Knowledge-Based Framework for Selection of Genomic Data Compression Algorithms - Alourani, Abdullah; Tahir, Muhammad; Sardaraz, Muhammad; Khan, Muhammad Saud (Applied sciences, 2022)
The development of new sequencing technologies has led to a significant increase in biological data. The exponential increase in data has exceeded increases in computing power. The storage and analysis of the huge amount of data poses challenges for researchers. Data compression is used to reduce the size of data, which ultimately reduces the cost of data transmission over the Internet. The field comprises experts from two domains, i.e., computer scientists and biological scientists. Computer scientists develop programs to solve biological problems, whereas biologists use these programs. Computer programs need parameters that are usually provided as input by the users. Users need to know different parameters while operating these programs. Users need to configure parameters manually, which leads to being more time-consuming and increased chances of errors. The program selected by the user may not be an efficient solution according to the desired parameter. This paper focuses on automatic program selection for biological data compression. Forward chaining is employed to develop an expert system to solve this problem. The system takes different parameters related to compression programs from the user and selects compression programs according to the desired parameters. The proposed solution is evaluated by testing it with benchmark datasets using programs available in the literature.