Data trusts have emerged as a key governance model in the digital economy, enabling trusted trustees to manage user data securely and privately for value exchange. However, the limited data volume held by individual trustees and strict regulatory barriers to data sharing make complex applications, such as point-of-interest (POI) recommendations, challenging to implement. Furthermore, the inherent sparsity of user behavior records significantly degrades the performance of traditional recommendation systems. To address these challenges, we propose a federated POI recommendation architecture tailored for personal data trusteeship. To address these challenges, we propose a federated POI recommendation architecture tailored for personal data trusteeship. Transcending the conventional client–server paradigm, we design a tripartite framework comprising data owners, data trustees, and an aggregation server, which enables secure, cross-silo knowledge aggregation among institutional trustees. Additionally, we introduce an adaptive multi-factor fusion algorithm driven by semantic-level attention. This algorithm jointly models five key preference dimensions, dynamically weighting heterogeneous features to capture complex user-POI interactions and effectively mitigate data sparsity. Extensive experiments across three datasets demonstrate our system’s exceptional robustness, achieving a 4.00% improvement in Precision@10 over the best baseline.

The dynamic optimization of large-scale transportation networks presents significant challenges due to their complexity, stochasticity, and the need for real-time decision-making. In conventional methods, there is often a failure to employ fully the resources of big data present in cities, thus not being able to respond appropriately to fluctuations in traffic conditions. This paper introduces a novel enhanced big data-driven reinforcement learning (EBD-RL) algorithm for dynamic optimization of transportation networks, addressing these challenges by leveraging advanced machine learning techniques and heterogeneous data sources. We propose a hierarchical control framework that decomposes the global optimization problem into manageable sub-problems while maintaining network-wide coordination. The EBD-RL algorithm incorporates prioritized experience replay and adaptive exploration strategies to improve learning efficiency and stability in high-dimensional state spaces. Experiments on a realistic urban network show that our method is superior to six state-of-the-art methods. Results show that EBD-RL reduces total travel time by up to 30% compared to the best baseline under various traffic demands and connected autonomous vehicle penetration rates. Furthermore, the algorithm exhibits enhanced resilience to traffic incidents, achieving up to 33% faster network recovery time in severe disruption scenarios. These findings highlight the potential of big data-driven reinforcement learning approaches to significantly improve the efficiency, adaptability, and resilience of modern urban transportation systems.

The metaverse is a human-centric beyond-reality virtual world, in which people use virtual identities to live, work, and socialize. Due to the openness and sharing of metaverse applications, the virtual-real identity link (VRIL) may cause uncertainties and unpredictable risks. At present, the research on VRIL risks is still in its infancy and VRIL risk predictions lack a comprehensive theoretical system and methodological tool. In this paper, we first construct a VRIL attack model, according to which an attacker can link a user’s real and virtual identities together using the information observed in the real and virtual worlds. Then we propose the tuple frequency-based VRIL prediction (TupPre) model and discover the population distribution, recursive hypergeometric (RH) distribution, and approximate binomial distribution of the tuple frequency (i.e., the occurrence times of attribute value combinations) given incomplete information. Focusing on the tuple frequency estimation error in biased samples, we introduce attribute value correlation knowledge to improve the prediction performance. The experimental results on generated and real-world datasets show that the TupPre model has excellent performance, with a mean area under the curves (AUCs) of 0.86 to 0.98 on these datasets, and it performs even more superior with certain background knowledge (mean AUC 0.95~0.98). The discovered basic distribution rules of the tuple frequency and the proposed quantitative analysis method for metaverse VRIL risk predictions construct the foundation of the identity privacy framework for the metaverse.

With the development of high-throughput genotyping technology, single nucleotide polymorphism (SNP)-SNP interactions (SSIs) detection has become an essential way for understanding disease susceptibility. Various methods have been proposed to detect SSIs. However, given the disease complexity and bias of individual SSI detectors, these single-detector-based methods are generally unscalable for real genome-wide data and with unfavorable results. We propose a novel ensemble learning-based approach (ELSSI) that can significantly reduce the bias of individual detectors and their computational load. ELSSI randomly divides SNPs into different subsets and evaluates them by multi-type detectors in parallel. Particularly, ELSSI introduces a four-stage pipeline (generate, score, switch and filter) to iteratively generate new SNP combination subsets from SNP subsets, score the combination subset by individual detectors, switch high-score combinations to other detectors for re-scoring, then filter out combinations with low scores. This pipeline makes ELSSI able to detect high-order SSIs from large genome-wide datasets. Experimental results on various simulated and real genome-wide datasets show the superior efficacy of ELSSI to state-of-the-art methods in detecting SSIs, especially for high-order ones. ELSSI is applicable with moderate PCs on the Internet and flexible to assemble new detectors. The code of ELSSI is available at https://www.sdu-idea.cn/codes.php?name=ELSSI.

MOTIVATION: Analysis of gene expression data can be crucial for elucidating biological relationships within living organisms. However, accurate quantification of gene expression relies directly upon the accuracy of the reference genome or transcriptome to which the expression data are mapped. Errors in gene annotation can lead to errors in the quantification of gene expression. One source of gene annotation error in eukaryotes arises from incorrect predictions of messenger RNA gene models within ribosomal DNA (rDNA) regions. RESULTS: Here, we provide examples of how the presence of false gene models in rDNA regions can result in a handful of genes appearing to contribute to >50% of the total transcripts per million values of entire RNA-seq datasets. To this end, we have created riboCleaner, a bioinformatics pipeline designed to identify misannotated gene models in rDNA regions and quantify rRNA-derived reads in RNA-seq data. We also show the applicability of riboCleaner in several plant genome assemblies. AVAILABILITY AND IMPLEMENTATION: We have implemented riboCleaner as a containerized Snakemake workflow. The workflow, instructions for building the container and other documentation are available at https://github.com/basf. The data underlying this article are available in GitHub at https://github.com/basf/riboCleaner. For convenience, a prebuilt Docker image containing riboCleaner is available at https://hub.docker.com/u/basfcontainers. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, we propose a Distance-based Sequence Indexing Method (DSIM) for indexing and searching genome databases. Borrowing the idea of video compression, we compress the genomic sequence database around a set of automatically selected reference words, formed from high-frequency data substrings and substrings in past queries. The compression captures the distance of each non-reference word in the database to some reference word. At runtime, a query is processed by comparing its substrings with the compressed data strings, through their distances to the reference words. We also propose an efficient scheme to incrementally update the reference words and the compressed data sequences as more data sequences are added and new queries come along. Extensive experiments on a human genome database with 2.62 GB of DNA sequence letters show that the new algorithm achieves significantly faster response time than BLAST, while maintaining comparable accuracy.

The growing interest in genomic research has caused an explosive growth in the size of DNA (deoxyribonucleic acid) databases making it increasingly challenging to perform searches on them. In this paper, we proposed an index structure called the ed-tree for supporting fast and effective homology searches on DNA databases. The ed-tree is developed to enable probe-based homology search algorithms like Blastn which generate short probe strings from the query sequence and then match them against the sequence database in order to identify potential regions of high similarity to the query sequence. Unlike Blastn however, the homology search algorithm we developed for ed-tree supports more flexible probe model with longer probes and more relaxed matching. As a consequence, the ed-tree is not only more effective and efficient than the latest Blastn (NCBI Blast2) when supporting homology search but also takes up moderate storage compared to existing data structures like the suffix tree. To index a DNA database of 2 giga base pairs (Gbps), ed-tree only takes less than 3Gb of secondary storage, which is easily handled by a desktop PC. Experiments will be shown in this paper to support our claim.