Relation between Bioinformatics and Computational Statistics in Cancer: A Survey
DOI:
https://doi.org/10.63282/3050-922X.ICAILLMBA-107Keywords:
Bioinformatics, Computational Statistics, Cancer Research, Genomic Data Analysis, Machine LearningAbstract
Cancer research has increasingly embraced bioinformatics and computational statistics to interpret large-scale and complex biological data generated by modern high-throughput technologies [14], [15]. Advances in sequencing and profiling platforms have produced extensive genomic, transcriptomic, and proteomic datasets that require sophisticated analytical techniques for meaningful interpretation [3], [16]. This survey explores how statistical modelling, machine learning, and data-driven approaches enable effective knowledge extraction from cancer data. Key applications such as cancer detection, subtype identification, prognosis estimation, and treatment response prediction are reviewed [4], [6]. In addition, this study addresses methodological challenges, including data heterogeneity, scalability, and interpretability, while discussing recent developments that advance precision oncology and personalised cancer therapy [1], [11].
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