EVALUATION OF DIFFERENT CLASSIFICATION MODELS TO EXTRACT GENE SIGNATURES FOR BREAST CANCER RECURRENCE USING MICROARRAY DATA

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

SEHHATI MOHAMMADREZA; KAYED MINA

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Isfahan Medical School Year:2017 | Volume:35 | Issue:419 Page(s): 984-103

Download Full-Text

View:

184

Download:

Cites:

Information Journal Paper

Title

EVALUATION OF DIFFERENT CLASSIFICATION MODELS TO EXTRACT GENE SIGNATURES FOR BREAST CANCER RECURRENCE USING MICROARRAY DATA

Author(s)

SEHHATI MOHAMMADREZA | KAYED MINA | Issue Writer Certificate

Keywords

ALGORITHMS

BIOMARKERS

BREAST CANCER

CLASSIFICATION

GENE EXPRESSION PROFILING

Abstract

Background: In this study, we aimed to improve the reliability and biological interpretability of gene signatures selected from microarrays by efficient usage of computational models and mathematical ALGORITHMS.Methods: At the first step, a good model with high accuracy was chosen to predict cancer recurrence in microarray gene expression data on breast tumors. In this regard, microarray gene expression data of breast tumor in 1271 cancer patients (379 with recurrence and 892 people without recurrence) were utilized to construct an appropriate predictive model for recurrence by comparing the performance of multiple classifiers. In the pre-processing stage, different methods like correlation-based feature selection (CFS), principal component analysis (PCA), independent component analysis (ICA), and genetic algorithm as well as a random selection method were used to reduce the dimensions and choose the most appropriate genes (features). Findings: A total of five gene signatures were selected by combining genetic algorithm, top scoring set (TSS), and random selection method, which showed the best results in most CLASSIFICATION models. The final indicator genes were TRIP13, KIF20A, NEK2, RACGAP1 and TYMS, which had significant contribution in the structure of microtubules and spindle and also regulated the attachment of spindle microtubules to kinetochore.Conclusion: By using hybrid models, we can avoid overfitting in training and achieve acceptable accuracy with biologically interpretable genes.

Cites

No record.

References

No record.

Cite

APA: Copy

SEHHATI, MOHAMMADREZA, & KAYED, MINA. (2017). EVALUATION OF DIFFERENT CLASSIFICATION MODELS TO EXTRACT GENE SIGNATURES FOR BREAST CANCER RECURRENCE USING MICROARRAY DATA. JOURNAL OF ISFAHAN MEDICAL SCHOOL (I.U.M.S), 35(419), 984-103. SID. https://sid.ir/paper/703926/en

Vancouver: Copy

SEHHATI MOHAMMADREZA, KAYED MINA. EVALUATION OF DIFFERENT CLASSIFICATION MODELS TO EXTRACT GENE SIGNATURES FOR BREAST CANCER RECURRENCE USING MICROARRAY DATA. JOURNAL OF ISFAHAN MEDICAL SCHOOL (I.U.M.S)[Internet]. 2017;35(419):984-103. Available from: https://sid.ir/paper/703926/en

IEEE: Copy

MOHAMMADREZA SEHHATI, and MINA KAYED, “EVALUATION OF DIFFERENT CLASSIFICATION MODELS TO EXTRACT GENE SIGNATURES FOR BREAST CANCER RECURRENCE USING MICROARRAY DATA,” JOURNAL OF ISFAHAN MEDICAL SCHOOL (I.U.M.S), vol. 35, no. 419, pp. 984–103, 2017, [Online]. Available: https://sid.ir/paper/703926/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops