A Universal Non-Parametric Approach For Improved Molecular Sequence Analysis

In the field of biological research, it is essential to comprehend the characteristics and functions of molecular sequences. The classification of molecular sequences has seen widespread use of neural network-based techniques. Despite their astounding accuracy, these models often require a substantial number of parameters and more data collection. In this work, we present a novel approach based on the compression-based Model, motivated from \cite{jiang2023low}, which combines the simplicity of basic compression algorithms like Gzip and Bz2, with Normalized Compression Distance …

accuracy analysis classification collection cs.lg data data collection functions network neural network non-parametric parameters parametric q-bio.qm research work