人工智能识别了乳腺癌干细胞分化的强有力的诱导因子,基于基因表达数据的对抗性学习
Zhongxiao Li1,2, Antonella Napolitano3, Monica Fedele3
1Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia.
Briefings in bioinformatics
|May 3, 2024
概括
这项研究使用人工智能来选小分子,以检测它们诱导癌症干细胞 (CSC) 分化的能力. 在6种测试的化合物中,有5种显示出有效性,为乳腺癌提供了新的治疗策略.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 癌症干细胞 (CSCs) 对瘤生长和复发至关重要.
- 诱导CSC分化是一种有前途的治疗策略.
- 针对CSC提供了长期癌症缓解的潜力.
研究的目的:
- 识别可以诱导癌症干细胞分化的小分子.
- 为了利用人工智能和转录学数据进行药物查.
- 为了发现乳腺癌的新型治疗剂.
主要方法:
- 在人类诱导的多能干细胞的单细胞RNA-Seq数据上训练了一个深度的神经网络.
- 选了LINCS数据库中的药物诱导基因表达特征.
- 采用对抗式学习来减轻数据集之间的特定领域偏差.
- 在乳腺癌细胞系中经过实验验证的最高分数分子 (MDA-MB-231和MCF7).
主要成果:
- 成功选了大量的小分子图书馆,以寻找诱导差异化的潜力.
- 确定了五种有效的化合物:三胺,OTS-167,奎纳克林,格拉尼斯特龙和A-443654.
- 证明了这些化合物的有效性,促进乳腺中枢细胞分化.
结论:
- 人工智能驱动的转录学分析可以有效地识别CSC差异化剂.
- 这些已识别的化合物代表了乳腺癌潜在的向治疗方法.
- 这种方法加速了新的癌症治疗方法的发现.
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