早期诊断:用于质谱数据分类的端到端CNN-LSTM模型
Khawla Seddiki1,2, Fŕed Eric Precioso3, Melissa Sanabria3
1Centre de Recherche du CHU de Québec-Université Laval, Québec City, Québec G1V 4G2, Canada.
Analytical chemistry
|August 25, 2023
概括
这项研究引入了一种新的深度学习 (DL) 方法,使用卷积神经网络 (CNN) 和长短期记忆 (LSTM) 网络来分析液态染色体质谱法 (LC-MS) 数据. 该方法通过准确区分瘤和正常组织来提高早期癌症检测.
科学领域:
- 生物医学数据分析
- 计算生物学是一种计算生物学.
- 癌症研究 癌症研究
背景情况:
- 液体染色体质谱法 (LC-MS) 对于细胞分析和癌症研究至关重要,提供组织的分子指纹.
- 在LC-MS数据分析中的挑战包括噪音,峰值转移和高维度,阻碍了精确的癌症诊断.
- 深度学习 (DL) 模型可以有效地处理复杂的数据,同时学习特征和分类,非常适合原始LC-MS数据.
研究的目的:
- 开发一个端到端的深度学习 (DL) 方法来分析液体染色体质谱 (LC-MS) 数据.
- 解决诸如噪音,峰值转移和癌症诊断LC-MS数据中的高维度等挑战.
- 创建一个DL模型,能够早期区分瘤和正常组织.
主要方法:
- 提出了一个新的深度学习 (DL) 框架,结合了卷积神经网络 (CNN) 和长期短期记忆 (LSTM) 网络.
- 该CNN组件减少数据的维度,并学习空间特征.
- 该LSTM组件捕获数据中的时间模式.
主要成果:
- 拟议的DL模型有效地减少了数据的维度,并从LC-MS数据中学习了相关的空间和时间特征.
- 该模型在同一数据集上的基准模型和最先进的模型相比显示出更高的性能.
- 该方法成功地实现了瘤和正常组织之间的早期歧视.
结论:
- 开发的DL框架为改善诊断过程中的早期癌症检测提供了一个有希望的策略.
- 综合CNN-LSTM方法有效地处理LC-MS数据的复杂性,用于癌症分析.
- 这种方法最大限度地减少了对广泛数据预处理和特征选择的需求.
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