Data Mining for Identification of Forkhead Box O (FOXO3a) in Different Organisms Using Nucleotide and Tandem Repeat Sequences

• Main Authors: Sabah Mayahi, Ahad Yamchi, Masood Golalipour, Majid Shahbazi
• Format: Article
• Language: English
• Published: Mazandaran University of Medical Sciences and Health Services 2020-02-01
• Series: Research in Molecular Medicine
• Subjects: decision tree; data mining; rapidminer; forkhead box o3
• Online Access: http://rmm.mazums.ac.ir/article-1-341-en.html
• ISSN: 2322-1348; 2322-133X

Background: Deregulation of FOXO3a gene which belongs to Forkhead box O (FOXO) transcription factors, can cause cancer (e.g. breast cancer). FOXO factors have important role in ubiquitination, acetylation, de-acetylation, protein-protein interactions and phosphorylation. Understanding the regulation and mechanisms of FOXO3a can lead to cancer treatment. The aim of this study recent association of data mining with genetics has provided a strong tool for knowledge discovery. Materials and Methods: Using genetics and bioinformatics, 30 sequences of FOXO3a genes were extracted from different species and were used in two datasets including 65 nucleotide features and 51 tandem repeat sequences. Then, we used different feature weighting and decision tree data mining algorithms on these datasets. Results: Among nucleotide features, the frequency of AA dinucleotide was the most important genomic feature for FOXO3a gene identification. Among tandem repeat sequences, the strings of TTTTTTTTT, GAGGAGGAG, CGGCGGCGGCGG and CGGCGGCGGCGGCGG were the most effective ones to distinguish FOXO3A gene between different species. Conclusion: The results of this study are important in understanding FOXO3a gene and developing a pathway for cancer and gene therapies in humans.