在前列腺癌中发现分子标记物的交叉omics可解释的神经网络
Xin Chen1, Sheng Yi1, Anwaier Yuemaierabola2
1School of Computer Science and Technology, Xinjiang University, Urumqi, 830017, China; Xinjiang Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830017, China.
Computational biology and chemistry
|January 10, 2026
概括
我们开发了一个新的AI模型,Cross-omics可解释神经网络 (CINN),以识别攻击性前列腺癌标志物. CINN集成了多学科数据,提高了预测准确度,并揭示了精准医学的关键生物学见解.
科学领域:
- 计算生物学和生物信息学
- 癌症基因组学 癌症基因组学
- 医学中的人工智能.
背景情况:
- 确定侵袭性前列腺癌的分子驱动因素至关重要,但具有挑战性.
- 传统模型难以处理复杂的多omics数据,而深度学习缺乏可解释性.
- 需要整合多种omics数据的方法,以精确预测癌症状态和标记物识别.
研究的目的:
- 开发一个可解释的深度学习框架,Cross-omics可解释神经网络 (CINN),用于前列腺癌.
- 整合多omics数据 (基因表达,突变,拷贝数变异) 进行增强的预测.
- 确定关键的分子标记物和潜在的前列腺癌进展的生物学机制.
主要方法:
- 提出CINN,一个生物模拟框架,将先前的生物知识 (路径/PPI网络) 与可训练面具层相结合.
- 动态优化生物连接,以改善知识的表现和解释性.
- 将CINN应用于前列腺癌数据集,整合基因表达,体突变和副本数变异.
主要成果:
- 在预测前列腺癌状态方面,CINN显著超过了基线 (P-NET).
- 使用可训练面具的CINN-pw变种提高了F1得分13.1% (至0.843),精度8.3% (至0.894),AUC提高了2.3% (至0.949).
- 确定TBP和TAF2是关键的分子候选者,与前列腺癌进展有关,文献支持.
结论:
- 在癌症研究中,CINN提供了一种可靠和可解释的方法来进行多主题数据分析.
- 该框架有助于发现临床相关的分子标记物和生物见解.
- 在前列腺癌中,CINN具有推进精准医学和向治疗策略的潜力.
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