(untitled) (INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2014)
Bioinformatics analyses huge amounts of biological data that demands in-depth understanding. On the other hand, data mining research develops methods for discovering motifs in biosequences. Motif discovery involves benefits and challenges. We show bridge of the two fields, data mining and Bioinformatics, for successful mining of biological data. We found the motivation and justification factors lead to preferring naturalistic method research for Bioinformatics, because naturalistic method depends on real data. The method empowers Bioinformatics techniques to handle the true properties and reducing assumptions for un-modeled or uncover biodata phenomena. The empowerment comes from recognizing and understanding biodata properties and processes.