  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 (NCD)
algorithm to achieve better performance on classification tasks without relying
on handcrafted features or pre-trained models. Firstly, we compress the
molecular sequence using well-known compression algorithms, such as Gzip and
Bz2. By leveraging the latent structure encoded in compressed files, we compute
the Normalized Compression Distance between each pair of molecular sequences,
which is derived from the Kolmogorov complexity. This gives us a distance
matrix, which is the input for generating a kernel matrix using a Gaussian
kernel. Next, we employ kernel Principal Component Analysis (PCA) to get the
vector representations for the corresponding molecular sequence, capturing
important structural and functional information. The resulting vector
representations provide an efficient yet effective solution for molecular
sequence analysis and can be used in ML-based downstream tasks. The proposed
approach eliminates the need for computationally intensive Deep Neural Networks
(DNNs), with their large parameter counts and data requirements. Instead, it
leverages a lightweight and universally accessible compression-based model.
