This paper presents an approach that applies a combination of computing techniques, including image processing and analysis, syntactic pattern matching, clustering techniques and artificial neural networks to interpret biological data. The application domain being is the analysis of 1D SDS-PAGE gels of Atlantic salmon skin mucus. Researchers in our group have visually identified protein band intensity patterns in the salmon's skin mucus. The objective is to produce a system to minimize the loss of livestock in the fish farming industry. Initial results of the gel image analysis application and manual data analysis have shown that reproducible patterns exist within the gel band data and can be classified as either increasing or decreasing patterns. This type of analysis is not restricted to the analysis of Atlantic salmon skin mucus proteins, but can be extended to other proteins that exhibit recurring patterns over a period of time that require identification and classification.