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一个联合复杂的网络和机器学习方法,用于识别自闭症大脑中的歧视性基因社区

Antonio Lacalamita^1,2, Ester Pantaleo^1,2, Alfonso Monaco^1,2

¹Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Bari, Italy.

|November 5, 2025

概括

基因共同表达网络分析确定了自闭症谱系障碍 (ASD) 中的关键基因社区. 这些发现使用机器学习进行了验证,提高了诊断准确度,并确定了关键的遗传变异.

科学领域:

神经遗传学神经遗传学
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

自闭症谱系障碍 (ASD) 呈现出显著的遗传异质性.
基因共同表达网络分析提供了一种探索复杂遗传架构的方法.
识别失调的基因社区对于理解ASD至关重要.

研究的目的:

用生物信息学方法识别和验证与ASD相关的基因社区.
应用机器学习来改进基于基因表达模式的ASD分类.
研究特定基因变异在已识别的基因社区中的作用.

主要方法:

使用公开可用的大脑微阵列数据集 (GSE28475).
通过莱登社区检测进行基因共同表达网络分析.
实现了一个机器学习框架,用于分类的特征选择.
在独立的微阵列数据集上验证的结果.
应用可解释的人工智能 (XAI) 用于因果分析.

主要成果:

通过使用基因表达数据,在区分自闭症与对照对象方面取得了高准确性.
确定了两个特定的基因群落 (43和44基因),为ASD相关变异增添了丰富性.
在独立数据集上验证了分类准确性,证实了可靠性.
XAI的分析证实了自闭症特异性变异在确定的社区中的关键作用.

结论:

基因共同表达网络分析有效地识别了ASD中生物相关的基因社区.
结合这些社区的机器学习模型增强了诊断潜力.
已识别的基因群体和相关变异为ASD潜在的遗传机制提供了洞察力.

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