查和预测基于PU学习的多奥米克T-ALL核心基因
IEEE transactions on computational biology and bioinformatics
|August 14, 2025
概括
这项研究使用一种新的PU包装方法确定T细胞急性淋巴细胞白血病 (T-ALL) 中的核心基因. 这些已识别的基因显示出T-ALL生物标志物开发和向治疗的潜力.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- T细胞急性淋巴细胞白血病 (T-ALL) 是一种重大瘤性疾病.
- 识别关键基因对于理解T-ALL病原体和开发向疗法至关重要.
研究的目的:
- 选和识别与T-ALL相关的核心基因.
- 开发和验证T-ALL基因发现的生物信息学方法.
主要方法:
- 对RNA-seq,CTCF ChIP-seq和DNA甲基化数据集的综合分析.
- 用多层感知子 (MLP) 分类器进行积极未标记 (PU) 包装的应用,用于候选基因选.
- 构建蛋白质-蛋白质相互作用 (PPI) 网络和功能丰富分析 (GO,KEGG) 用于核心基因验证.
主要成果:
- 成功选差异表达基因 (DEGs) 和确定候选T-ALL相关基因.
- 使用PU学习方法处理有限的阳性样本数据集.
- 通过功能丰富和文献审查验证识别的核心基因,确认它们与T-ALL.相关.
结论:
- 拟议的生物信息学方法有效地预测T-ALL相关的核心基因.
- 已识别的核心基因对未来T-ALL生物标志物研究和治疗点开发具有重大潜力.
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