A Review of Data Mining and Knowledge Discovery Approaches for Bioinformatics
Keywords:Bioinformatics, Data Mining, Knowledge Discovery Database, Gene Ontology, Similarity Function
This review explores the Knowledge Discovery Database (KDD) approach, which supports the bioinformatics domain to progress efficiently, and illustrate their relationship with data mining. Thus, it is important to extract advantages of Data Mining (DM) strategy management such as effectively stressing its role in cost control, which is the principle of competitive intelligence, and the role of it in information management. As well as, its ability to discover hidden knowledge. However, there are many challenges such as inaccurate, hand-written data, and analyzing a large amount of variant information for extracting useful knowledge by using DM strategies. These strategies are successfully applied in several applications as data warehouses, predictive analytics, business intelligence, bioinformatics, and decision support systems. There are many DM techniques that are applied for disease diagnostics and treatment, for example cancer diseases that are investigated using multi-layer perception, Naïve Bayes, Decision Tree, Simple Logistic, K-Nearest Neighbor. As will be explored in this paper. Consequently, for future perspectives there is research in progress for real Iraqi data of Breast Cancer using Data Mining techniques, specifically the Tree decision and K-nearest algorithms.