Matrix-based vector representations in neural networks for classifying molecular biology data.

Summary: Selecting an appropriate classifier is essential for achieving accurate classification. In this study, we propose novel neural network (NNs)-based alternatives to standard classifiers as support vector machines. NNs, particularly convolutional neural networks and transformer networks, have shown exceptional performance in processing image data. To leverage this capability, we explore methods for transforming 1D vector data into 2D matrix representations, enabling the application of NNs pre-trained on large-scale image datasets. Specifically, we introduce a new data restructuring technique based on Wigner transforms, and we compare many methods proposed in the literature. The effectiveness and robustness of our approach are assessed using various benchmark datasets, from peptide classification to DNA barcoding classification, demonstrating consistently strong performance. Availability and implementation: All source code and related resources used in this work are made publicly available at https://github.com/LorisNanni/Matrix-Representation-of-Vectors-in-Neural-Networks-for-Data-Classification.