人工智能在使用蛋白p53检测癌症中的作用:一篇评论
Manisha R Patil1, Anand Bihari2
1School of Computer Science Engineering and Information System, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Molecular biology reports
|December 10, 2024
概括
瘤抑制蛋白p53对于预防瘤至关重要. 使用p53数据的机器学习模型显示了早期癌症检测和理解瘤发展的前景.
科学领域:
- 在瘤学瘤学.
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
背景情况:
- 由TP53基因编码的瘤抑制蛋白p53对于正常细胞发育和瘤预防至关重要.
- p53调节细胞生长,DNA修复和细胞亡,保持基因组的完整性.
- 了解p53在瘤发育中的作用对于癌症研究至关重要.
研究的目的:
- 综合分析与p53用于癌症预测的机器学习技术.
- 评估这些AI模型在检测和推测癌症方面的有效性.
- 探索使用p53和AI早期癌症检测的新研究机会.
主要方法:
- 对p53数据应用的机器学习技术的审查.
- 分析各种数据类型,并纳入预测模型.
- 对各种机器学习方法的性能指标的评估.
主要成果:
- 确定了各种适用于基于p53的癌症预测的机器学习技术.
- 评估不同模型的性能和数据要求.
- 强调了人工智能与p53结合用于癌症诊断的潜力.
结论:
- 机器学习利用p53蛋白质,为早期癌症检测提供了一个有前途的途径.
- 进一步的研究可以利用AI和p53来增强癌症监测和干预策略.
- 本综述为开发先进的人工智能驱动的癌症预测工具提供了基础的理解.
相关概念视频
02:26Protein Networks
3.9K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
3.9K
02:23Abnormal Proliferation
4.4K
Under normal conditions, most adult cells remain in a non-proliferative state unless stimulated by internal or external factors to replace lost cells. Abnormal cell proliferation is a condition in which the cell's growth exceeds and is uncoordinated with normal cells. In such situations, cell division persists in the same excessive manner even after cessation of the stimuli, leading to persistent tumors. The tumor arises from the damaged cells that replicate to pass the damage to the...
4.4K