Computational intelligence poses several possibilities in Bioinformatics, particularly by generating low-cost, low-precision, good solutions. Rough sets promise to open up an important dimension in this direction. The present article surveys the role of artificial neural networks, fuzzy sets and genetic algorithms, with particular emphasis on rough sets, in Bioinformatics. Since the work entails processing huge amounts of incomplete or ambiguous biological data, the knowledge reduction capability of rough sets, learning ability of neural networks, uncertainty handling capacity of fuzzy sets and searching potential of genetic algorithms are synergistically utilized.