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PubMed1992

Oligonucleotide and amplification fingerprinting of wild species and cultivars of banana (Musa spp.).

DNA oligonucleotide and amplification fingerprinting have been successfully used to detect genetic polymorphisms in 15 representative species and cultivars of the genus Musa, comprising AA, AAA, AAAA, AAB, ABB, and BB genotypes. In-gel-hybridization of Hinf I-digested genomic banana DNA to the 32P-labeled synthetic oligonucleotides (GATA)4, (GTG)5, and (CA)8 revealed considerable polymorphisms between Musa species and cultivars. The fingerprint patterns proved to be somatically stable and did not show differences between individual plants of 'Grand Nain' (AAA genotype). Dendrograms based on oligonucleotide fingerprint band sharing data proved to be consistent with most of the known features of the history of banana and plantain cultivation and evolution, respectively. DNA samples from the same banana species and cultivars were also amplified by PCR using single or pairwise combinations of short oligonucleotide primers. Amplification products were separated on agarose or polyacrylamide gels and visualized by ethidium bromide or silver staining, respectively. Polymorphic patterns were obtained with some but not all primers. By using the CCCTCTGCGG primer in simplex and/or duplex PCR, the induced mutant 'GN60A' was clearly recognized from its original variety 'Grand Nain'. Both fingerprint techniques allowed the detection of bands characteristic for the A and B genome. This DNA fingerprinting technology has potential application in several areas of Musa improvement.

Base Sequence↗

PubMedDate not supplied

A computer-based frozen blood inventory and information system.

A computer-based, time-sharing data processing system was developed to assist in maintaining information regarding units of red blood cells frozen for eventual transfusion. An automated system has been programmed to compile and retrieve data concerning stored units, prepare shipping documents as required, and maintain transfusion records in a retrievable manner for thawed or shipped units. Requests for frozen red blood cells are processed through this system. Units are selected by the computer to meet requirements specified by the operator of a keyboard terminal. These requirements include method of cryopreservation, specific antigenic characteristics required, and the number of units requested. The computer prints out on the keyboard terminal the units meeting the requirements and the keyboard operator then indicates to the computer the name of the requesting facility, if these units are to be shipped. This input initiates a programmed routine that generates a shipping invoice and a new file for these units in a permanent transfusion record which can by acessed by either donor number or the frozen blood cell code.

Blood Banks↗

PubMed2026

HXMS: a standardized file format for HX-MS data.

MOTIVATION: Hydrogen/deuterium exchange-mass spectrometry (HX-MS) is a rapidly expanding technique used to investigate protein conformational ensembles. The growing popularity and utility of HX-MS has driven the development of diverse instrumentation and software, resulting in inconsistent, non-standardized data analysis and representation. Most HX-MS data formats also employ only mean deuteration representations of the data rather than full isotopic mass spectra, which reduces the information content of the data and limits downstream quantitative analysis. RESULTS: Inspired by reliable protein structure and genomics data formats, we present HXMS, a unified, lightweight, scalable, and human-readable file format for HX-MS data. The HXMS format preserves the isotopic mass envelopes for all peptides, captures the full experimental time-course including fully deuterated control samples, and contains all other key information. It supports multimodal distributions, post-translational modifications (PTMs), and experimental replicates. To promote compatibility with existing HX-MS workflows, we also developed PFLink, a Python package that converts exported data files from commonly used HX-MS software to the HXMS format. PFLink and the HXMS format will enable quantitative, higher-resolution data processing, improved data sharing and storage among HX-MS practitioners, future machine learning applications, and further developments in HX-MS analysis. AVAILABILITY AND IMPLEMENTATION: PFLink is publicly available to install locally on HuggingFace, alongside documentation, or use online at HuggingFace (https://huggingface.co/spaces/glasgow-lab/PFlink). The supplementary information includes sample input files, sample HXMS files, and a generic unfilled PFlink custom CSV file that users may populate with key experimental conditions and results, which can then be read and converted into the HXMS format.

Software↗

PubMed1992

Research on first-episode psychosis: report on a National Institute of Mental Health Workshop.

The need to focus increased research on patients experiencing their first episode of psychosis was emphasized in A National Plan for Schizophrenia Research. To develop strategies for enhancing research in this area, a National Institute of Mental Health Workshop on First-Episode Psychosis was held in 1991. The topics discussed at that workshop are summarized, with key issues including the following: (1) the need for better operational definitions of onset, end of an episode, and relapse of psychosis; (2) careful consideration of inclusion and exclusion criteria related to age, gender, prior treatment, comorbid substance abuse, and similar issues; (3) the challenge of finding patients never exposed to neuroleptics and the value of entering first-episode patients into standardized treatment protocols; (4) the design of followup studies; (5) strategies to increase the pool of applicants; and (6) approaches for increasing power through data sharing and collaboration between groups.

Age Factors↗

PubMed2026

Schizophrenia Spectrum Biomarkers Consortium: Establishment of a Biorepository for the Discovery of Quantitative Fluid Biomarkers.

BACKGROUND AND HYPOTHESIS: Schizophrenia spectrum disorders (SSDs) produce severe symptoms, disability, and premature mortality, but only partially effective symptomatic treatments exist. Treatment development is impeded by lack of insight into disease mechanisms or objective biomarkers for clinical trials. Advances in genetics and neurobiology have converged on strong pathogenic hypotheses for SSDs centered on synapse dysfunction and excessive pruning, pathogenic processes that may produce measurable proteomic evidence in cerebrospinal fluid (CSF). Leveraging design precedents from successful fluid biomarkers discovery for Alzheimer's disease, we undertook a pilot study to test the feasibility of repeated CSF and blood samples collection from individuals with SSDs. Here we report on successful implementation of longitudinal bio-behavioral phenotyping in SSDs and establishment of a repository to permit broad sample and data sharing. STUDY DESIGN: The Schizophrenia Spectrum Biomarkers Consortium (SSBC) study principles included longitudinal study design, paired CSF and plasma collection associated with robust phenotypic characterization, at 3 academic sites and the establishment of a biorepository. Participants underwent clinical and cognitive assessments, neuroimaging, blood draws, and CSF collection via lumbar puncture (LP) every 6 months. STUDY RESULTS: SSBC successfully enrolled 48 SSD and 41 Healthy Controls with a 73% longitudinal retention. Clinical, cognitive, and neuroimaging results were consistent across sites and with existing studies. Study procedures were well tolerated, and almost all LPs (99%) resulted in either no or minor headache/backache that resolved without medical interventions. CONCLUSIONS: The pilot SSBC study demonstrates that a multi-site, longitudinal study with repeat CSF collection is feasible, with excellent participant acceptability and retention.

Humans↗

PubMed2026

Genomic Medicine Sweden: Advancing precision medicine at the national level.

High-throughput sequencing has transformed clinical diagnostics of rare diseases (RD), cancer and infectious diseases by enabling the identification of disease-causing genetic alterations and facilitating individualised treatment and care. In response to these advances, Genomic Medicine Sweden (GMS) was established in 2017 as a national collaborative effort to accelerate implementation of genomics-based precision medicine within Sweden's regionally organized, publicly funded healthcare system. GMS brings together the seven university healthcare regions and their associated medical faculties, in collaboration with healthcare regions across Sweden, Science for Life Laboratory, patient organizations, industry and governmental agencies. Activities are coordinated through national disease-specific expert groups, supported by cross-cutting functions in bioinformatics, health economics, ethics, education and patient engagement. At the operational level, seven Genomic Medicine Centres, embedded at university hospitals, develop and deliver harmonised genomic diagnostics nationwide. The National Genomics Platform provides secure infrastructure for large-scale data storage, analysis, and national and international data sharing. Following initial project-based funding, GMS now receives long-term governmental support. This review describes the national implementation of genomic-based precision diagnostics, discusses challenges and lessons learnt, and highlights key milestones across disease areas, including whole-genome sequencing in RD and paediatric cancer, comprehensive genomic profiling of haematological malignancies and solid tumours, pathogen genomics in microbiology, pharmacogenomic testing and emerging applications of polygenic risk scores in complex diseases. Collectively, these efforts have contributed to more than 500,000 genomic tests being performed within Swedish healthcare between 2017 and 2025. Finally, we outline future diagnostic needs and priority areas to ensure sustainable, scalable and equitable access to precision medicine.

Precision Medicine↗

PubMed2026

Safeguarding biomedical AI: a critical scoping review of privacy-enhancing technologies, hybrid approaches, and deployment models.

BACKGROUND: Biomedical artificial intelligence (AI) requires the integration of privacy-enhancing technologies (PETs) to safeguard sensitive clinical, imaging, and genomic data while preserving analytical utility. OBJECTIVES: This review critically and systematically maps applications of PETs across the biomedical AI lifecycle in accordance with PRISMA-ScR guidelines and evaluates their technical trade-offs, deployment feasibility, and residual risks. METHODS: We systematically searched PubMed, IEEE Xplore, ACM Digital Library, and Scopus for studies published between 2015 and 2025. Eligible studies addressed differential privacy, federated learning, secure multiparty computation, homomorphic encryption, or hybrid approaches in biomedical AI. Data were charted on PET type, modality, lifecycle stage, utility metrics, privacy parameters, and deployment considerations. A critical appraisal rubric assessed threat-model adequacy, methodological clarity, reproducibility, privacy-utility transparency, and deployment realism. Additionally, we hand-searched major venues (USENIX Security, NeurIPS, AAAI) and screened Google Scholar for grey literature, applying de-duplication across sources. RESULTS: We identified 87 studies spanning clinical decision support, genomics, and medical imaging. From 25,761 initial records, 3,754 underwent title/abstract screening and 1,968 underwent full-text assessment. PETs demonstrated distinct strengths and limitations: differential privacy provided provable guarantees but reduced performance on imbalanced data; federated learning improved data access but remained vulnerable to gradient leakage; and cryptographic methods ensured confidentiality at high computational cost. Synthetic data generation supported privacy-conscious data sharing and benchmarking but remained sensitive to disclosure risk, fidelity loss, and subgroup representation. Hybrid and emerging approaches, including trusted execution environments, zero-knowledge proofs, and privacy-preserving transformer architectures, mitigated composability gaps yet lacked full end-to-end assurance. Case studies at hospital and biobank scale illustrated practical feasibility and infrastructure demands. CONCLUSIONS: Situating PETs within technical and operational contexts clarifies their capabilities, limitations, and deployment challenges. Residual risks persist, including fairness concerns, inference-time leakage, and overreliance on PETs as compliance proxies. Sustained technical innovation and institutional governance remain essential for the trustworthy integration of PETs in biomedical AI.

biomedical AI↗

PubMed1992

Caring between nursing students and physically/mentally handicapped children: a phenomenological study.

Advanced technology has increased the life expectancy to age 20 for most chronically ill children. Consequently, nurses will be encountering an increasing number of physically and mentally handicapped children. Nursing students' discovery of the meaning of caring with exceptional children will help prepare them for interactions with these special children during their nursing careers. A phenomenological study was undertaken to explore 36 nursing students' caring experiences with physically and mentally handicapped children. Van Kaam's phenomenological methodology was used to analyze the data. Sharing the findings of this phenomenological study with nursing students is one approach faculty can use to help reduce their anxiety regarding upcoming clinical rotations with exceptional children.

Adult↗

PubMed2026

Development of a Blockchain-Based Platform to Enable Indigenous Data Sovereignty and Shared Research Participation With Indigenous Communities: Technology Prototyping and Community Engagement Study.

BACKGROUND: Historic and ongoing problematic practices regarding the collection, storage, and use of Indigenous health data have led to the need to ensure principles of Indigenous Data Sovereignty (IDS) are followed in research practices and technology development. OBJECTIVE: This project, a partnership between UC San Diego and the Native BioData Consortium (NativeBio), sought to explore the practical application of blockchain technology and its potential to facilitate Indigenous-led research collaboration. METHODS: This project first undertook purposeful relationship building with NativeBio to form a Community Advisory Board (CAB) for identifying community and technology needs for a blockchain research collaboration platform with an initial focus on genomic data. Over a 2-year project period, a series of public meetings and presentations at Indigenous-led conferences introduced the concept of exploring compatibility between blockchain and IDS principles, followed by iterative prototyping and co-design of a blockchain platform with NativeBio, using Ethereum as the underlying protocol. RESULTS: Direct engagement with NativeBio and the CAB informed the initial design and development of a "b-IDS" proof-of-concept (POC) blockchain platform. The POC consists of three main components: (1) the web front-end layer, (2) the Ethereum network that executes the smart contract and blockchain storage aspects of the framework, and (3) the back-end database that stores off-chain interactions and data for future use with external genomic data repositories. After refinement of the POC, a community-based participatory research (CBPR) use case aligned with IDS principles was identified as a practical workflow and incorporated into the design of the POC for implementation. CONCLUSIONS: The findings from this project demonstrated the potential use of operationalizing IDS through blockchain technology with proactive and sustained engagement with Indigenous partners. Blockchain technology may have certain advantages over other data governance approaches and systems, facilitating timely oversight, shared decision-making and consent structures, and direct involvement of Indigenous communities in technology design, respecting the core principles of IDS and CBPR. Future development of the blockchain-IDS POC will need to incorporate other research practices and ethics frameworks to expand its use to other public health and biomedical research use cases.

Blockchain↗

PubMed2016

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking.

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

Biological Products↗

PubMed2025

The GSA Family in 2025: A Broadened Sharing Platform for Multi-omics and Multimodal Data.

The Genome Sequence Archive family (GSA family) provides a comprehensive suite of database resources for archiving, retrieving, and sharing multi-omics data for the global academic and industrial communities. It currently comprises four distinct database members: the Genome Sequence Archive (GSA, https://ngdc.cncb.ac.cn/gsa), the Genome Sequence Archive for Human (GSA-Human, https://ngdc.cncb.ac.cn/gsa-human), the Open Archive for Miscellaneous Data (OMIX, https://ngdc.cncb.ac.cn/omix), and the Open Biomedical Imaging Archive (OBIA, https://ngdc.cncb.ac.cn/obia). Compared to its 2021 version, the GSA family has expanded significantly by introducing a new repository, the OBIA, and by comprehensively upgrading the existing databases. Notable enhancements to the existing members include broadening the range of accepted data types, strengthening quality control systems, improving the data retrieval system, and refining data-sharing management mechanisms.

Humans↗

PubMed2025

A Sociotechnical Approach to Genomic Data Privacy: A Comparative Analysis.

The sharing of genomic data across international borders presents significant privacy law challenges.Secured computed environments on smartphones allow the storing and processing of sensitive data without the underlying data being shared with processors.A novel technology, described here, to process genomic data within a secured computing environment seems to comport with EU and US privacy laws, despite their differing aims and rules.This technology suggests there may be technological solutions to privacy law fragmentation across jurisdictions, so long as data subjects socially trust the technology and have control over their data.

genome↗

PubMed2026

Single-cell transcriptomic landscape of the southern green stink bug (Nezara viridula) midgut.

BACKGROUND: The southern green stink bug (SGSB), Nezara viridula, is a globally distributed hemipteran pest that damages many economically important crops. Its midgut supports digestion, defense, symbiosis, and interactions with orally delivered control agents, yet the cellular composition of this tissue remains poorly characterized. We therefore developed a single-cell transcriptomic atlas of the N. viridula midgut. RESULTS: Single-cell RNA sequencing of two biological replicates yielded a quality-filtered data set of 13,763 cells. Unsupervised clustering identified 12 transcriptionally distinct populations with putative annotations, including a stem cell/enteroblast (SC/EB)-like population, seven enterocyte-related populations, goblet-like cells, enteroendocrine cells, visceral muscle cells, and an extracellular-matrix-associated epithelial population. Enterocyte-related populations accounted for more than 77% of recovered cells. Putative annotations were assigned primarily from marker gene enrichment and homology to markers reported in other insects. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses identified population-associated functional enrichment patterns, and pseudotime analysis suggested transcriptional relationships between the SC/EB-like population and several enterocyte- and secretory-associated populations without establishing developmental lineages. Immune- and defense-associated transcripts were preferentially enriched in the pEC2 population, and genes associated with symbiont recognition, insecticide action, xenobiotic transport, and orally delivered double-stranded RNA showed population-biased expression. Descriptive comparisons with published insect midgut data sets identified shared and data-set-specific patterns among annotated populations. CONCLUSION: This atlas provides the first single-cell transcriptomic resource for a stink bug midgut and establishes a descriptive cellular framework for SGSB midgut biology. The dataset prioritizes candidate genes and cell populations for future spatial validation, functional testing, and studies of hemipteran midgut physiology, symbiosis, immunity, and pest-management-relevant traits. © 2026 Society of Chemical Industry.

Nezara viridula↗

PubMed2026

Ethical Governance of Open Data Across Biomedical Research, Healthcare, and Public Health: Privacy, Equity, Trust, and Controlled Access.

Open data has become central to biomedical research and public health, but health information is uniquely sensitive and difficult to share responsibly. In this narrative review, open data is considered as a spectrum of health-data sharing arrangements, ranging from public aggregate datasets to controlled-access repositories, federated analysis, and synthetic data. This narrative review synthesizes the scientific and societal rationale for greater openness with the ethical, legal, and governance constraints that shape what "open" can realistically mean in healthcare. We examine how data sharing supports reproducibility, machine learning, and more efficient research, while also enabling public health surveillance and learning health systems. Against these benefits, we analyze privacy and re-identification risks, consent challenges in large-scale secondary use, inequities including data colonialism, and tensions introduced by commercialization. We integrate lessons from prominent case examples spanning pandemic data sharing, genomic initiatives, population registries, patient-led rare disease infrastructures, and regional data spaces. Across these domains, experience suggests that durable progress depends less on unrestricted openness than on calibrated access, privacy-preserving architectures, clear accountability, and sustained public engagement. We conclude by proposing a pragmatic ethical orientation for healthcare open data: treat openness as a spectrum of controlled sharing arrangements, embed equity and reciprocity into governance, and institutionalize trust-building measures that can persist beyond emergencies and political cycles.

Data colonialism↗

PubMed2026

DIVAS: an R package for identifying shared and individual variations of multiomics data.

MOTIVATION: Multiomics data integration aims to identify biological patterns shared across molecular modalities. Most existing methods detect either jointly shared variation, across all modalities, or individual variation, unique to a single modality, but overlook partially shared variation, shared by only a subset of modalities. This is a critical limitation, because many biological mechanisms manifest in some but not all molecular modalities. RESULTS: We present an open-source R package implementing data integration via analysis of subspaces (DIVAS), a framework for systematically identifying jointly shared, partially shared and individual variations across multiple data types. DIVAS combines angle-based subspace analysis with inference through rotational bootstrap, hierarchically searching all combinations of modalities to decompose multiomics data into interpretable components with scores and loadings. In simulations with a known sharing structure, DIVAS recovered every component across a wide range of noise levels, whereas existing methods did not. Applied to multi-modal COVID-19 data, it reveals partially shared immune and metabolic dysregulation patterns underpinning disease severity that conventional approaches would miss. AVAILABILITY AND IMPLEMENTATION: DIVAS is available at https://github.com/ByronSyun/DIVAS, with documentation and vignettes. The COVID-19 case study vignette is available at https://byronsyun.github.io/DIVAS_COVID19_CaseStudy/.

Multiomics↗

PubMed2025

Rehabilomics Strategies Enabled by Cloud-Based Rehabilitation: Scoping Review.

BACKGROUND: Rehabilomics, or the integration of rehabilitation with genomics, proteomics, metabolomics, and other "-omics" fields, aims to promote personalized approaches to rehabilitation care. Cloud-based rehabilitation offers streamlined patient data management and sharing and could potentially play a significant role in advancing rehabilomics research. This study explored the current status and potential benefits of implementing rehabilomics strategies through cloud-based rehabilitation. OBJECTIVE: This scoping review aimed to investigate the implementation of rehabilomics strategies through cloud-based rehabilitation and summarize the current state of knowledge within the research domain. This analysis aims to understand the impact of cloud platforms on the field of rehabilomics and provide insights into future research directions. METHODS: In this scoping review, we systematically searched major academic databases, including CINAHL, Embase, Google Scholar, PubMed, MEDLINE, ScienceDirect, Scopus, and Web of Science to identify relevant studies and apply predefined inclusion criteria to select appropriate studies. Subsequently, we analyzed 28 selected papers to identify trends and insights regarding cloud-based rehabilitation and rehabilomics within this study's landscape. RESULTS: This study reports the various applications and outcomes of implementing rehabilomics strategies through cloud-based rehabilitation. In particular, a comprehensive analysis was conducted on 28 studies, including 16 (57%) focused on personalized rehabilitation and 12 (43%) on data security and privacy. The distribution of articles among the 28 studies based on specific keywords included 3 (11%) on the cloud, 4 (14%) on platforms, 4 (14%) on hospitals and rehabilitation centers, 5 (18%) on telehealth, 5 (18%) on home and community, and 7 (25%) on disease and disability. Cloud platforms offer new possibilities for data sharing and collaboration in rehabilomics research, underpinning a patient-centered approach and enhancing the development of personalized therapeutic strategies. CONCLUSIONS: This scoping review highlights the potential significance of cloud-based rehabilomics strategies in the field of rehabilitation. The use of cloud platforms is expected to strengthen patient-centered data management and collaboration, contributing to the advancement of innovative strategies and therapeutic developments in rehabilomics.

Cloud Computing↗

PubMed2026

dGAMLSS: an exact, distributed algorithm to fit Generalized Additive Models for Location, Scale, and Shape for privacy-preserving population reference charts.

MOTIVATION: There is growing interest in estimating population reference ranges across age and sex to better identify atypical clinically-relevant measurements throughout the lifespan. For this task, the World Health Organization recommends using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), which can model non-linear growth trajectories under complex distributions that address the heterogeneity in human populations.Fitting GAMLSS models requires large, generalizable sample sizes, especially for accurate estimation of extreme quantiles, but obtaining such multi-site data can be challenging due to privacy concerns and practical considerations. In settings where patient data cannot be shared, privacy-preserving distributed algorithms for federated learning can be used, but no such algorithm exists for GAMLSS. RESULTS: We propose distributed GAMLSS (dGAMLSS), a distributed algorithm that can fit GAMLSS models across multiple sites without sharing patient-level data. This includes specific considerations for the fitting of smooth functions at varying levels of communication efficiency. We demonstrate the effectiveness of dGAMLSS in constructing population reference charts across clinical, genomics, and neuroimaging settings and show that dGAMLSS is able to reproduce pooled reference charts and inference down to numerical differences. AVAILABILITY AND IMPLEMENTATION: An R package providing examples of the dGAMLSS algorithm, as well as functions for sharing and aggregating site-specific parameters, is available at https://github.com/hufengling/dGAMLSS.

Algorithms↗

PubMed2026

ChemGenXplore: an interactive tool for exploring and analysing chemical genomic data.

MOTIVATION: Chemical genomics is a powerful high-throughput approach to systematically link phenotypes to genotypes. However, the vast datasets generated remain challenging to explore due to the lack of integrated, interactive tools for visualization and analysis. Existing workflows often require multiple independent software tools, limiting data accessibility and collaboration. Therefore, we created a user-friendly platform that enables efficient exploration and sharing of chemical genomics data. RESULTS: We developed ChemGenXplore, a web-based Shiny application designed to streamline the visualization and analysis of chemical genomic screens. It offers two primary functionalities: one for exploring pre-implemented datasets and another for analysing user-uploaded datasets. ChemGenXplore enables users to visualize phenotypic profiles, assess gene-gene and condition-condition correlations, perform GO and KEGG enrichment analysis, and generate customizable, interactive heatmaps. To further support collaborative research, ChemGenXplore also facilitates the comparative analysis of chemical genomic and other omics datasets. By consolidating these features into a single interactive and accessible tool, ChemGenXplore facilitates data sharing, enhances reproducibility, and promotes collaboration within the research community. AVAILABILITY AND IMPLEMENTATION: ChemGenXplore is freely accessible as a web application at https://chemgenxplore.kaust.edu.sa/. Source code and documentation, including instructions for local installation, are provided on GitHub (https://github.com/Hudaahmadd/ChemGenXplore). A Docker image is also available on DockerHub (https://hub.docker.com/r/hudaahmad/chemgenxplore) to ensure reproducibility and simplify installation.

Software↗