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User identification is to identify the online accounts’ identity, which is a critical problem in many applications. The key problem in that is to find a typical pattern from the online accounts data. Different from the previous works, we identify users based on the camera fingerprint. The underlying idea is that the accounts belonging to the same individual contain the photos taken by the same cameras. Based on that, we propose a new framework to deal with the user identification problem. Specifically, the camera feature of each image is extracted by the PRNU (Photo Response Non-Uniformity) algorithm. With the proposed hierarchical clustering approach, the camera features of different camera source can be achieved. Extensive experiments on a collected photo dataset show that the proposed framework is effective for identifying users, especially in the scenarios that users have multiple cameras and photos forwarding behavior.

Tumor purity is the proportion of tumor cells in the sampled admixture. Estimating tumor purity is one of the key steps for both understanding the tumor micro-environment and reducing false positives and false negatives in the genomic analysis. However, existing approaches often lose some accuracy when analyzing the samples with high degree of heterogeneity. The patterns of clonal architecture shown in sequencing data interfere with the data signals that the purity estimation algorithms expect. In this article, we propose a computational method, EMPurity, which is able to accurately infer the tumor purity of the samples with high degree of heterogeneity. EMPurity captures the patterns of both the tumor purity and clonal structure by a probabilistic model. The model parameters are directly calculated from aligned reads, which prevents the errors transferring from the variant calling results. We test EMPurity on a series of datasets comparing to three popular approaches, and EMPurity outperforms them on different simulation configurations.

It is suggested that the evolution of somatic mutations may be significant impacted by inherited polymorphisms, while the clonal somatic copy-number mutations may contribute to the potential selective advantages of heterozygous germline variants. A fine resolution on clonal architecture of such cooperative germline-somatic dynamics provides insight into tumour heterogeneity and offers clinical implications. Although it is reported that germline allelic imbalance patterns often play important roles, existing approaches for clonal analysis mainly focus on single nucleotide sites. To address this need, we propose a computational method, GLClone that identifies and estimates the clonal patterns of the copy-number alterations on germline variants. The core of GLClone is a hierarchical probabilistic model. The variant allelic frequencies on germline variants are modeled as observed variables, while the cellular prevalence is designed as hidden states and estimated by Bayesian posteriors. A variational approximation algorithm is proposed to train the model and estimate the unknown variables and model parameters. We examine GLClone on several groups of simulation datasets, which are generated by different configurations, and compare to three popular state-of-the-art approaches, and GLClone outperforms on accuracy, especially a complex clonal structure exists.