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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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一种基于机器学习和深度学习的集成多omics技术,用于白血病预测.

Erum Yousef Abbasi1, Zhongliang Deng1, Qasim Ali2

  • 1State Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, China.

Heliyon
|February 14, 2024
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概括

这项研究引入了一种新的白血病诊断方法,使用机器学习 (ML) 和深度学习 (DL) 在多omics数据上进行诊断. 深度学习,特别是循环神经网络 (RNN),实现了98%的准确性,超过了ML方法用于血癌预测.

关键词:
深度学习是一种深度学习.基因组学就是基因组学.在白血病中,白血病.机器学习是机器学习.多个omics的多个omics.

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科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 人工智能的人工智能

背景情况:

  • 扩大癌症数据为更好地理解和个性化治疗提供了机会.
  • 人工智能 (AI),特别是机器学习 (ML) 和深度学习 (DL),对于分析多omics数据来预测白血病等血液癌症至关重要.
  • 需要新的方法来有效处理和解释大量的生物数据,以改善诊断.

研究的目的:

  • 引入和评估一种使用综合多omics数据进行白血病诊断的新方法.
  • 为了比较各种ML和DL算法的诊断准确度,用于白血病预测.
  • 确定最佳的ML或DL技术,以准确有效地诊断白血病.

主要方法:

  • 使用各种ML算法集成的多omics数据的分析:随机森林 (RF),天真贝叶斯 (NB),决策树 (DT),物流回归 (LR) 和梯度提升 (GB).
  • 将ML技术与DL方法进行比较:循环神经网络 (RNN) 和前神经网络 (FNN).
  • 基于17个特征的验证,包括患者人口统计,突变类型和染色体数据.

主要成果:

  • 梯度提升 (GB) 在评估的ML技术中实现了97%的准确性.
  • 循环神经网络 (RNN) 在DL方法中以98%的准确性表现出卓越的性能.
  • 提出的方法有效地过了未经分类的数据,突出了DL在白血病预测方面的有效性.

结论:

  • 深度学习 (DL) 方法,特别是RNN,显示出使用多omics数据准确预测白血病的重大前景.
  • 该研究验证了高通量技术和先进算法的有效性,改善了医疗保健诊断和患者护理.
  • 将ML和DL技术进行比较,发现DL是优化白血病诊断准确性的优越方法.