一种改进SVM-RFE基因选择方法并结合LASSO的新型基因表达检测方法,在降低成本和时间内降低乳腺癌风险。

IF 1.5 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Madhuri Gupta, Bharat Gupta
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引用次数: 7

摘要

乳腺癌是导致妇女死亡的主要疾病。它是由乳腺癌细胞的基因突变引起的。基因检测已经成为检测基因突变的流行方法,但对于印度等发展中国家的一些患者来说,检测费用相对昂贵。基因测试需要2到4周的时间来确定癌症。由于部分患者癌细胞生长速度快,病程长短受基因预后影响。在研究工作中,提出了一种利用机器学习技术根据患者的临床结果预测基因表达水平的成本和时间效率高的方法。提出了一种改进的SVM-RFE_MI基因选择技术,通过解释方差统计分析提取高方差基因,找到与乳腺癌相关的最显著基因。最小绝对收缩选择算子(LASSO)和岭回归技术用于预测基因表达水平。该方法预测显著基因的表达,具有较低的均方根误差和可接受的调整r平方值。根据研究,分析这些选定的基因有利于在早期诊断乳腺癌,减少了成本和时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A novel gene expression test method of minimizing breast cancer risk in reduced cost and time by improving SVM-RFE gene selection method combined with LASSO.

A novel gene expression test method of minimizing breast cancer risk in reduced cost and time by improving SVM-RFE gene selection method combined with LASSO.

A novel gene expression test method of minimizing breast cancer risk in reduced cost and time by improving SVM-RFE gene selection method combined with LASSO.

A novel gene expression test method of minimizing breast cancer risk in reduced cost and time by improving SVM-RFE gene selection method combined with LASSO.

Breast cancer is the leading diseases of death in women. It induces by a genetic mutation in breast cancer cells. Genetic testing has become popular to detect the mutation in genes but test cost is relatively expensive for several patients in developing countries like India. Genetic test takes between 2 and 4 weeks to decide the cancer. The time duration suffers the prognosis of genes because some patients have high rate of cancerous cell growth. In the research work, a cost and time efficient method is proposed to predict the gene expression level on the basis of clinical outcomes of the patient by using machine learning techniques. An improved SVM-RFE_MI gene selection technique is proposed to find the most significant genes related to breast cancer afterward explained variance statistical analysis is applied to extract the genes contain high variance. Least Absolute Shrinkage Selector Operator (LASSO) and Ridge regression techniques are used to predict the gene expression level. The proposed method predicts the expression of significant genes with reduced Root Mean Square Error and acceptable adjusted R-square value. As per the study, analysis of these selected genes is beneficial to diagnose the breast cancer at prior stage in reduced cost and time.

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来源期刊
Journal of Integrative Bioinformatics
Journal of Integrative Bioinformatics Medicine-Medicine (all)
CiteScore
3.10
自引率
5.30%
发文量
27
审稿时长
12 weeks
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