一个改进的癌症诊断算法用于基于PCA的蛋白质质谱和一个结合ResNet和SENet的单维神经网络
Liang Ma1,2, Wenqing Gao2,3, Xiangyang Hu1,2
1Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, P. R. China. yujiancheng@nbu.edu.cn.
The Analyst
|November 4, 2024
概括
由于微妙的早期症状,早期癌症诊断具有挑战性. 一个新的PCA-1DSE-ResCNN算法有效地分析复杂的质谱数据,以改善癌症检测和早期诊断.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 癌症仍然是全球主要的健康问题,早期发现对治疗成功至关重要.
- 缺乏特定的早期症状往往导致晚期诊断,阻碍患者的结果.
- 基于质谱的蛋白质组学显示出癌症诊断的前景,但面临着高维,噪音数据的挑战.
研究的目的:
- 开发和验证一种改进的算法,用于分析用于早期癌症诊断的高维质谱数据.
- 解决临床蛋白质组学中数据维度,噪声和潜在诊断错误的挑战.
- 用先进的计算方法提高癌症检测的准确性和可靠性.
主要方法:
- 提出了一种改进的算法,PCA-1DSE-ResCNN,它结合了主要组件分析 (PCA) 来减少维度和卷积神经网络 (CNN) 来进行分类.
- 该CNN组件 (1DSE-ResCNN) 集成了残留和挤压激发块,以减轻过和梯度消失.
- 使用高维卵巢癌质谱数据集验证了算法的性能.
主要成果:
- 与其他方法相比,PCA-1DSE-ResCNN算法表现出优越的性能.
- 在多个数据集的早期卵巢癌检测中实现了高准确度,特异性和灵敏度.
- 有效地学习高维数据特征,并处理质谱数据中的非线性关系.
结论:
- PCA-1DSE-ResCNN算法为分析癌症诊断中复杂的质谱数据提供了一个强大的解决方案.
- 这种方法有可能对快速诊断和早期发现各种癌症作出重大贡献.
- 改进的计算策略对于克服癌症查临床蛋白质组学的局限性至关重要.
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