Application of software tools in the development of novel drug for Breast Cancer

The DNA methylation, in human cells regulates tumor growth and it also indicates the breast cancer susceptibility. In a research they pinpointed the genes that are involved invariations that occur in methylationof the breast cancer epigenome. The DNA methylation(CG sites)sequences from 7-normal individuals and 8-breast cancer patients were compared by using Statistical software package R and by Upperone-tailed chi-square tests. Consensus Path Database is used to map selected CG sites to study the biological relations between healthy individuals with patients affected by tumour. Based on the data collected from PubMed and Gene Card, 26 potential biomarker genes were discovered. All these data helps in the development of novel treatments for breast cancers.

The Neural Networks with an Incremental Learning Algorithm

As breast cancer can be very aggressive, only early detection can prevent mortality. The proposed system is to eliminate the unnecessary waiting time aswell as reducing human and technical errors in diagnosing breast cancer. The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. 

This paper uses the neural networks with an incremental learning algorithm as a tool to classify a mass in the breast (benign and malignant) using selection of the most relevant risk factors and decision making of the breast cancer diagnosis To test the proposed algorithm we used the Wisconsin Breast Cancer Database (WBCD). ANN with anincremental learning algorithm performance is tested using classificationaccuracy, sensitivity and specificity analysis, and confusion matrix. The obtained classification accuracy of 99.95%, a very promising result compared with previous algorithms already applied and recent classification techniques applied to the same database.