellazzi, R. (2009). Phenotype forecasting with SNPs data through gene-based Bayesian networks. BMC Bioinformatics, 10(2), S7.
- Milchevskaya, V., Tödt, G. & Gibson, T. J. (2017). A Tool to Build Up-To-Date Gene Annotations for Affymetrix Microarrays. Genomics and Computational Biology, 3(2), 38.
- Nagarajan, R., Scutari, M. & Lèbre, S. (2013). Bayesian networks in R. Springer.
- Ramayo-Caldas, Y., Fortes, M. R. S., Hudson, N. J., Porto-Neto, L. R., Bolormaa, S., Barendse, W., Kelly, M., Moore, S. S., Goddard, M. E. & Lehnert, S. A. (2014). A marker-derived gene network reveals the regulatory role of, and in intramuscular fat deposition of beef cattle. Journal of Animal Science, 92(7), 2832-2845.
- Rebhan, M., Chalifa-Caspi, V., Prilusky, J. & Lancet, D. (1997). GeneCards: integrating information about genes, proteins and diseases. Trends in Genetics, 163(4), 13.
- Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D. A. & Nolan, G. P. (2005). Causal protein-signaling networks derived from multiparameter single-cell data. Science, 308(5721), 523-529
- Scutari, M. (2014). Bayesian network constraint-based structure learning algorithms: Parallel and optimised implementations in the bnlearn r package. arXiv preprint arXiv:1406.7648.
- Sherif, F. F., Zayed, N. & Fakhr, M. (2015). Discovering Alzheimer genetic biomarkers using Bayesian networks. Advances in bioinformatics, 2015.
- Stalteri, M. A. & Harrison, A. P. (2007). Interpretation of multiple probes sets mapping to the same gene in Affymetrix GeneChips. BMC Bioinformatics, 8(1), 13.
- Stingo, F. C., Swartz, M. D. & Vannucci, M. (2015). A Bayesian approach to identify genes and gene-level SNP aggregates in a genetic analysis of cancer data. Statistics and Its Interface, 8(2), 137-151.
- Verardo, L., Lopes, M., Wijga, S., Madsen, O., Silva, F., Groenen, M., Knol, E., Lopes, P. & Guimarães, S. (2016). After genome-wide association studies: Gene networks elucidating candidate genes divergences for number of teats across two pig populations. Journal of Animal Science, 94(4), 1446-1458.
- Weber, K. L., Welly, B. T., Van Eenennaam, A. L., Young, A. E., Porto-Neto, L. R., Reverter, A. & Rincon, G. (2016). Identification of gene networks for residual feed intake in Angus cattle using genomic prediction and RNA-seq. PloS One, 11(3), e0152274.
|
