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一个基于神经网络的评分系统,用于预测乳腺癌的预后和治疗

Min Deng¹, Xinyu Chen², Jiayue Qiu²

¹MOE Frontier Science Center for Precision Oncology, Cancer Center, Faculty of Health Sciences, University of Macau, Taipa, Macau, China.

Current protocols

|August 21, 2024

概括

一个新的转移和免疫基因组风险评分 (MIRS) 有助于乳腺癌的预后. 该工具有助于预测患者的结果,并使用转录组学数据分析指导个性化治疗策略.

科学领域:

在瘤学瘤学.
生物信息学是一种生物信息学.
免疫学免疫学免疫学

背景情况:

乳腺癌缺乏精确的分子生物标志物用于早期预测,限制了治疗选择.
转移性和瘤透性免疫细胞影响抗瘤治疗反应.
这些特征对于指导个性化乳腺癌治疗的综合预后价值尚不清楚.

研究的目的:

为乳腺癌分析引入转移和免疫基因组风险评分 (MIRS).
在没有高级编程技能的情况下提供使用MIRS工具的协议.
促进MIRS在分析乳腺癌新数据集中的应用,以改善临床管理.

主要方法:

使用生物信息学开发转移性和免疫基因组风险评分 (MIRS).
从转录组学数据计算MIRS得分的详细协议.
使用MIRS网络工具进行结果预测和治疗策略评估的指导方针.

主要成果:

该MIRS是一个全面和用户友好的评分系统.
协议允许使用MIRS.有效分析乳腺癌数据集.
该工具有助于评估治疗反应和指导治疗策略.

结论:

MIRS提供了一种新的方法来分析乳腺癌的复杂性.
该工具使研究人员和临床医生能够利用转录组学数据进行个性化医学.
MIRS有可能提高乳腺癌的预后准确性和治疗选择.

关键词:

在这个过程中,R是R.乳腺癌乳腺癌乳腺癌治疗策略治疗策略瘤微环境是一个微环境.

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