eccDNA2Ca:一个集体深度学习框架,用于可解释地预测与癌症相关的染色体外圆形DNA
Lina Zhou1, Mengxi Wang1, Lingyun Zou1
1Department of Clinical Data Research, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing University, 4000a4 Chongqing, China; School of Medicine, Chongqing University, 400030 Chongqing, China.
Journal of advanced research
|February 13, 2026
概括
我们开发了eccDNA2Ca,这是一种新的计算工具,可以从原始序列中识别与癌症相关的染色体外圆形DNA (eccDNA). 这一框架优先考虑了eccDNA用于功能研究和癌症基因组学中的生物标志物发现.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 癌症研究 癌症研究
背景情况:
- 外染色体循环DNA (eccDNA) 驱动瘤基因放大,治疗耐药性和癌症异质性.
- 目前对eccDNA的计算方法缺乏对瘤发生的功能和临床意义的关注.
研究的目的:
- 开发eccDNA2Ca,这是一个可解释的框架,用于从原始序列中预测癌症相关的eccDNA.
- 通过优先考虑eccDNAs,使功能研究和生物标志物发现成为可能.
主要方法:
- 开发了ccDNA2Ca,这是一个整体学习框架,结合了XGBoost,CNN和LSTM.
- 在16种癌症类型的465个人类eccDNA精选数据集上训练模型.
- 利用基因组特征,深度序列编码,SHAP分析和动机发现来实现可解释性.
主要成果:
- eccDNA2Ca实现了卓越的性能 (AUC> 0.96,AUPR> 0.95) 并超过了传统的分类器.
- 特征解释突出重复内容和k-mer频率;动机分析发现了转录因子结合位.
- 预测的eccDNA与瘤突变负担,免疫透,微卫星不稳定性和患者存活率之间的验证关联.
结论:
- eccDNA2Ca是第一个可解释的开源工具,可以根据序列特征对癌症相关的eccDNA进行优先排序.
- 为eccDNA功能研究,癌症生物标志物发现和转化基因组学提供可扩展的资源.
- 源代码和Web服务器是公开可用的,用于更广泛的研究应用.
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