INTERNATIONAL CONGRESS OF
CancerGenetics
Bioinformatics in Oncology: Tools, Challenges, and Clinical Applications
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Mina Shirmohammadpour,1 Arman Taran,2 Bahman Mirzaei,3,*
1. Department of Microbiology and Virology, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
2. Department of Microbiology and Virology, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
3. Department of Microbiology and Virology, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
- Introduction: Cancer research has entered the era of big data, where bioinformatics plays a critical role in deciphering genomic, transcriptomic, and proteomic information for early diagnosis and personalized therapy. This review highlights the applications of bioinformatics in cancer detection, classification, drug response prediction, and identification of therapeutic targets.
- Methods: A review of original studies and meta-analyses from 2015 to 2024 was conducted. Emphasis was placed on next-generation sequencing (NGS) data analysis, cancer-specific databases, and AI-based prediction models.
- Results: Bioinformatics tools have enabled identification of driver mutations, differential gene expression profiles, and biomarkers in breast, lung, and colorectal cancers. Machine learning algorithms are used for cancer subtype classification and survival prediction. Tools such as TCGA, GEO, and cBioPortal provide integrated platforms for data mining. However, data heterogeneity, standardization issues, and interpretation of variants of unknown significance remain major challenges.
- Conclusion: Bioinformatics is revolutionizing cancer research by transforming complex molecular data into actionable clinical insights. Continued advances in computational tools and data integration are essential for the realization of true precision oncology.
- Keywords: Bioinformatics, Cancer genomics, Precision oncology, Machine learning, Biomarkers, NGS
