Objectives
Classification of breast cancer patients into different risk classes is very important in clinical applications. It is estimated that the advent of high-dimensional gene expression data could improve patient classification. In this study, a new method for transforming the high-dimensional gene expression data in a low-dimensional space based on wavelet transform (WT) is presented. Methods
The proposed method was applied to three publicly available microarray data sets. After dimensionality reduction using supervised wavelet, a predictive support vector machine (SVM) model was built upon the reduced dimensional space. In addition, the proposed method was compared with the supervised principal component analysis (PCA). Results
The performance of supervised wavelet and supervised PCA based on selected genes were better than the signature genes identified in the other studies. Furthermore, the supervised wavelet method generally performed better than the supervised PCA for predicting the 5-year survival status of patients with breast cancer based on microarray data. In addition, the proposed method had a relatively acceptable performance compared with the other studies. Conclusion
The results suggest the possibility of developing a new tool using wavelets for the dimension reduction of microarray data sets in the classification framework.
Citations
Citations to this article as recorded by
Antimicrobial Resistance Patterns Among Bloodstream Infection Isolates in a Tertiary Care Hospital in Northwestern India: A Retrospective Study Rajendra Surje, Preeti Shrivastava, Abhishek Kumar Jain, Kanchan Gupta Journal of Madha Academy of Research and Science.2026; 1: 4. CrossRef
Role of machine learning in molecular pathology for breast cancer: A review on gene expression profiling and RNA sequencing application Sahar Rezaei, Zeinab Hamedani, Kousar Ahmadi, Parna Ghannadikhosh, Alireza Motamedi, Maedeh Athari, Hengameh Yousefi, Amir Hossein Rajabi, Alireza Abbasi, Hossein Arabi Critical Reviews in Oncology/Hematology.2025; 213: 104780. CrossRef
Diagnosing thyroid disorders: Comparison of logistic regression and neural network models Shiva Borzouei, Hossein Mahjub, NegarAsaad Sajadi, Maryam Farhadian Journal of Family Medicine and Primary Care.2020; 9(3): 1470. CrossRef
Thyroid disorder diagnosis based on Mamdani fuzzy inference system classifier Negar Asaad Sajadi, Hossein Mahjub, Shiva Borzouei, Maryam Farhadian Koomesh Journal.2020; 22(1): 107. CrossRef
Diagnosis of hypothyroidism using a fuzzy rule-based expert system Negar Asaad Sajadi, Shiva Borzouei, Hossein Mahjub, Maryam Farhadian Clinical Epidemiology and Global Health.2019; 7(4): 519. CrossRef
WaveICA: A novel algorithm to remove batch effects for large-scale untargeted metabolomics data based on wavelet analysis Kui Deng, Fan Zhang, Qilong Tan, Yue Huang, Wei Song, Zhiwei Rong, Zheng-Jiang Zhu, Kang Li, Zhenzi Li Analytica Chimica Acta.2019; 1061: 60. CrossRef
Objectives
We aimed at evaluating the virulence of atypical <i>Shigella flexneri</i> II:(3)4,7(8) by DNA microarray and invasion assay. Methods
We used a customized <i>S. flexneri</i> DNA microarray to analyze an atypical <i>S. flexneri</i> II:(3)4,7(8) gene expression profile and compared it with that of the <i>S. flexneri</i> 2b strain. Results
Approximately one-quarter of the atypical <i>S. flexneri</i> II:(3)4,7(8) strain genes showed significantly altered expression profiles; 344 genes were more than two-fold upregulated, and 442 genes were more than 0.5-fold downregulated. The upregulated genes were divided into the category of 21 clusters of orthologous groups (COGs), and the “not in COGs” category included 170 genes. This category had virulence plasmid genes, including the <i>ipa-mxi-spa</i> genes required for invasion of colorectal epithelium (type III secretion system). Quantitative reverse-transcription polymerase chain reaction results also showed the same pattern in two more atypical <i>S. flexneri</i> II:(3)4,7(8) strains. Atypical <i>S. flexneri</i> II:(3)4,7(8) showed four times increased invasion activity in Caco-2 cells than that of typical strains. Conclusion
Our results provide the intracellularly regulated genes that may be important for adaptation and growth strategies of this atypical <i>S. flexneri</i>.
First
Prev
