Archives

  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • 2021-03
  • br Barat A Ruskin H J Comparative correlation structure of

    2019-10-14


    Barat, A., & Ruskin, H. J. (2015). Comparative correlation structure of colon can-cer locus specific methylation: Characterisation of patient profiles and potential markers across 3 array-based datasets. Journal of Cancer, 6(8), 795.
    Chakraborty, S. (2014). In silico analysis identifies genes common between five pri-mary gastrointestinal cancer sites with potential clinical applications. Annals of Gastroenterology: Quarterly Publication of the Hellenic Society of Gastroenterology, 27(3), 231.
    Coley, D. A. (1999). An introduction to genetic algorithms for scientists and engineers.
    World Scientific Publishing Company.
    Ding, C., & Peng, H. (2005). Minimum redundancy feature selection from microarray gene EPZ-6438 data. Journal of Bioinformatics and Computational Biology, 3(02), 185–205.
    García, V., Sánchez, J., Cleofas-Sánchez, L., Ochoa-Domínguez, H., & López-Orozco, F. (2017). An insight on the large g, small nproblem in gene– expression microarray classification. In Proceedings of the Iberian conference on pattern recognition and image analysis (pp. 483–490). Springer.
    García, V., & Sánchez, J. S. (2015). Mapping microarray gene expression data into dissimilarity spaces for tumor classification. Information Sciences, 294, 362–375.
    González, F., & Belanche, L. A. (2013). Feature selection for microarray gene expres-sion data using simulated annealing guided by the multivariate joint entropy. arXiv:1302.1733,.
    Han, B., Lai, H., Xie, R., Li, L., & Zhu, L. (2014). Identification of glioma cancer-alerted gene markers based on a diagnostic outcome correlation analysis preferential approach. International Journal of Data Mining and Bioinformatics, 9(1), 67–88.
    Jirapech-Umpai, T., & Aitken, S. (2005). Feature selection and classification for mi-croarray data analysis: Evolutionary methods for identifying predictive genes. BMC Bioinformatics, 6(1), 148.
    Lundemo, A. G., Pettersen, C. H., Berge, K., Berge, R. K., & Schønberg, S. A. (2011). Tetradecylthioacetic acid inhibits proliferation of human sw620 colon cancer cells-gene expression profiling implies endoplasmic reticulum stress. Lipids in Health and Disease, 10(1), 190.
    Luque-Baena, R., Urda, D., Subirats, J., Franco, L., & Jerez, J. (2013). Analysis of can-cer microarray data using constructive neural networks and genetic algorithms. In Proceedings of the IWBBIO international work-conference on bioinformatics and biomedical engineering (pp. 55–63).
    Malhotra, R., Singh, N., & Singh, Y. (2011). Genetic algorithms: Concepts, design for optimization of process controllers. Computer and Information Science, 4(2), 39.
    Popovic, D., Sifrim, A., Pavlopoulos, G. A., Moreau, Y., & De Moor, B. (2012). A simple genetic algorithm for biomarker mining. In Proceedings of the IAPR international conference on pattern recognition in bioinformatics (pp. 222–232). Springer.
    Raza, K., & Jaiswal, R. (2013). Reconstruction and analysis of cancer-specific gene regulatory networks from gene expression profiles. arXiv:1305.5750.
    Sastry, K., Goldberg, D. E., & Kendall, G. (2005). Genetic Algorithms, Search Method-ologies: Introductory Tutorials in Optimization and Decision Support Techniques (pp. 97–125). Springer: Springer.
    Valavanis, I., Pilalis, E., Georgiadis, P., Kyrtopoulos, S., & Chatziioannou, A. (2015). Cancer biomarkers from genome-scale dna methylation: comparison of evolu-tionary and semantic analysis methods. Microarrays, 4(4), 647–670.
    Zhao, L. (2013). Functional characterization of the candidate colorectal cancer gene CNOT1. University of Minnesota Ph.D. thesis. Cancer/Radiothérapie 23 (2019) 201–208