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使用基于隐藏马尔科夫模型和随机卷积内核转换的微阵列分析诊断白血病

Sareh Baqeri Matak¹, Elham Askari², Sara Motamed²

¹Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran.

Computational biology and chemistry

|September 14, 2025

概括

这项研究使用深度学习和微阵列数据来准确检测白血病,达到99.26%的准确性. 识别DNA变化有助于早期白血病诊断和干预.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

白血病是一种流行癌症,早期检测至关重要.
微阵列数据分析用于白血病诊断是复杂的,因为大量的基因.
识别关键基因对于准确诊断疾病至关重要.

研究的目的:

通过微阵列数据和深度学习,提高白血病类型的诊断准确性.
开发一种选择基本诊断特征和序列的模型.
从样本数据中预测五种白血病类别.

主要方法:

使用具有U-Net架构的生成对抗网络 (GAN) 进行特征选择和序列处理,用于合成数据生成.
数据标记,通过隐藏马尔科夫模型 (HMM) 进行特征排名,并使用随机卷积内核转换 (ROCKET) 进行分类.
整合原始和合成数据进行全面分析.

主要成果:

拟议的深度学习模型实现了高分类准确率99.26%.
与现有的诊断方法相比,该模型表现出了优越的性能.
已经成功预测了五种不同类型的白血病.

结论:

利用DNA变化和遗传突变显著改善白血病诊断.
识别基因组修改有助于预测白血病风险,并促进早期检测.
这项研究强调了先进的计算方法在及时进行白血病干预方面的潜力.

关键词:

基因基因基因基因基因基因隐藏的马尔科夫模型在白血病中,白血病.微阵列的微阵列随机卷积内核转换随机卷积内核转换

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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...