Using machine learning to identify active and druggable pathways in primary and metastatic cancers through reference-free pathway analysis

使用机器学习通过无参考途径分析来识别原发性和转移性癌症中的活性和可药物途径

• 批准号: 458984
• 负责人: Keshavarz Faeze
• 金额: $ 7.65万
• 依托单位: BC Cancer, part of PHSA (Vancouver)
• 依托单位国家: 加拿大
• 项目类别: Studentship Programs
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-11-01 至 2024-11-01
• 项目状态: 已结题
• 来源: https://webapps.cihr-irsc.gc.ca/decisions/p/project_details.html?applId=458984&lang=en
• 关键词: Cancer Genomics; Cellular Pathway Modification; Classification; Machine Learning; Random Forest; Targeted Therapies; Transcriptomics

The modifications in exonic regions of DNA (parts of DNA sequence that are translated to proteins) can affect the structure and consequently the function of proteins and the cellular pathways they participate in. Changes in the cellular network can be det

DNA外显子区域的修饰（翻译成蛋白质的DNA序列的一部分）可以影响蛋白质的结构，从而影响蛋白质的功能及其参与的细胞途径。蜂窝网络的变化是可以察觉的