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].