通过整合机器学习,文献挖掘和元分析来实现癌症缓解症的转录学签名
Kening Zhao1, Esmaeil Ebrahimie2, Manijeh Mohammadi-Dehcheshmeh3
1Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, 3086, Australia.
Computers in biology and medicine
|March 7, 2024
概括
研究人员使用机器学习识别了26个基因的癌症缓解症的转录密码签名. 这种签名有助于发现新的治疗方法和评估癌症患者肌肉缩的现有药物.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 癌症缓解症是一种代谢综合征,导致严重的骨肌肉缩.
- 目前没有有效的临床干预癌症缓解症.
- 临床前动物模型和转录基因数据对于研究缓解症机制至关重要.
研究的目的:
- 为了识别癌症缓解症的强有力的转录基因签名.
- 评估用于分析转录数据的机器学习模型.
- 探索潜在的药物重定向策略,用于癌症缓解症治疗.
主要方法:
- 十个卡塞克特小鼠肌肉转录数据集的元分析.
- 应用七种属性权重模型来识别转录学签名.
- 评估11个模式发现模型和文献挖掘用于药物重用.
主要成果:
- 一个26个基因转录的签名癌症缓解症被确定.
- 深度学习和随机森林模型在分类方面表现出卓越的表现.
- 黑色素和infliximab的组合显示了与关键缓解症基因 (Rorc,Fbxo32) 的潜在负面相互作用.
结论:
- 整合机器学习,元分析和文献挖掘是有效的识别癌症缓解症签名.
- 识别的签名对改善临床诊断和管理有影响.
- 这种方法有助于发现癌症缓解症的新疗法策略.
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