1401 program error gene construction kit

Guide to analysis of DNA microarray data. GEMMAóA Grid environment for microarray management and analysis in bone marrow stem cells experiments. 542-557.ĩ.Beltrame F, Papadimitropoulos A, Porro I, Scaglione S, Schenone A, Torterolo L, et al. Epidemiology and control of common diseases in Iran. Expression analysis of MiR-21, MiR-205, and MiR-342 in breast cancer in Iran. doi: 10.1145/2556288.2557108ħ.Savad S, Mehdipour P, Miryounesi M, Shirkoohi R, Fereidooni F, Mansouri F, et al.

The conference on human factors in computing systems 2014 April 26- May1 Toronto, Canada: ACM SIGCH 1625-34 p. Support matching and satisfaction in an online breast cancer support community. Available from: Ħ.Vlahovic TA, Wang YC, Kraut RE, Levine JM. Cancer News: American Cancer Society, 1947. American Cancer Society guidelines for the early detection of cancer. In addition, the gene sets network formed on gene expression data was incompetent.īreast cancer؛ Bi-clustering؛ Cluster analysis؛ Microarray data؛ Gene expression؛ Neoplasms؛ Bayesian networkĤ.Smith RA, Cokkinides V, von Eschenbach AC, Levin B, Cohen C, Runowicz CD, et al. Accordingly, it could be recommended for data analysis. The data evaluation revealed that the results of the models were almost the same, but the PL model performed better than the others, finding 11 bi-clusters this model was used to build the network of gene sets.Ĭonclusion: According to the results, the PL method was suitable for clustering the data. Four models, except for CC, successfully found bi-clusters in the data set. Results: After preprocessing, clustering was performed on the data with the dimension (4710 × 18) of the genes. Furthermore, the results of the best model were assessed for building a genes sets network with Bayesian networks. The enrichment efficacy of the clusters was evaluated with gene ontology, and the results of these five models were compared with the Jaccard index, variance stability, least-square error, and goodness of fit indices. For this purpose, we obtained the microarray gene expression data for lapatinib-resistant breast cancer cell lines from previously published research. Method: A descriptive and inferential statistical analysis was carried out to evaluate unsupervised learning models of gene expression analysis and five bi-clustering methods (including PLAID (PL), Fabia, Bimax, Cheng & Church (CC), and Xmotif) were compared. Clustering is the method for analyzing high-dimension data, which we used in the present research for collecting similar genes in separated clusters. One of the genetic evaluation methods of this disease is microarray technology, which allows the examination of the simultaneous expression of thousands of genes. Gene mutations are the key determinants of the disease therefore, the genetic study of this disease is of paramount importance. شناسه دیجیتال (DOI): 10.30476/mejc.2022.89998.1557Īhmad Sohrabi 1؛ Neda Saraygord-Afshari 2؛ Masoud Roudbari 1ġDepartment of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, IranĢDepartment of Medical Biotechnology, Faculty of Allied Medical Sciences, Iran University of Medical Sciences, Tehran, Iranīackground: Breast cancer is one of the most prevalent types of cancer in Iranian women and the second cause of death in women worldwide. The Application of Bi-clustering and Bayesian Network for Gene Sets Network Construction in Breast Cancer Microarray Data

1401 program error gene construction kit