Background This study aimed to establish an artificial neural network (ANN) model based on variant pathways to predict the risk of thyroid cancer. Methods The RNASeq data of 482 thyroid cancer samples were downloaded from the TCGA database. The samples were divided into low-risk and high-risk groups, followed by identification of differentially expressed genes (DEGs). Co-expression analysis and pathway enrichment analysis were then performed. The variant pathways were screened according to the functional deviation score of each pathway, and an ANN model was established. Finally, the efficiency of the ANN model for risk assessment was validated by survival analysis and analysis of an independent microarray dataset (GSE34289) for thyroid cancer. Results In total, 190 DEGs (85 up-regulated and 105 down-regulated) were identified between the low-risk and high-risk groups. Ten risk-related variant pathways were identified between the low-risk and high-risk groups, which were related to inflammatory and immune responses. Based on these variant pathways, an ANN model was built, consisting of an input layer, two hidden layers, and an output layer, corresponding to 15, 8, 5, and 1 neuron, respectively. Survival analysis showed that this model could effectively distinguish the samples with different risks. Analysis of microarray dataset GSE34289 showed that the accuracy of this model for predicating low-risk and high-risk samples was 77.5 and 86.0%, respectively. Conclusions This study suggests that the ANN model based on variant pathways can be used for effectively evaluating the risk of thyroid cancer. Electronic supplementary material The online version of this article (10.1186/s12881-019-0829-4) contains supplementary material, which is available to authorized users.

Interfacial thermal resistances between heterogeneous materials are still a challengeable subject since the mechanism to explain it quantitatively is not clear in spite of its importance. We propose a Monte Carlo (MC) model to study phonon interfacial elastic and inelastic scattering behaviors for superlattices composed of Si and Ge materials, which substantially reduces the amount of computations. In particular, below Debye temperatures, the molecular dynamics (MD) solution is not precise enough for semiconductors because of quantization errors. In this work, thermal conductivities and thermal rectifications of Si/Ge and Ge/Si superlattices with different periods are investigated separately at temperatures below 200 K.