BACKGROUND: The exploration of gene-disease associations is crucial for understanding the mechanisms underlying disease onset and progression, with significant implications for prevention and treatment strategies. Advances in high-throughput biotechnology have generated a wealth of data linking diseases to specific genes. While graph representation learning has recently introduced groundbreaking approaches for predicting novel associations, existing studies always overlooked the cumulative impact of functional modules such as protein complexes and the incompletion of some important data such as protein interactions, which limits the detection performance. RESULTS: Addressing these limitations, here we introduce a deep learning framework called ModulePred for predicting disease-gene associations. ModulePred performs graph augmentation on the protein interaction network using L3 link prediction algorithms. It builds a heterogeneous module network by integrating disease-gene associations, protein complexes and augmented protein interactions, and develops a novel graph embedding for the heterogeneous module network. Subsequently, a graph neural network is constructed to learn node representations by collectively aggregating information from topological structure, and gene prioritization is carried out by the disease and gene embeddings obtained from the graph neural network. Experimental results underscore the superiority of ModulePred, showcasing the effectiveness of incorporating functional modules and graph augmentation in predicting disease-gene associations. This research introduces innovative ideas and directions, enhancing the understanding and prediction of gene-disease relationships.

Although Wi-Fi is an ideal technology for many ranging applications, the performance of current methods is limited by the system bandwidth, leading to low accuracy of ~1 m. For many applications, measuring differential range, viz., the change in the range between adjacent measurements, is sufficient. Correspondingly, this work proposes WiDRa - a Wi-Fi based Differential Ranging solution that provides differential range estimates by using the sum-carrier-phase information. The proposed method is not limited by system bandwidth and can track range changes even smaller than the carrier wavelength. The proposed method is first theoretically justified, while taking into consideration the various hardware impairments affecting Wi-Fi chips. In the process, methods to isolate the sum-carrier phase from the hardware impairments are proposed. Extensive simulation results show that WiDRa can achieve a differential range estimation root-mean-square-error (RMSE) of$\approx 1$mm in channels with a Rician-factor$\geq 7$(a$100 \times $improvement to existing methods). The proposed methods are also validated on off-the-shelf Wi-Fi hardware to demonstrate feasibility, where they achieve an RMSE of <1 mm in the differential range. Finally, limitations of current investigation and future directions of exploration are suggested, to further tap into the potential of WiDRa.

MOTIVATION: Beta-diversity quantitatively measures the difference among microbial communities thus enlightening the association between microbiome composition and environment properties or host phenotypes. The beta-diversity analysis mainly relies on distances among microbiomes that are calculated by all microbial features. However, in some cases, only a small fraction of members in a community plays crucial roles. Such a tiny proportion is insufficient to alter the overall distance, which is always missed by end-to-end comparison. On the other hand, beta-diversity pattern can also be interfered due to the data sparsity when only focusing on nonabundant microbes. RESULTS: Here, we develop Flex Meta-Storms (FMS) distance algorithm that implements the "local alignment" of microbiomes for the first time. Using a flexible extraction that considers the weighted phylogenetic and functional relations of microbes, FMS produces a normalized phylogenetic distance among members of interest for microbiome pairs. We demonstrated the advantage of FMS in detecting the subtle variations of microbiomes among different states using artificial and real datasets, which were neglected by regular distance metrics. Therefore, FMS effectively discriminates microbiomes with higher sensitivity and flexibility, thus contributing to in-depth comprehension of microbe-host interactions, as well as promoting the utilization of microbiome data such as disease screening and prediction. AVAILABILITY AND IMPLEMENTATION: FMS is implemented in C++, and the source code is released at https://github.com/qdu-bioinfo/flex-meta-storms.

Millimeter wave (mmWave) communication is a key component of 5G systems and beyond. Such systems provide high bandwidth and high data rate but are sensitive to blockages. A sudden blockage in the line of sight (LOS) link leads to abrupt disconnection. Thus addressing blockage problems is essential for enhancing the reliability and latency of mmWave communication networks. In this paper, we propose a novel solution that relies only on in-band mmWave wireless measurements to proactively predict future dynamic line-of-sight (LOS) link blockages. The proposed solution utilizes deep neural networks and special patterns of received signal power, which we call pre-blockage wireless signatures, to infer future blockages. Specifically, the machine learning models attempt to predict: (i) Whether a blockage will occur in the next few seconds? (ii) At what time instance will this blockage occur? To evaluate our proposed approach, we build a mmWave communication setup with moving blockage in an indoor scenario and collect received power sequences. Simulation results on a real dataset show that blockage occurrence can be predicted with more than 85% accuracy, and the exact time instance of blockage occurrence can be obtained with less than 2 time instances (1.66s) error for prediction interval of 10 time instances (8.8s). This demonstrates the potential of the proposed solution for dynamic blockage prediction and proactive hand-off.

Line-of-sight link blockages represent a key challenge for the reliability and latency of millimeter wave (mmWave) and terahertz (THz) communication networks. This paper proposes to leverage LiDAR sensory data to provide awareness about the communication environment and proactively predict dynamic link blockages before they happen. This allows the network to make proactive decisions for hand-off/beam switching which enhances its reliability and latency. We formulate the LiDAR-aided blockage prediction problem and present the first real-world demonstration for LiDAR-aided blockage prediction in mmWave systems. In particular, we construct a large-scale real-world dataset, based on the DeepSense 6G structure, that comprises co-existing LiDAR and mmWave communication measurements in outdoor vehicular scenarios. Then, we develop an efficient LiDAR data denoising (static cluster removal) algorithm and a machine learning model that proactively predicts dynamic link blockages. Based on the real-world dataset, our LiDAR-aided approach is shown to achieve 95% accuracy in predicting blockages happening within 100ms and more than 80% prediction accuracy for blockages happening within one second. If used for proactive hand-off, the proposed solutions can potentially provide an order of magnitude saving in the network latency, which highlights a promising direction for addressing the blockage challenges in mmWave/sub-THz networks.

In recent years, with the development of high-throughput technologies and the increase of omics data, molecular diagnostics and the analysis of patients' exact causative genes become possible. However, massive data also challenges traditional methods. GWAS(Genome-Wide Association Study) traditional methods are usually used for causative gene discovery of single-gene disorders. But because they needs to do statistical test and analysis to each SNP loci; High-throughput genomic data show that there are usually millions of such SNP(Single Nucleotide Polymorphism) loci, so it may take longer times and more resources to discover the exact causative genes of patients. In this regard, we use a prediction strategy which integrates the knowledge of both bioinformatics and complex networks to discover the exact causative gene from given candidate genes. The strategy is: first, build a protein interaction network and choose the core disease gene; second, calculate similarities between candidate genes and core gene for each patient; third, sort the similarity of each patient in descending order, so that the top-ranking genes are the potential causative genes. Based on the above policy, local, semi-local, global indicators are compared in this paper. In addition, the paper also proposes a similarity index based on asymmetric similarity. Experimental results show that taking into account the asymmetry interaction for selecting different core gene, the new method based on asymmetric similarity achieved the best discovery result, which also proves the effectiveness of the new method.

One reason for semi-supervised clustering fail to deliver satisfactory performance in document clustering is that the transformed optimization problem could have many candidate solutions, but existing methods provide no mechanism to select a suitable one from all those candidates. This paper alleviates this problem by posing the same task as a soft-constrained optimization problem, and introduces the salient degree measure as an information guide to control the searching of an optimal solution. Experimental results show the effectiveness of the proposed method in the improvement of the performance, especially when the amount of priori domain knowledge is limited.