With the continuous advancement of Internet technology, public demand for healthcare services has been growing rapidly. However, the overall distribution of medical resources remains significantly unbalanced. To promote the development of hierarchical diagnosis and treatment systems, the construction of an Internet hospital platform that enables seamless interconnection and resource sharing among all participants in the healthcare process is of critical importance to deliver efficient and high-quality medical services. Currently, existing Internet hospital platforms face challenges such as limited scalability and inefficient medical data circulation. To address these problems, this study proposes a three-tier architecture for Internet hospital platforms by designing a unified overall structure. Based on this architecture, we develop a data exchange platform that can effectively meet the needs of various participants in the medical system. Application results demonstrate that the platform offers high scalability and effectively enhances the efficiency of medical resource circulation.

MOTIVATION: Studies on pan-cancer related genes play important roles in cancer research and precision therapy. With the richness of research data and the development of neural networks, several successful methods that take advantage of multiomics data, protein interaction networks, and graph neural networks to predict cancer genes have emerged. However, these methods also have several problems, such as ignoring potentially useful biological data and providing limited representations of higher-order information. RESULTS: In this work, we propose a pan-cancer related gene predictive model, the DGHNN, which takes biological pathways into consideration, applies a deep graph and hypergraph neural network to encode the higher-order information in the protein interaction network and biological pathway, introduces skip residual connections into the deep graph and hypergraph neural network to avoid problems with training the deep neural network, and finally uses a feature tokenizer and transformer for classification. The experimental results show that the DGHNN outperforms other methods and achieves state-of-the-art model performance for pan-cancer related gene prediction. AVAILABILITY AND IMPLEMENTATION: The DGHNN is available at https://github.com/skytea/DGHNN.

Previous research has identified hematoma as the primary cause of mortality and disability in intraventricular hemorrhage (IVH), yet neurologic outcomes post-IVH have not significantly improved following hematoma evacuation. Therefore, utilizing spatial transcriptomics sequencing technology to acquire and analyze IVH data is of significant importance for the early diagnosis and treatment of acute cerebral hemorrhage. However, existing spatial transcriptomics clustering algorithms often overlook local information, fail to integrate multimodal data, and suffer from performance instability. To address these issues, we developed a multimodal spatial transcriptomics clustering algorithm named STARGATE, which initially captures the global spatial topology and local long-sequence gene set features of cells by integrating Graph Convolutional Networks (GCNs) based on self-attention mechanisms and Long Short-Term Memory networks (LSTMs), subsequently fuses gene expression, tissue imagery, morphological, and spatial coordinate features using Gated Recurrent Units (GRUs), and finally enhances the accuracy and robustness of predictions through ensemble learning methods. Our key experimental results include: (1) In clustering experiments on our proprietary mouse IVH spatial transcriptomics dataset, the STARGATE model achieved Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), and Fowlkes-Mallows Index (FMI) scores of 0.80, 0.84, and 0.82, respectively, all superior to classic algorithms such as stLearn and CCST. (2) In clustering experiments on the mouse IVH dataset, the STARGATE model reached an accuracy of 89.63%, with an excellent confusion matrix, demonstrating outstanding accuracy and stability. (3) In clustering experiments on the public dataset DLFPC, which is known for its high data complexity leading to generally lower clustering metrics, the STARGATE model's ARI, NMI, and FMI indicators still outperformed classic algorithms such as SpaGCN and Seurat.

In recent years, the positive impact of virtual reality restorative environments on mental health has been confirmed by numerous studies. This study employed OpenAI's Sora model to create three types of virtual videos with restorative effects: natural, animal, and human environments. Using a facial emotion recognition system built with Keras, OpenCV, and PyQt5, along with the fer2013 facial expression database, and the psychological indicator detection technology of the PAD emotion scale, we conducted an experiment with 24 college students to assess their emotional responses and intensity to these three types of virtual videos in immersive virtual reality scenarios. We also collected subjective and objective physiological data from participants after they experienced different restorative virtual videos and analyzed the data using statistical methods.The results indicate that the three types of virtual videos generated by the Sora model had a positive impact on the participants' emotions. Most participants experienced a significant increase in positive emotions such as gentleness and surprise after watching the virtual natural environment videos, while negative emotions like anxiety and unease were effectively alleviated. Further analysis showed that the animal and natural environments were more effective in emotional regulation than the human environment.This study innovatively investigates the application of AI technology, particularly the Sora model, in virtual reality restorative environments, paving new directions for the exploration and application of AI technology in the emotional regulation of college students and providing new insights for future research.

The continuous mutation and evolution of H5N1 may cause a wide spread of the epidemic, however, the mechanism of H5N1 mutation and evolution are still unclear. Therefore, further mining the overall evolutionary pattern of H5N1 sequences, studying the impact of global factors such as migratory bird flyways on H5N1 evolution, and constructing a corresponding web service are crucial for preventing potential future H5N1 epidemics. To this end, we firstly analyzed the within-host mutation features of H5N1 sequences and optimized the phylogenetic model. Secondly, we employed the optimized model to construct the phylogenetic trees and performed H5N1 evolution group analysis. Thirdly, we not only analyzed the correlation between different evolution groups of H5N1 and global migratory bird flyways, but also investigated the impact of the El Niño climate phenomenon on the evolution of H5N1. Finally, we built up a web service for H5N1 mutation and evolution analysis and visualization. Our main results include: (1) We found that the global H5N1 can be categorized into three major evolution groups at different time in various geographic regions. (2) We not only found that all three major groups of HA fragments were related to the East Asian -Australasian flyway of migratory birds, but also demonstrated the correlation and causality between the Oceanic Niño Index and the alternation of old and new evolutionary groups of H5N1. (3) VP-H5N1 provides a fast and easy-to-use web service platform for online analysis and visualization of H5N1 evolution.

By reviewing previous CpG-related studies, we consider that the transcription regulation of about half of the human genes, mostly housekeeping (HK) genes, involves CpG islands (CGIs), their methylation states, CpG spacing and other chromosomal parameters. However, the precise CGI definition and positioning of CGIs within gene structures, as well as specific CGI-associated regulatory mechanisms, all remain to be explained at individual gene and gene-family levels, together with consideration of species and lineage specificity. Although previous studies have already classified CGIs into high-CpG (HCGI), intermediate-CpG (ICGI) and low-CpG (LCGI) densities based on CpG density variation, the correlation between CGI density and gene expression regulation, such as co-regulation of CGIs and TATA box on HK genes, remains to be elucidated. First, this study introduces such a problem-solving protocol for human-genome annotation, which is based on a combination of GTEx, JBLA and Gene Ontology (GO) analysis. Next, we discuss why CGI-associated genes are most likely regulated by HCGI and tend to be HK genes; the HCGI/TATA± and LCGI/TATA± combinations show different GO enrichment, whereas the ICGI/TATA± combination is less characteristic based on GO enrichment analysis. Finally, we demonstrate that Hadoop MapReduce-based MR-JBLA algorithm is more efficient than the original JBLA in k-mer counting and CGI-associated gene analysis.

Since the COVID-19 epidemic is still expanding around the world and poses a serious threat to human life and health, it is necessary for us to carry out epidemic transmission prediction, whole genome sequence analysis, and public psychological stress assessment for 2019-nCoV. However, transmission prediction models are insufficiently accurate and genome sequence characteristics are not clear, and it is difficult to dynamically assess the public psychological stress state under the 2019-nCoV epidemic. Therefore, this study develops a 2019nCoVAS web service (http://www.combio-lezhang.online/2019ncov/home.html) that not only offers online epidemic transmission prediction and lineage-associated underrepresented permutation (LAUP) analysis services to investigate the spreading trends and genome sequence characteristics, but also provides psychological stress assessments based on such an emotional dictionary that we built for 2019-nCoV. Finally, we discuss the shortcomings and further study of the 2019nCoVAS web service.

Since the expression and localization of protein plays the important role for GC, this study is to explore the connections between these proteomic information and GC prognosis. After we use bioinformatics method to analyze the dataset from Human Protein Atlas, we found that the proportions of high expression intensity in GC are less than those in normal stomach tissue (NST) samples, whereas the proportion of low expression intensity in GC are greater than those in NST; the proteins located in the nucleus of normal cells are more stable than others; MEN1 is such a tumor suppressor gene that has favorable prognosis.

Because of the different genetic structures and functional gene diversity, bacterial genome data have high complexity and dimensions. Therefore, it is difficult to reveal the sequence patterns and biological mechanism of a genome with classical analysis methods. Since artificial intelligence (AI) applications are capable of mining key biological information from massive multidimensional data, they are broadly employed to analyze bacterial genomes. However, to our knowledge, there are few systematic reviews that illustrate these AI applications in bacterial genomics research. Therefore, we first introduce the characteristics of bacterial genomics, and then briefly summarize AI applications in bacterial genomics research from three aspects: gene finding, gene function prediction and gene expression network construction. Finally, we discuss the challenges and future AI applications in bacterial genomics research.

BACKGROUND: Glioblastoma multiforme (GBM) is one of the most common malignant brain tumors and its average survival time is less than 1 year after diagnosis. RESULTS: Firstly, this study aims to develop the novel survival analysis algorithms to explore the key genes and proteins related to GBM. Then, we explore the significant correlation between AEBP1 upregulation and increased EGFR expression in primary glioma, and employ a glioma cell line LN229 to identify relevant proteins and molecular pathways through protein network analysis. Finally, we identify that AEBP1 exerts its tumor-promoting effects by mainly activating mTOR pathway in Glioma. CONCLUSIONS: We summarize the whole process of the experiment and discuss how to expand our experiment in the future.

It is well known that CpG island plays an important role in gene methylation. Since CpG island is closely related to human genetic characteristics such as TATA-box, tissue expression specificity, and LAUPs (Lineage-associated Underrepresented Permutations), it is important to investigate the sequence specificity of CpG island as well as the potential genetic characteristics related to CpG island to further understand the methylation related regulation mechanism. Therefore, this study develops such an online service website for CpG island related density and LAUPs analysis (CGIDLA, www.combio-lezhang.online/cgidla/index.html), that not only can investigate the relationship among the CpG island density, TATA-box feature, and expression breadth of human genes, but also deposit LAUPs of 32 representative species to help molecular biologists investigate the relationship between CpG island and LUAPs. Moreover, CGIDLA provides the source code download service and the related LAUPs counting functions.

Motivation: This study addresses several important questions related to naturally underrepresented sequences: (i) are there permutations of real genomic DNA sequences in a defined length (k-mer) and a given lineage that do not actually exist or underrepresented? (ii) If there are such sequences, what are their characteristics in terms of k-mer length and base composition? (iii) Are they related to CpG or TpA underrepresentation known for human sequences? We propose that the answers to these questions are of great significance for the study of sequence-associated regulatory mechanisms, such cytosine methylation and chromosomal structures in physiological or pathological conditions such as cancer. Results: We empirically defined sequences that were not included in any well-known public databases as lineage-associated underrepresented permutations (LAUPs). Then, we developed a Jellyfish-based LAUPs analysis application (JBLA) to investigate LAUPs for 24 representative species. The present discoveries include: (i) lengths for the shortest LAUPs, ranging from 10 to 14, which collectively constitute a low proportion of the genome. (ii) Common LAUPs showing higher CG content over the analysed mammalian genome and possessing distinct CG*CG motifs. (iii) Neither CpG-containing LAUPs nor CpG island sequences are randomly structured and distributed over the genomes; some LAUPs and most CpG-containing sequences exhibit an opposite trend within the same k and n variants. In addition, we demonstrate that the JBLA algorithm is more efficient than the original Jellyfish for computing LAUPs. Availability and implementation: We developed a Jellyfish-based LAUP analysis (JBLA) application by integrating Jellyfish (Marçais and Kingsford, 2011), MEME (Bailey, et al., 2009) and the NCBI genome database (Pruitt, et al., 2007) applications, which are listed as Supplementary Material. Supplementary information: Supplementary data are available at Bioinformatics online.