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Radiotherapy for lung cancer can lead to significant fatigue and a decrease in physical activity. An app that reminds the patients several times per day to perform a certain number of steps may be helpful in this respect. Such an app has just been developed. We aimed to investigated the functionality and practicability of the app in healthy volunteers before testing it in a prospective clinical trial. Thirty healthy volunteers participated in this prospective study (APPAREL-HV) and evaluated the app by affirming (=satisfaction) or negating ten statements in three sections related to functionality and practicability. If the satisfaction rate was <80%, the app required further optimization. If it was <60%, it appeared not useful. Subgroup analyses were performed for iPhone vs. Android users, German vs. Danish participants, and younger (<50 years) vs. older (&#x2265;50 years) patients. Overall satisfaction rates were 86.7% (two statements), 93.3-100% (two statements), and 90.0-100% (six statements) in the sections Download and installation, Navigation, and Content/functions, respectively. Android users were significantly less satisfied with Download and installation (50.0% vs. 95.8%, p=0.018). Otherwise, satisfaction rates were always &#x2265;80%, and subgroup analyses showed no significant differences between the groups compared. According to the results of the APPAREL-HV, the current version of the app appeared not sufficiently useful for Android users and required significant improvement for this subgroup in the section Download and installation.

Pelvic floor dysfunction (PFD) is a highly prevalent health problem, encompassing urinary incontinence, emptying disorders of the bladder, fecal incontinence, emptying disorders of the bowel, pelvic organ prolapse, sexual dysfunction, and chronic pelvic pain. Mobile health (mHealth) interventions delivered through apps can provide remote health services to improve patient compliance and enhance treatment effectiveness. Although apps for preventing and managing PFD have been developed and used, the features and quality of these apps in China have not been systematically examined. This study aimed to systematically summarize the functions and evaluate the quality of the existing mHealth apps for preventing and managing all kinds of PFD, such as urinary incontinence, fecal incontinence, and chronic pelvic pain. We systematically searched for potential PFD apps on the Apple App Store, Huawei AppGallery, and VIVO App Store. Apps were included if they were free, designed for preventing or managing PFD, in the Chinese language, could be downloaded and run on Android, Harmony, or iOS operating systems (OS), and incorporated elements of preventing and managing PFD. We excluded apps that were intended for use by health care providers and not relevant to PFD. Apps that met the inclusion criteria were downloaded and included for final analysis. The user version of the Mobile App Rating Scale (uMARS) was used to assess the apps' quality and summarize the apps' functionality according to guidelines. Of the 3897 apps screened, 46 apps that met the inclusion criteria were included in the final analysis. All apps were developed by corporations. More than half of the apps had download counts exceeding 10,000, and 24 (52.2%) apps scored 4 or higher in app stores. Furthermore, nearly half of the apps (n=21, 45.7%) had been updated within the past month at the time of retrieval. The overall uMARS scores ranged from 2.29 to 4.50, with a mean uMARS score of 3.46 (SD 0.50), which is considered acceptable quality. Based on uMARS scores, 15.2% (n=7) were rated as poor quality, 65.2% (n=30) as acceptable, and 19.6% (n=9) as good quality. More than half of the apps provided the functions of exercise (n=44, 95.7%), personal information recording (n=31, 67.4%), and health education (n=28, 60.9%). Only 5 apps provided 5 or more functions. The apps for PFD revealed acceptable quality, and the majority provided exercise, personal information recording, and health education functions. However, many apps lacked comprehensive functionalities and did not provide immediate feedback or high-quality educational information. Health care providers should follow international guidelines to create high-quality, evidence-based, multifunctional apps for PFD. Future studies should explore the effects of the apps and real-world user feedback data in clinical settings.

The Single-Cell Pediatric Cancer Atlas (ScPCA) Portal is a resource for uniformly processed single-cell and single-nucleus RNA sequencing (RNA-seq) data and de-identified metadata from pediatric tumor samples. Originally comprising data from 10 projects funded by Alex's Lemonade Stand Foundation (ALSF), the Portal currently contains summarized gene expression data for over 700 samples across 55 cancer types from ALSF-funded and community-contributed datasets. Downloads include expression data as SingleCellExperiment or AnnData objects containing raw and normalized counts, principal-component analysis (PCA) and uniform manifold approximation and projection (UMAP) coordinates, automated cell-type annotations, and copy-number variation estimates, along with summary reports. Some samples have additional data from bulk RNA-seq, spatial transcriptomics, and/or feature barcoding (e.g., CITE-seq) included in the download. All data on the Portal were uniformly processed using scpca-nf, an efficient and open-source Nextflow workflow that uses alevin-fry to quantify gene expression. Comprehensive documentation, including file descriptions and a getting-started guide, are available online.

Although large language models (LLMs) have undergone substantial development, their applicability to epidemiological research has not been sufficiently examined. This study aims to develop and evaluate an LLM-based framework for hypothesis generation and testing, demonstrating its application in childhood asthma in the National Health and Nutrition Examination Survey (NHANES). Pilot study was conducted to explore factors associated with childhood asthma in the 2001-2020 NHANES cycles. A modular agent system was developed, including Database Query, Statistic, Paper Search, and Paper Download tools, along with two LLM models (Key Generator and Hypothesis Tester). Multivariable logistic regression was used to test for the association between each variable and current asthma, generating a tentative affirmative claim. The Key Generator module produced keywords for literature search, the Paper Search and Paper Download tools queried PubMed and retrieved relevant studies, and the Hypothesis Tester module synthesized evidence and determined the support for claims for each variable. Keywords and conclusions were reviewed by researchers and validated using multiple LLMs (ChatGPT, DeepSeek, and Gemini) to ensure consistency and robustness. 25,839 children with (n = 2928) and without (n = 22,911) current asthma, and 10,359 variables were included in the multivariable analysis, which yielded 100 variables associated with asthma. Of these, 21 were directly related to asthma (supporting published studies), 43 were indirectly related to asthma (based on background knowledge, though not explicitly discussed in the available publications), and 34 were unrelated to asthma. Two variables were excluded due to a lack of discriminative keywords. This study demonstrates the effectiveness of LLM-based models for generating and testing hypotheses about childhood asthma.

Deciphering phenotype-specific regulatory mechanisms is key to understanding the molecular basis of complex diseases and traits. However, constructing multi-omic regulatory networks (MO-RNs) is challenging, as it requires integrating heterogeneous omics data, incorporating biological context, and detecting regulatory mechanisms that vary across conditions. The R package MORE (Multi-Omics REgulation) addresses these challenges by applying robust statistical models to infer phenotype-specific regulatory networks from multi-omics data. However, the use of MORE typically requires programming expertise, limiting its accessibility to non-specialist users. To democratize access to advanced multi-omics modeling tools, we present MOREshiny, an interactive web application built on Shiny that extends the module of pathway enrichment analysis and automatically guides the choice of statistical methods. MOREshiny enables users to upload multi-omic data, configure their models, and interpret results through a user-friendly interface-without the need for coding skills. MOREshiny also allows users to download MORE results for their later exploration and study. To demonstrate the utility of MOREshiny, we showcase its functionalities on a multi-omic ovarian cancer dataset to understand regulatory differences between patients who did or did not require chemotherapy. MOREshiny is freely available for download as a dockerized R Shiny package at https://github.com/BiostatOmics/MOREshiny.

Cerebral infarction is a neurological disease with complex pathological mechanisms. This study aimed to explore the association between ferroptosis-related genes (FRGs), ubiquitin-proteasome system-related genes (UPSRGs) and cerebral infarction, and to find possible biomarkers for diagnosis. Gene expression data of cerebral infarction were downloaded. Module genes linked to both FRGs and UPSRGs were screened using Weighted Gene Co-expression Network Analysis (WGCNA). Candidate genes were obtained by intersecting Differentially expressed genes (DEGs) and module genes identified by WGCNA. Machine learning algorithms were employed to identify intersecting genes. Gene expression level analysis and receiver operating characteristic curve (ROC) analysis were performed to identify biomarkers, which were further validated via in vitro experiments. Gene Set Enrichment Analysis (GSEA) and immune cell infiltration assessment were also performed. WGCNA identified 1,304 module genes identified, and a total of 110 candidate genes were identified through the intersection of the 512 DEGs with the WGCNA - identified module genes. A total of seven intersecting genes were identified through the application of machine learning techniques, and CD19 and CCR7 were confirmed as biomarkers via expression and ROC analyses. GSEA indicated that the biomarkers were involved in mitochondrial function and ubiquitin-proteasome system pathways. Analysis of immune cell infiltration revealed that the identified biomarkers exhibited associations with various immune cell types. CD19 and CCR7 were identified as candidate diagnostic biomarkers for cerebral infarction, providing exploratory evidence for immune-related changes that require further mechanistic validation.

This dataset presents 352 nuclear genes assembled from whole genome skimming data of 43 Rhododendron samples. The data were generated from 14 Rhododendron dauricum collected from seven distinct geographical populations in Northeast China, together with sequence data from 29 additional Rhododendron samples downloaded from the NCBI database. Using the universal set of 353 angiosperm nuclear genes as a reference, all genes were assembled with the HybPiper v2.1.1 pipeline. The dataset contains raw assembly sequences in FASTA format for each gene. Sequence alignment, trimming, and phylogenetic analysis were performed to construct phylogenetic trees. The resulting phylogenies based on concatenated 352-gene dataset and the screened 17-gene sub-dataset clearly distinguished R. dauricum from other Rhododendron species. Moreover, both datasets resolved individuals from the same population into distinct clades, enabling geographical origin traceability for the protected species R. dauricum. This dataset provides high-resolution molecular markers for research on Rhododendron phylogenomics, population genetics, conservation, and molecular identification.

Uveal melanoma (UVM) is a highly malignant ocular tumor with a poor prognosis. Macrophages and monocytes in the tumor microenvironment promote immune escape, angiogenesis, and metastasis. Thus, exploring their roles may provide insights into UVM progression. The Cancer Genome Atlas (TCGA) was accessed to obtain the data of mRNA expression and follow-up data of UVM, and UVM single-cell profiles were downloaded to cluster cells by annotation of single-cell marker genes. The differentially expressed genes (DEGs) in macrophage/monocyte cells compared to other cell types were revealed. ssGSEA was applied to compute the score of DEGs and to reveal the genes for WGCNA in UVM. A prognostic risk model for UVM was constructed by uni/multivariate Cox and LASSO regression analyses to reveal the differential overall survival status. Further cellular validations were conducted to examine the effects of core genes in UVM. TIMER tool was applied for the analysis of immune cell infiltration levels in UVM. Chemotherapeutic drug sensitivity in UVM was assessed with the pRRophetic package. Six cell subpopulations were identified in the UVM samples, among which macrophage/monocyte cells were more predominant. Kaplan-Meier curves showed that UVM patients in the group of high RiskScore (consisting of the genes BTBD6, C2CD4B, CCL24, and S100A4) presented a poorer prognosis, higher infiltration of monocytic lineage, T cells, CD8 T cells, cytotoxic lymphocytes, and higher expression of immune checkpoint-related genes. A significant negative correlation between RiskScore and the IC50 of XMD8-85, lapatinib, roscovitine, salubrinal, bexarotene, LFM-A13, FTI-277, and TGX221 chemotherapeutic agents was further noticed. In this study, we computationally identified genes associated with both disease progression and macrophage/monocyte-related characteristics in UVM and constructed a prognostic risk model with predicted immune infiltration patterns. These findings generate testable hypotheses that may inform future experimental studies on the immune mechanisms underlying UVM.

This work studies trustworthy use of large language models for remote sensing satellite downlink scheduling. Rather than accepting a generated optimization model at face value, we organize the workflow into three guarded steps: candidate generation, benchmark-based validation, and fallback exact solving. The core technical component is a global time-slicing validator that converts visibility windows into atomic intervals; so, mutual exclusion at the ground-station side, mutual exclusion at the satellite side, and per-satellite download caps can be checked in a physically faithful manner. Results on a prototype instance indicate that LLM-based modeling can be integrated into a dependable scheduling pipeline when external verification and recovery are built into the loop.

The widespread availability of the internet in recent years has made it easier to use electronic databases, and bibliometric analysis is now being carried out in a variety of fields. This method aimed to investigate the characteristics and trends of dental anesthesiology research published in Anesthesia Progress, the oldest and most authoritative journal in the field, from its inception in 1966 to 2023. We identified the 50 most-cited articles to evaluate the impact and evolution of research in this field. The search was conducted by entering "Anesthesia Progress" in the "Source Title" field, which is one of the search items in the Scopus database. The results were sorted by number of citations, and the bibliographic information for the top 50 most-cited articles was downloaded. The analysis revealed that most of these influential papers originated in the United States (60%), highlighting its dominance in dental anesthesiology research. Key topics identified included "sedation" and "local anesthesia," reflecting an increasing emphasis on pain management and psychological support in dental care. The findings also indicated a modest decline in the proportion of scientific reports, suggesting potential stagnation in new research contributions over time. Furthermore, index keyword analysis illustrated a growing specialization in addressing anxiety and pain associated with dental procedures. This study underscores the need for enhanced research and innovation in dental anesthesiology to meet the evolving needs of clinical practice and improve patient care.

Cholangiocarcinoma (CCA) is a highly heterogeneous biliary malignancy with a poor prognosis. Finding early diagnosis and therapeutic targets for CCA is of great importance. The aim of this study was to screen for key genes involved in CCA using bioinformatics analysis, identify establishment of sister chromatid cohesion N-acetyltransferase 2 (ESCO2) as a core candidate, and validate its role experimentally. The CCA data were downloaded using the Gene Expression Comprehensive Database and the Cancer Genome Atlas, the core gene ESCO2 with strong correlation with CCA was screened by raw letter analysis, and the prognostic value of key CCA genes was analyzed by Kaplan-Meier. The effects of ESCO2 on CCA cells and its oncogenic effects were investigated by cell and animal experiments. A total of 1,372 differential genes were screened in this study, with 742 up-regulated genes including ESCO2 and 630 down-regulated genes. Patients with high ESCO2 expression had a poorer prognosis and were significantly associated with N stage. Immune infiltration analysis revealed that ESCO2 expression was negatively correlated with CD8A and FGFBP2. Cellular experiments showed that ESCO2 was significantly up-regulated in CCA cells. ESCO2 overexpression promotes CCA cell proliferation and inhibits apoptosis. In vivo experiments confirmed that ESCO2 promotes tumor growth and shortens survival in mice. ESCO2 is a key regulatory gene affecting the development of CCA and plays an important role in CCA cell proliferation, which could be a new target for CCA diagnosis and treatment.

Hybrid capture sequencing (Hyb-Seq) is a widely used approach in phylogenomics, providing efficient access to targeted genomic regions. However, deriving high-quality phylogenetic trees from raw sequencing reads requires extensive bioinformatics processing, which increases complexity, the risk of errors, and challenges in file management, especially for users unfamiliar with bioinformatics workflows. We developed HybSuite, a streamlined Bash-based bioinformatics pipeline built upon mainstream tools such as HybPiper 2, designed to simplify the Hyb-Seq phylogenomic analysis from raw reads to species trees. Compared to existing tools (e.g., HybPiper 2, CAPTUS), it offers a modular yet integrated workflow covering all key steps from downloading from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA), adapter removal, data assembly, and paralog handling to species tree inference and extensive in-depth analysis. We validated HybSuite by reconstructing a robust phylogeny for the Elaeagnaceae family, using the Angiosperms353 probe set and a dataset of 100 single-copy nuclear loci from Arabidopsis. HybSuite provides a flexible and user-friendly pipeline for Hyb-Seq phylogenomic analyses, and its high accuracy and efficiency were demonstrated through benchmarking with two empirical datasets. HybSuite is freely available at https://github.com/Yuxuanliu-HZAU/HybSuite. The pipeline is compatible with both the Linux and MacOS platforms.

Epigenome-wide association studies (EWAS) have identified numerous DNA methylation (DNAm) CpG sites associated with complex traits and diseases, but interpretation of those CpG sites remains challenging because in EWAS, CpGs are mostly linked to nearby genes based only on genomic proximity. Expression quantitative trait methylation (eQTM) analyses connect DNAm CpGs with statistically associated gene expression levels. However, a comprehensive, searchable resource integrating eQTMs across diverse tissues and disease contexts has been lacking. We developed the eQTM Atlas, a web-based resource that manually curates more than 11 million DNAm-gene expression associations from eight cohorts, covering 11 tissue types, four broad disease contexts, 173,886 unique CpG probes and 20,231 unique genes. The Atlas supports gene- or CpG-searches by tissue or disease type and finding associated CpG or genes, visualization of cis- and trans-eQTMs through genome browser, heatmap interfaces across various tissues, and cohort-level data downloads. By integrating eQTM results with EWAS resources, the eQTM Atlas enables users to connect disease- or trait-associated CpGs to statistically associated genes rather than relying solely on proximity-based gene annotation, supporting functional interpretation of EWAS findings and generation of disease-specific regulatory hypotheses. The eQTM Atlas is freely available at https://shiny.crc.pitt.edu/eqtm_browser/ . The web interface is implemented in R Shiny and hosted through the University of Pittsburgh Center for Research Computing (CRC). Source code is available at https://github.com/ads303/eQTM-Atlas .

PRRSV-2 prevalent strains mainly include C-PRRSV, HP-PRRSV, and NADC30-like, with the latter being the current dominant lineage. Due to the high recombination and genetic variation of PRRSV, existing RT-qPCR assays face an increasing risk of false negatives. Therefore, based on the current prevalent strain sequences, it is of great significance to update and establish a one-step multiplex RT-qPCR method that can simultaneously detect C-PRRSV, HP-PRRSV and NADC30-like. By downloading the latest prevalent strain full genome sequences from NCBI and isolating them in our laboratory, the conserved and type-specific target regions of the three strains were screened in the high-variable region of the nsp2 gene. TaqMan MGB probes and primers were designed. After optimizing the reaction conditions, the standard curve, amplification efficiency, sensitivity, specificity, repeatability and clinical application effect of this method were evaluated. The established standard curve showed a good linear relationship within the range of 1 &#xd7; 108 to 1 &#xd7; 103 copies/&#x3bc;L, with a correlation coefficient R2 of 0.998 for all. The amplification efficiency ranged from 97.58 to 103.53%. The minimum detection limits for C-PRRSV, HP-PRRSV and NADC30-like were 10.128 copies, 8.998 copies and 8.458 copies, respectively. This method showed no cross-reaction with common porcine pathogens such as PRRSV-1, PCV2 and CSFV, and the intra-batch and inter-batch coefficient of variation was less than 2%. The positive rate of PRRSV in 588 samples was 15.5% (91/588), which was higher than 13.94% (82/588) of the reported methods. The consistency Kappa of the two methods was 0.87. This study successfully established a one-step multiplex RT-qPCR method based on current prevalent strain sequences, which offers high sensitivity, strong specificity, and good repeatability, and can be used for rapid differential diagnosis of the three PRRSV subtypes in clinical samples, thereby supporting precise diagnosis, epidemiological monitoring, and prevention and control of PRRSV in China.

The products of isocyanide synthase (ICS) biosynthetic gene clusters (BGCs) have been implicated in microbial interactions, pathogenesis, and metal homeostasis. While several ICS BGCs have been described as mediating metal-associated ecologies, the evolutionary history of these clusters is unexplored among Lecanoromycetes, a clade comprised predominantly of symbiotic, lichen-forming fungi (LFF), which are known to thrive in both metal-contaminated and scarce environments. Analyzing nearly 4,000 fungal genomes, including 90 Lecanoromycetes, we identified a significant 3-fold enrichment of ICS-encoding genes in lichenized fungi compared with non-lichenized counterparts. This expansion includes six distinct clades enriched in LFF. Evolutionary reconstruction uncovered a widespread "split" variant of the copper-responsive (crmA) pathway, where the ICS- and NRPS-like components are encoded on separate genes, contrasting to the canonical "fused" crmA megasynthase. Metabolic characterization and genetic deletions in Fusarium graminearum confirmed that this split architecture is functionally equivalent to the fused form. Our chemical analysis suggests the first evidence of a potential leucine-derived isocyanide metabolite. Phylogenetic reconstruction indicates that the fused crmA arose from a split ancestor whose NRPS-like subdomain evolved from a canonical thioester reductase architecture likely via domain replacement. Redefinition of the crmA pathway to include the split ICS/NRPS-like variant reveals that crmA is one of the most prevalent ICSs in the fungi. We developed a website (https://isocyanides.fungi.wisc.edu/) that facilitates the exploration and downloading of all major results in our study. Our work demonstrates how exploring understudied fungal lineages can define new specialized metabolism lineages and reshape our understanding of the evolution of broadly conserved biosynthetic pathways.

Electroencephalography (EEG) is widely used clinically and in research, including AI-driven applications for cognitive state analysis and neurological disorder detection, such as epilepsy. However, automated seizure detection faces challenges, including inconsistent windowing, timestamp misalignment, label-based signal segmentation, and unstructured large-scale EEG data-especially critical in event-driven settings. To address these, we introduce Meta-EEGs, a structured, domain-agnostic EEG representation for temporally labelled tasks. Meta-EEGs provide consistent windowing with precise time alignment, enable event-based segmentation based on annotations or class labels, and organise raw EEG recordings into a simpler, relatively reduced in volume format suitable for AI model input. They also support the creation and management of hierarchical EEG datasets, currently lacking in the field. As case studies, we applied Meta-EEGs to the CHB-MIT and Siena Scalp EEG Databases, generating structured datasets that are publicly available on Figshare and have been downloaded over 2000 times since 2022. The working code is accessible on GitHub. Main features and applications:&#x2022;Provides consistent window definition, timestamp alignment, signal segmentation, and standardised structuring for large-scale EEG studies.&#x2022;Releases two fully annotated, reduced in volume datasets for automated seizure detection, supporting reproducible and generalisable analysis.&#x2022;Enables AI model development for seizure detection, event classification, patient-specific and cross-patient analysis, and hierarchical EEG tasks without repeated initial preprocessing.

High-throughput genomic analyses of germline and cancer genomes facilitate the identification of causal and actionable genetic variants. The recent advances in next-generation sequencing technology generated large-scale genomic and/or multi-omics dataset. Due to huge volume of data, scientists are facing challenges in visualizing, and interpreting the data. Currently available tools to visualize genetic variants from VCF files are not very user-friendly as most of them require knowledge of command line tools or scripts to install and run those software. Therefore, graphical user interface based tools or software are needed to summarize and visualize the VCF data. We have developed a Shiny App, interactive tool using the R programming language that utilizes existing R packages like "vcfR" and "maftools" to visualize and generate quality control metrics for genetic data. Our tool is powered by Shiny, making it easier to summarize and visualize genomic data using a GUI. XVCF has been developed for the summarization and visualization of genomic variation data. The tool offers an easy and friendly interface, allowing users to perform data loading, summarization, and visualization interactively. XVCF extract useful information such as read depth, mapping quality, genotype, quality control summary, and allele frequency from unannotated data. In the second module of XVCF, the cancer genomic data is analyzed using "maftools" to produce oncoplot, lollipop plot, gene summary, etc. XVCF is available for free download from https://github.com/rashidma/XVCF. Being a shiny R package, XVCF can be installed across different operating systems and utilize different computer hardware configurations. Visualizing genomic data has always been challenging. Existing tools/software seem to be difficult to use due to lack of technical computer programming knowledge. We offer XVCF to visualize and/or summarize genomic data at a greater ease due to its graphical user interface and powerful cross-platform R shiny framework.

Understanding the regulatory consequences of genetic variation in the aging human brain requires molecular maps that span brain regions, cell types and regulatory modalities. We present the Alzheimer's Disease Sequencing Project Functional Genomics (FunGen-AD) xQTL Atlas, a harmonized resource of molecular quantitative trait loci from four postmortem brain studies, ROSMAP, MSBB, Knight-ADRC and MiGA. The atlas integrates histone acetylation, DNA methylation, gene expression, splicing and protein abundance QTLs across 14 brain regions, 7 major cell types and 17,566 samples, with standardized association, significance-filtered and fine-mapping outputs. To expand discovery beyond conventional 1-Mb cis windows, we include variants within Topologically Associating Domains (TAD) and their boundaries where appropriate, identifying on average 21% more variant-molecular-trait associations per dataset. Statistical fine-mapping reduced broad association sets by 95% into credible sets of candidate regulatory variants. Distributed through the NIAGADS xQTL portal and bulk-download services, the atlas provides a comprehensive functional-genomic foundation for interpreting genetic risk variants in Alzheimer's disease and aging-brain research.

Chemical toxicity assessment commonly includes in vivo rat exposure experiments, with transcriptomic measurements collected at various exposure times and chemical doses. The mechanisms underlying chemical-induced toxicity are then inferred by analyzing changes in gene expression. Recently, genome-scale metabolic models (GSMs), which represent the metabolic network of a cell/organism and contain metabolites, reactions, genes, and the relationship between the genes and reactions, have been used to provide a systems-level understanding of gene expression. However, most of the algorithms that integrate gene expression with GSMs require familiarity with MATLAB or Python programming, making them less accessible for users without computational experience. Here, we introduce ToxMet (https://toxmet.bhsai.org), an open-access, user-friendly web application that provides tabular and graph-based network views to visualize the latest rat GSM (iRno v4.2) and predicts chemical-induced metabolic perturbations in rat tissues by integrating toxicogenomic measurements with the rat GSM. ToxMet uses two well-validated computational algorithms, TIMBR and Pheflux, to predict metabolic perturbations and provides the prediction results as interactive and downloadable tables, scatter plots, and network visualizations. As such, the web tool can process a maximum of 10 conditions for a single job, and the results can be used for dose-response studies to monitor organ metabolism at the subsystem level. We evaluated ToxMet's ability to predict toxicity mechanisms by applying it to publicly available toxicogenomic data for two exemplar toxicants: gentamicin and thioacetamide, which are known to induce kidney and liver injury, respectively. ToxMet predicted known toxicity mechanisms for both chemicals, thus demonstrating its ability to provide novel insights into the metabolic mechanisms of chemical-induced toxicity and aid in the discovery of biomarkers and therapeutics using gene expression data.