identifying tumor cell
Identifying tumor cells at the single-cell level using machine learning - Genome Biology
Cancer is a disease that stems from the disruption of cellular state. Through genetic perturbations, tumor cells attain cellular states that give them proliferative advantage over the surrounding normal tissue [1]. The inherent variability of this process has hampered efforts to find highly effective common therapies, thereby ushering the need for precision medicine [2]. The scale of single-cell experiments is poised to revolutionize personalized medicine by effective characterization of the complete heterogeneity within a tumor for each individual patient [3, 4]. Recent expansion of single-cell sequencing technologies has exponentially increased the scale of knowledge attainable through a single biological experiment [5].
Identifying Tumor Cells in the Blood with Artificial Intelligence
Cancer diagnostics is an area of keen interest in the fight against cancer. Cancer often slips by routine checkups and veils itself until it's in later stages. At this point, patients have limited options, and the prognosis tends to be grim. Diagnostic research has become a key focus to catch cancer at early stages and give patients a significantly better chance. Biomarkers are the current go-to method for this, and circulating tumor cells are an example of a cellular biomarker.