Advances in liquid biopsy: Computational and artificial intelligence approaches in cancer research

Abstract

Liquid biopsy analysis has emerged as a promising approach for non-invasive cancer monitoring and diagnosis. This review provides an overview of the current landscape and future potential of liquid biopsy in cancer research, with a particular focus on the computational methodologies and techniques utilized for liquid biopsy data analysis and interpretation. The challenges and opportunities in extracting meaningful insights from the vast array of genomic, epigenomic, transcriptomic, and proteomic data are discussed, as well as the possibilities and current pitfalls of artificial intelligence approaches. In addition, the benefits and limitations of integrating multimodal cancer research data are covered, with a focus on advancing precision oncology and personalized medicine. By providing a critical assessment of the field, this review aims to foster knowledge about the available computational approaches and facilitate the choice of the most appropriate methodology for in silico investigation of liquid biopsy data, ultimately enhancing research endeavors and disease management strategies. Most of the works discussed in this review have emerged within the past 5 years, indicating a rapidly growing interest in this technique.