GSP is not only limited to the analysis of DNA sequences. It can also be applied to other types of biological data. For instance:

1. Microarray Data Analysis: Microarray technology allows researchers to monitor the expression levels of thousands of genes simultaneously. GSP can help in analyzing microarray data by identifying patterns and trends in gene expression, which can provide insights into various biological processes and disease mechanisms.
2. Protein Sequence Analysis: GSP techniques can also be used to analyze protein sequences. Similar to DNA sequences, protein sequences can be represented numerically and analyzed using various signal processing techniques. This can help in identifying protein families, predicting protein structures, and understanding protein functions.
3. Genomic Image Processing: Some areas of genomics involve the analysis of images, such as karyotyping or fluorescence in situ hybridization (FISH) images. GSP methods can be used to enhance these images, segment them into meaningful regions, and extract features for further analysis.
4. Genome Analysis: Identifying structural features and patterns within genomes.
5. Gene Prediction: Predicting the locations of genes within a DNA sequence.
6. Mutation Detection: Detecting and characterizing mutations within genetic sequences.