Review of Machine Learning Approaches in the Analysis of Cancer Tumor Heterogeneity Using Single-Cell Sequencing

This paper aims to review the current method-ologies, advancements, and future directions in applications of Machine learning in Single-cell Sequencing of Cancer tumors, which will prove beneficial for the research community. Cancer is a threatening chronic disease that kills millions of people if diagnosed at advanced stages. Cancer tumors are heterogeneous for different cancer types and patients. Cancer treatments are advancing towards personalized medicines, taking into account the nature of cancer tumors and the patient's genetic profile. The genomic big data obtained through Single-cell sequencing techniques can assist in understanding the heterogeneity in different cancers, which can provide valuable insights into selecting an effective cancer therapy tailored to the type of cancer. This paper reviews the recent progress of machine learning and its applications in single-cell analysis of heterogeneity in cancers. The paper also addresses the possible future directions and advancements in the related fields.