在神经网络中基于矩阵的向量表示,用于分类分子生物学数据
Loris Nanni1, Sheryl Brahnam2, Daniel Fusaro1
1Department of Information Engineering, University of Padova, Padova 35131, Italy.
Bioinformatics advances
|December 15, 2025
概括
本研究引入了新的神经网络 (NN) 方法,以将矢量数据转换为矩阵表示,以改进分类. 这些技术利用了在图像数据上预先训练的NN,在各种数据集中实现了强大的性能.
科学领域:
- 机器学习 机器学习
- 生物信息学是一种生物信息学.
背景情况:
- 准确的分类依赖于适当的分类器选择.
- 像支向量机器这样的标准分类器也有局限性.
- 神经网络 (NN),特别是CNN和变压器,在图像数据处理方面表现出色.
研究的目的:
- 为标准分类器提出基于NN的新型替代方案.
- 通过将其转换为2D矩阵表示来适应NNs用于1D矢量数据分类.
- 为了利用在大规模图像数据集上预先训练的NN.
主要方法:
- 探索将1D矢量数据转换为2D矩阵表示的方法.
- 引入使用维格纳变换的新型数据重组技术.
- 拟议方法与现有文献方法的比较.
主要成果:
- 基于NN的方法的有效性和稳定性得到证明.
- 在各种基准数据集中保持一致的强表现.
- 在和DNA条形码分类任务中的成功应用.
结论:
- 拟议的矩阵表示技术可以有效地使用图像训练的NN来进行矢量数据分类.
- 这种方法为传统分类器提供了一个强大的替代方案.
- 该方法对生物信息学及其他领域的各种分类挑战具有前景.
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