Using iEBE-VISHNU hybrid model with the AMPT initial conditions, we study the higher order flow harmonics of identified hadrons in 2.76 A TeV Pb+Pb collisions. Comparison with the recent ALICE measurements at 20-30% centrality shows that our calculations nicely describe the data below 2 GeV, especially for the v2, v3 and v4 mass-orderings among pions, kaons and protons. We also extended the calculations to other centrality bins, which presents similar mass-ordering patterns for these flow harmonics as the ones observed at 20-30% centrality. In the later part of this article, we explore the development of vn mass ordering/splitting during the hadronic evolution through the comparison runs from iEBE-VISHNU hybrid model and pure hydrodynamics with different decoupling temperatures.

Cancer is a heterogeneous disease that is driven by the accumulation of both genetic and nongenetic alterations, so integrating multiomics data and extracting effective information from them is expected to be an effective way to predict cancer driver genes. In this paper, we first generate comprehensive instructive features for each gene from genomic, epigenomic, transcriptomic levels together with protein–protein interaction (PPI)-networks-derived attributes and then propose a novel semisupervised deep graph learning framework GGraphSAGE to predict cancer driver genes according to the impact of the alterations on a biological system. When applied to eight tumor types, experimental results suggest that GGraphSAGE outperforms several state-of-the-art computational methods for driver genes identification. Moreover, it broadens our current understanding of cancer driver genes from multiomics level and identifies driver genes specific to the tumor type rather than pan-cancer. We expect GGraphSAGE to open new avenues in precision medicine and even further predict drivers for other complex diseases.

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