Accurate prediction of pan-cancer types using machine learning with minimal number of DNA methylation sites

DNA methylation analysis has been applied to determine the primary site of cancer;however, robust and accurate prediction of cancer types with a minimum number of sites is still a significant scientific challenge. To build an accurate and robust cancer type prediction tool with a minimum number of DNA methylation sites, we internally benchmarked different DNA methylation site selection and ranking procedures, as well as different classification models. We used The Cancer Genome Atlas dataset (26 cancer types with 8296 samples) to train and test models and used an independent dataset (17 cancer types with 2738 samples) for model validation. A deep neural network model using a combined feature selection procedure (named MethyDeep) can predict 26 cancer types using 30 methylation sites with superior performance compared with the known methods for both primary and metastatic cancers in independent validation datasets. In conclusion, MethyDeep is an accurate and robust cancer type predictor with the minimum number of DNA methylation sites;it could help the cost-effective clarification of cancer of unknown primary patients and the liquid biopsy-based early screening of cancers.

Simulation of higher-order topological phases and related topological phase transitions in a superconducting qubit

Higher-order topological phases give rise to new bulk and boundary physics,as well as new classes of topological phase transitions.While the realization of higher-order topological phases has been confirmed in many platforms by detecting the existence of gapless boundary modes,a direct determination of the higher-order topology and related topological phase transitions through the bulk in experiments has still been lacking.To bridge the gap,in this work we carry out the simulation of a twodimensional second-order topological phase in a superconducting qubit.Owing to the great flexibility and controllability of the quantum simulator,we observe the realization of higher-order topology directly through the measurement of the pseudo-spin texture in momentum space of the bulk for the first time,in sharp contrast to previous experiments based on the detection of gapless boundary modes in real space.Also through the measurement of the evolution of pseudo-spin texture with parameters,we further observe novel topological phase transitions from the second-order topological phase to the trivial phase,as well as to the first-order topological phase with nonzero Chern number.Our work sheds new light on the study of higher-order topological phases and topological phase transitions.