Biostatistical Analysis on Anti-breast Cancer Drug Screening Volume 7 - Issue 1

Xia Jiang¹ and Bin Zhao²*

• ¹Hospital, Hubei University of Technology, Wuhan, Hubei, China
• ²School of Science, Hubei University of Technology, Wuhan, Hubei, China

Received: December 02, 2021   Published: December 13, 2021

Corresponding author: Bin Zhao, School of Science, Hubei University of Technology, Wuhan, Hubei, China

DOI: 10.32474/RRHOAJ.2021.07.000256

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Breast cancer is one of the most lethal cancers, estrogen receptor α Subtype (ERα) is an important target. The compounds that able to fight ERα active may be candidates for treatment of breast cancer. According to ERα activity, pharmacokinetic properties and safety of the compounds identified 15 biological activity descriptors. In this paper, 15 biological activity descriptors were verified according to the gradient optimization algorithm based on neural network. The research shows that these 15 biological activity descriptors can not only predict ERα with low error, can also predict the pharmacokinetic properties and safety with an accuracy more than 90%. Therefore, these biological activity descriptors has great medical research value.

Keywords: Breast cancer; Softmax function; Adam algorithm; Biological activity

Abstract| Introduction| Overview of Bp Neural Network| Data Description and Preprocessing| Model Training and Prediction| Conclusion| Reference|