ADRS-CNet: An adaptive dimensionality reduction selection and classification network for DNA storage clustering algorithms

• URL: http://arxiv.org/abs/2408.12751v2
• Date: Sun, 22 Sep 2024 03:10:02 GMT
• Title: ADRS-CNet: An adaptive dimensionality reduction selection and classification network for DNA storage clustering algorithms
• Authors: Bowen Liu, Jiankun Li,
• Abstract summary: Methods like PCA, UMAP, and t-SNE are commonly employed to project high-dimensional features into low-dimensional space. This paper proposes training a multilayer perceptron model to classify input DNA sequence features and adaptively select the most suitable dimensionality reduction method.
• Score: 8.295062627879938
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: DNA storage technology offers new possibilities for addressing massive data storage due to its high storage density, long-term preservation, low maintenance cost, and compact size. To improve the reliability of stored information, base errors and missing storage sequences are challenges that must be faced. Currently, clustering and comparison of sequenced sequences are employed to recover the original sequence information as much as possible. Nonetheless, extracting DNA sequences of different lengths as features leads to the curse of dimensionality, which needs to be overcome. To address this, techniques like PCA, UMAP, and t-SNE are commonly employed to project high-dimensional features into low-dimensional space. Considering that these methods exhibit varying effectiveness in dimensionality reduction when dealing with different datasets, this paper proposes training a multilayer perceptron model to classify input DNA sequence features and adaptively select the most suitable dimensionality reduction method to enhance subsequent clustering results. Through testing on open-source datasets and comparing our approach with various baseline methods, experimental results demonstrate that our model exhibits superior classification performance and significantly improves clustering outcomes. This displays that our approach effectively mitigates the impact of the curse of dimensionality on clustering models.

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