With the explosive growth in the number of protein sequences generated in the postgenomic age, research into identifying cytokines from proteins and detecting their biochemical mechanisms becomes increasingly important. Unfortunately, the identification of cytokines from proteins is challenging due to a lack of understanding of the structure space provided by the proteins and the fact that only a small number of cytokines exists in massive proteins. In view of fact that a proteins sequence is conceptually similar to a mapping of words to meaning, n-gram, a type of probabilistic language model, is explored to extract features for proteins. The second challenge focused on in this work is genetic algorithms, a search heuristic that mimics the process of natural selection, that is utilized to develop a classifier for overcoming the protein imbalance problem to generate precise prediction of cytokines in proteins. Experiments carried on imbalanced proteins data set show that our methods outperform traditional algorithms in terms of the prediction ability.