Title

Diagnosis of triple negative breast cancer using expression data with several machine learning tools

Authors

Sankaranarayanan Pranaya¹, PK Ragunath^1,* & P Venkatesan²

Affiliation

¹Department of Bioinformatics, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai - 600 116, India; ²Department of Statistics, ICMR, National Institute for Research in Tuberculosis, Chetpet, Chennai - 600 031, India; *Corresponding author

Email

Pranaya Sankaranarayanan- Email: pranayas@sriramachandra.edu.in; Phone: +91 7339371771
PK Ragunath – Email: sriherbioinfo.1@gmail.com; Phone: +91 9841351069
Venkatesan Perumal – Email: venkaticmr@gmail.com; Phone: +91 9444057487

Article Type

Research Article

Date

Received March 2, 2022; Revised April 30, 2022; Accepted April 30, 2022, Published April 30, 2022

Breast cancer is one of the top three commonly caused cancers worldwide. Triple Negative Breast Cancer (TNBC), a subtype of breast cancer, lacks expression of the oestrogen receptor, progesterone receptor, and HER2. This makes the prognosis poor and early detection hard. Therefore, AI based neural models such as Binary Logistic Regression, Multi-Layer Perceptron and Radial Basis Functions were used for differential diagnosis of normal samples and TNBC samples collected from signal intensity data of microarray experiment. Genes that were significantly upregulated in TNBC were compared with healthy controls. The MLP model classified TNBC and normal cells with an accuracy of 93.4%. However, RBF gave 74% accuracy and binary Logistic Regression model showed an accuracy of 90.0% in identifying TNBC cases.

Keywords

Triple Negative Breast Cancer (TNBC), Breast Cancer, Machine learning, Artificial Neural Network (ANN), Logistic Regression, Radial Basis Function (RBF), Multi-Layer Perceptron (MLP)

Citation

Pranaya et al. Bioinformation 18(4): 325-330 (2022)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.