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The human papillomavirus (HPV), a common virus that infects the reproductive tract, may lead to malignant changes within the infection area in certain cases and is directly associated with such cancers as cervical cancer, anal cancer, and vaginal cancer. Identification of novel HPV infection related genes can lead to a better understanding of the specific signal pathways and cellular processes related to HPV infection, providing information for the development of more efficient therapies. In this study, several novel HPV infection related genes were predicted by a computation method based on the known genes involved in HPV infection from HPVbase. This method applied the algorithm of random walk with restart (RWR) to a protein-protein interaction (PPI) network. The candidate genes were further filtered by the permutation and association tests. These steps eliminated genes occupying special positions in the PPI network and selected key genes with strong associations to known HPV infection related genes based on the interaction confidence and functional similarity obtained from published databases, such as STRING, gene ontology (GO) terms and KEGG pathways. Our study identified 104 novel HPV infection related genes, a number of which were confirmed to relate to the infection processes and complications of HPV infection, as reported in the literature. These results demonstrate the reliability of our method in identifying HPV infection related genes. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang. [ABSTRACT FROM AUTHOR]

Mapping dyke and dyke-related fractures is important for magma propagation and emplacement mechanisms analysis. Here, we propose a semi-automatic workflow for dyke and fracture mapping using unmanned aerial vehicle (UAV)-based photogrammetric data. Our workflow integrates mean-shift and GrabCut in one cut (GCOC) algorithms to segment dykes, and harnesses the phase symmetry algorithm and a series of follow-up actions to detect fractures. Sijiao Island of Zhejiang Province (SE China) is examined as a case study to validate the approaches. With our workflow, the generated dyke map attains more than a 97% recall rate when compared with the results of expert interpretation, and the ratio of the number of detected false fracture segments to the number of total segments is less than 18%. The dyke strike and fracture strike calculation match well with field measurements. Moreover, by combining visual interpretation and the resulting geometric information, our results manifest that dyke magma invaded along pre-existing fractures to form dykes on Sijiao Island, and the difference between dyke fractures and host rock fractures may be because of the effect of major changes of regional stress. This workflow has great potential for high-resolution terrestrial mapping. • Integrating mean-shift and GrabCut in one cut algorithms improves the accuracy of dyke segmentation. • Use of the phase symmetry algorithm and a series of subsequent actions decreases the detection error rate of fractures. • Semi-automatic extraction of dykes and fractures ensures the objectivity and efficiency of data analysis. • The generated maps contribute to determining the dyke intrusion model of Sijiao Island. [ABSTRACT FROM AUTHOR]