BACKGROUND: Tumor Mutation Burden (TMB) is commonly characterized as the number of non-synonymous somatic SNVs per megabase within the gene region identified through whole exon sequencing or targeted sequencing in a tumor sample. It has been statistically demonstrated that TMB was related to the ability of neoantigen production and used to predict the efficacy of immunotherapy for various types of cancers. However, screening for TMB in patients poses challenges due to the extensive labor and financial resources required for the preparation of large quantities of parallel sequencing libraries. RESULTS: In this study, we employed compressed sensing (CS) to calculate TMB from overlapped pooling sequencing data, aiming to reduce the sequencing cost by minimizing the number of library builds. Over 90% SNPs could still be detected without a significant loss of mutation information even when the data is pooled from ten different samples. Based on this, the orthogonal matching pursuit (OMP) algorithm and the basic pursuit (BP) algorithm were used to reconstruct TMB from pooling sequencing data. The performance of these two algorithms was evaluated. The BP algorithm consistently performed well across all cases, albeit necessitating extended computational time. The OMP algorithm has been proved to be suitable for scenarios where the original matrix was sparse but it showed low overall performance. Based on an accurate calculation of TMB, we determined that the number of sequencing runs could be reduced to 0.6 times the total number of samples, resulting in a 40% reduction in sequencing cost. CONCLUSIONS: In conclusion, we calculated TMB from overlapped pooling sequencing data utilizing compressed sensing strategy to reduce sequencing cost. Our findings confirm that the SNP calling from ten samples' pooling sequencing data is feasible. Additionally, we performed an assessment of the reconstruction efficiency of both the BP model and the OMP model.

Next Point-of-Interests (POIs) recommendation task aims to provide a dynamic ranking of POIs based on users' current check-in trajectories. The recommendation performance of this task is contingent upon a comprehensive understanding of users' personalized behavioral patterns through Location-based Social Networks (LBSNs) data. While prior studies have adeptly captured sequential patterns and transitional relationships within users' check-in trajectories, a noticeable gap persists in devising a mechanism for discerning specialized behavioral patterns during distinct time slots, such as noon, afternoon, or evening. In this paper, we introduce an innovative data structure termed the ``Mobility Tree'', tailored for hierarchically describing users' check-in records. The Mobility Tree encompasses multi-granularity time slot nodes to learn user preferences across varying temporal periods. Meanwhile, we propose the Mobility Tree Network (MTNet), a multitask framework for personalized preference learning based on Mobility Trees. We develop a four-step node interaction operation to propagate feature information from the leaf nodes to the root node. Additionally, we adopt a multitask training strategy to push the model towards learning a robust representation. The comprehensive experimental results demonstrate the superiority of MTNet over eleven state-of-the-art next POI recommendation models across three real-world LBSN datasets, substantiating the efficacy of time slot preference learning facilitated by Mobility Tree.

User mobility behaviors frequently exhibit a spatial clustering phenomenon, wherein points of interest (POIs) visited by the same user tend to be in close proximity. Consequently, leveraging geographical influences for user preference modeling remains a prevalent approach in POI recommendation tasks. However, existing studies often overlook users’ hidden geographical habits for the following reasons: (1) Geographical features are commonly approximated by manually partitioned regions or fixed distributions, inadequately capturing the nuanced spatial proximity among POIs. (2) POIs with high geographical correlations are not explicitly incorporated as feedback signals during the training process, resulting in a lack of spatial clustering pattern learning within users’ preference representations. This paper introduces GeoCo, aGeographicalCorrelation enhanced network for POI recommendation. First, we model POIs’ geographical features using fine-grained hierarchical sequences to capture multilevel spatial relations. Subsequently, we propose a pre-training network that employs the sentence similarity assessment technique to comprehend the semantics of geographical correlations. Second, we introduce a novel multi-objective training process that intuitively learns spatial clustering patterns through user mobility behaviors. Extensive experiments conducted on two location-based social network (LBSN) datasets, Gowalla and Foursquare, demonstrate the superiority of our proposed model over fourteen state-of-the-art baseline models in POI recommendation tasks. Compared with the baselines, GeoCo has achieved a performance improvement of at least 5$\%$in Rec@5 and HR@5 on both datasets. Furthermore, we verify the effectiveness of pre-trained location vectors and the multi-objective training process in enhancing the model's understanding of geographical correlations for user preference construction.

With the rapid development of mobile technology, it is very convenient to share people’s current locations by checking-in on Location-Based Social Networks (LBSNs). Using users’ check-in histories to study mobility preferences and recommend new locations is a typical application to LBSNs. Most existing models explore reasonable representations for users and locations. However, a lack of behavioral mobility modeling would hamper a better understanding of users’ mobility patterns. This paper proposes a location recommendation model to serve the personalized LBSNs application, called Spatio-temporal Individual mobility graph encoding network with Group Mobility Assistance (SIGMA). We design a spatio-temporal interaction enhanced graph neural network to encode the mobility graphs to represent individual mobility behaviors. Furthermore, we provide a novel stacked scoring approach to generate the recommendation score by combining the stacked individual mobility graphs with the group influences. We conduct extensive experiments on two real-world LBSNs data, Foursquare and Gowalla. The result demonstrates SIGMA outperforms ten state-of-the-art models and further confirms that both the individual and the group mobility behaviors play essential roles in the practical scenario of location recommendation.