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DNA damage is a critical factor in the onset and progression of cancer. When DNA is damaged, the number of genetic mutations increases, making it necessary to activate DNA repair mechanisms. A crucial factor in the base excision repair process, which helps maintain the stability of the genome, is an enzyme called DNA polymerase $\boldsymbol{\beta}$β (Pol$\boldsymbol{\beta}$β) encoded by the POLB gene. It plays a vital role in the repair of damaged DNA. Additionally, variations known as Single Nucleotide Polymorphisms (SNPs) in the POLB gene can potentially affect the ability to repair DNA. This study uses bioinformatics tools that extract important features from SNPs to construct a feature matrix, which is then used in combination with machine learning algorithms to predict the likelihood of developing cancer associated with a specific mutation. Eight different machine learning algorithms were used to investigate the relationship between POLB gene variations and their potential role in cancer onset. This study not only highlights the complex link between POLB gene SNPs and cancer, but also underscores the effectiveness of machine learning approaches in genomic studies, paving the way for advanced predictive models in genetic and cancer research.

Purpose: The purpose of this study is to examine the critical failure factors (CFFs) linked to various types of process improvement (PI) projects such as Kaizen, Lean, Six Sigma, Lean Six Sigma and Agile. Proposing a mitigation framework accordingly is also an aim of this study. Design/methodology/approach: This research undertakes a systematic literature review of 49 papers that were relevant to the scope of the study and that were published in four prominent databases, including Google Scholar, Scopus, Web of Science and EBSCO. Findings: Further analysis identifies 39 factors that contribute to the failure of PI projects. Among these factors, significant emphasis is placed on issues such as “resistance to cultural change,” “insufficient support from top management,” “inadequate training and education,” “poor communication” and “lack of resources,” as primary causes of PI project failures. To address and overcome the PI project failures, the authors propose a framework for failure mitigation based on change management models. The authors present future research directions that aim to enhance both the theoretical understanding and practical aspects of PI project failures. Practical implications: Through this study, researchers and project managers can benefit from well-structured guidelines and invaluable insights that will help them identify and address potential failures, leading to successful implementation and sustainable improvements within organizations. Originality/value: To the best of the author’s knowledge, this paper is the first study of its kind to examine the CFFs of five PI methodologies and introduces a novel approach derived from change management theory as a solution to minimize the risk associated with PI failure.