The Pathogen–Host Interactions Database (PHI-base) is an online database that catalogues experimentally-verified pathogenicity, virulence and effector genes from fungal, oomycete, and bacterial pathogens, which infect animal, plant, fungal, and insect hosts. PHI-base is a valuable resource in the discovery of genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention.

Information in PHI-base is manually curated by domain experts and is supported by strong experimental evidence (for example, gene disruption and gene complementation experiments), as well as references to the literature in which the original experiments are described. Annotations are made using terms from ontologies and controlled vocabularies, including the Gene Ontology (GO), Brenda Tissue Ontology (BTO), and the Pathogen--Host Interaction Phenotype Ontology (PHIPO).

PHI-base 5 includes data that was curated using a new curation process described in Cuzick et. al (2023). Data releases for PHI-base 5 do not use the same schema as data releases from PHI-base 4, but all data records from PHI-base 4 that can be made compatible with the new schema are included with this release. Data releases from PHI-base 4 and PHI-base 5 will occur in parallel until such time that all data from PHI-base 4 can be migrated to PHI-base 5. The PHI-base 4 data releases are available on Zenodo at https://zenodo.org/doi/10.5281/zenodo.5356870.

Data content

• phi-base_v5.0.xlsx: the PHI-base dataset as an Excel spreadsheet. This format follows the layout of the PHI-base 5 website, with sheets corresponding to the sections of gene pages on the website. This format is designed for use by non-technical users.

• phi-base_v5.0.json: the PHI-base dataset in JSON format. This format is closer to the data format that is exported by PHI-Canto, the curation tool used by PHI-base. This format is primarily intended for programmatic usage and has additional data (e.g. metadata for curation sessions) that is not included in the spreadsheet format.

• phi-base.schema.json: a JSON Schema file for the JSON format of the dataset. This is included as documentation for the fields in the JSON file, but can also be used to validate the dataset.

PHI-base is an online database (available at phi-base.org) that catalogues experimentally verified pathogenicity, virulence and effector genes from fungal, oomycete and bacterial pathogens, which infect animal, plant, fungal and insect hosts. PHI-base is a valuable resource in the discovery of genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention.

Each entry in PHI-base is curated by domain experts and is supported by strong experimental evidence (for example, gene disruption and gene complementation experiments), as well as literature references in which the original experiments are described. Each gene in PHI-base is presented with its nucleotide sequence and deduced amino acid sequence (available in a FASTA file), as well as a detailed description of the predicted protein's function during the host infection process. To facilitate data interoperability, we have annotated genes using ontologies, controlled vocabularies, and links to external sources (including UniProt, Gene Ontology, Enzyme Commission, NCBI Taxonomy, EMBL, PubMed and FRAC).

This PHI-base dataset is a Frictionless Data Package that contains an export of the PHI-base database in CSV format (comma-separated values), plus a FASTA file with sequences for each gene in the database. This version of the dataset, version 4.17, contains 5,521 publications, covering 22,408 pathogen–host interactions and 9,973 pathogen genes across 296 pathogen species and 249 host species.

Erratum

Please note that the funding information included in the readme file for this dataset (specifically README.md and README.html) is incorrect. The correct funding sources are Growing Health [BB/X010953/1; BBS/E/RH/230003A] and Delivering Sustainable Wheat [BB/X011003/1; BBS/E/RH/230001B], both ultimately funded by the Biotechnology and Biological Sciences Research Council (BBSRC). The metadata for this dataset has been amended to use the correct funding sources (updated 16 September 2024).

jats:titleAbstract/jats:title jats:pSince 2005, the Pathogen–Host Interactions Database (PHI-base) has manually curated experimentally verified pathogenicity, virulence and effector genes from fungal, bacterial and protist pathogens, which infect animal, plant, fish, insect and/or fungal hosts. PHI-base (www.phi-base.org) is devoted to the identification and presentation of phenotype information on pathogenicity and effector genes and their host interactions. Specific gene alterations that did not alter the in host interaction phenotype are also presented. PHI-base is invaluable for comparative analyses and for the discovery of candidate targets in medically and agronomically important species for intervention. Version 4.12 (September 2021) contains 4387 references, and provides information on 8411 genes from 279 pathogens, tested on 228 hosts in 18, 190 interactions. This provides a 24% increase in gene content since Version 4.8 (September 2019). Bacterial and fungal pathogens represent the majority of the interaction data, with a 54:46 split of entries, whilst protists, protozoa, nematodes and insects represent 3.6% of entries. Host species consist of approximately 54% plants and 46% others of medical, veterinary and/or environmental importance. PHI-base data is disseminated to UniProtKB, FungiDB and Ensembl Genomes. PHI-base will migrate to a new gene-centric version (version 5.0) in early 2022. This major development is briefly described./jats:p

Open access or shared research data must comply with (HIPAA) patient privacy regulations. These regulations require the de-identification of datasets before they can be placed in the public domain. The process of image de-identification is time consuming, requires significant human resources, and is prone to human error. Automated image de-identification algorithms have been developed but the research community requires some method of evaluation before such tools can be widely accepted. This evaluation requires a robust dataset that can be used as part of an evaluation process for de-identification algorithms.

We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM image information objects were selected from datasets published in TCIA. Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM data elements to mimic typical clinical imaging exams. The evaluation dataset was de-identified by a TCIA curation team using standard TCIA tools and procedures. We are publishing the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (result of TCIA curation) in advance of a potential competition, sponsored by the National Cancer Institute (NCI), for de-identification algorithm evaluation, and de-identification of medical image datasets. The evaluation dataset published here is a subset of a larger evaluation dataset that was created under contract for the National Cancer Institute. This subset is being published to allow researchers to test their de-identification algorithms and promote standardized procedures for validating automated de-identification.

Database that catalogs experimentally verified pathogenicity, virulence and effector genes from fungal, Oomycete and bacterial pathogens, which infect animal, plant, fungal and insect hosts. It is an invaluable resource in the discovery of genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention. In collaboration with the FRAC team, it also includes antifungal compounds and their target genes. Each entry is curated by domain experts and is supported by strong experimental evidence (gene disruption experiments, STM etc), as well as literature references in which the original experiments are described. Each gene is presented with its nucleotide and deduced amino acid sequence, as well as a detailed description of the predicted protein's function during the host infection process. To facilitate data interoperability, genes have been annotated using controlled vocabularies and links to external sources (Gene Ontology terms, EC Numbers, NCBI taxonomy, EMBL, PubMed and FRAC).

This dataset contains the predicted prices of the asset Phi over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Comprehensive dataset containing 41 verified Alpha Phi locations in United States with complete contact information, ratings, reviews, and location data.

Effector candidates of E. arachidis in PHI database.

Textbooks Are All You Need

Leveraging Large Language Models (LLMs), there's an opportunity to create a comprehensive open-source repository reminiscent of the historic Library of Alexandria. This initiative represents a preliminary attempt at producing high-quality books covering an extensive range of subjects. The source of these samples varies:

Some generated using the RAG model, referencing Wikipedia or other search data. Some are completely synthetically generated. Some created… See the full description on the dataset page: https://huggingface.co/datasets/open-phi/textbooks.

1024m/PHI-4-Hindi-Instruct-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

Genes encoding pathogenicity related factors derived from PHI database

Interactions between proteins underlie all aspects of complex biological mechanisms. Therefore, methodologies based on complex network analyses can facilitate identification of promising candidate genes involved in phenotypes of interest and put this information into appropriate contexts. To facilitate discovery and gain additional insights into globally important pathogenic fungi, we have reconstructed computationally inferred interactomes using an interolog and domain-based approach for 15 diverse Ascomycete fungal species, across nine orders, specifically Aspergillus fumigatus, Bipolaris sorokiniana, Blumeria graminis f. sp. hordei, Botrytis cinerea, Colletotrichum gloeosporioides, Colletotrichum graminicola, Fusarium graminearum, Fusarium oxysporum f. sp. lycopersici, Fusarium verticillioides, Leptosphaeria maculans, Magnaporthe oryzae, Saccharomyces cerevisiae, Sclerotinia sclerotiorum, Verticillium dahliae, and Zymoseptoria tritici. Network cartography analysis was associated with functional patterns of annotated genes linked to the disease-causing ability of each pathogen. In addition, for the best annotated organism, namely F. graminearum, the distribution of annotated genes with respect to network structure was profiled using a random walk with restart algorithm, which suggested possible co-location of virulence-related genes in the protein–protein interaction network. In a second ‘use case’ study involving two networks, namely B. cinerea and F. graminearum, previously identified small silencing plant RNAs were mapped to their targets. The F. graminearum phenotypic network analysis implicates eight B. cinerea targets and 35 F. graminearum predicted interacting proteins as prime candidate virulence genes for further testing. All 15 networks have been made accessible for download at www.phi-base.org providing a rich resource for major crop plant pathogens.

PHI Tokenization Platforms Market Outlook

According to our latest research, the Global PHI Tokenization Platforms market size was valued at $1.2 billion in 2024 and is projected to reach $5.8 billion by 2033, expanding at a robust CAGR of 19.2% during 2024–2033. The primary driver behind this significant market growth is the increasing stringency of data privacy regulations worldwide, which compels healthcare organizations to adopt advanced solutions for protecting sensitive personal health information (PHI). As cyber threats intensify and the volume of digital health data surges, PHI tokenization platforms are becoming indispensable tools for ensuring regulatory compliance, minimizing data breach risks, and enabling secure data sharing across the healthcare ecosystem. This trend is further bolstered by the rapid digital transformation initiatives underway in the healthcare sector, which necessitate robust, scalable, and interoperable data security solutions.

Regional Outlook

North America currently commands the largest share of the PHI Tokenization Platforms market, accounting for over 45% of the global market value in 2024. This dominance is attributed to the region’s mature healthcare infrastructure, widespread adoption of electronic health records, and the presence of stringent regulatory frameworks such as HIPAA and HITECH. The United States, in particular, has witnessed a surge in cyberattacks targeting healthcare organizations, prompting accelerated investments in advanced data security solutions like PHI tokenization platforms. Additionally, the region is home to several leading technology providers and innovative startups, which further fuels product development and market penetration. The combination of regulatory pressure, high awareness, and technological maturity positions North America as the epicenter of innovation and adoption in the PHI tokenization landscape.

The Asia Pacific region is projected to be the fastest-growing market for PHI tokenization platforms, with an anticipated CAGR exceeding 22% during the forecast period. This rapid expansion is driven by the digitalization of healthcare systems, increasing healthcare IT spending, and the rising frequency of data breaches in countries like China, India, and Japan. Governments across the region are enacting new data protection laws and incentivizing healthcare providers to upgrade their cybersecurity protocols. Furthermore, the proliferation of cloud-based healthcare solutions and the growing adoption of telemedicine are generating fresh demand for secure data management platforms. Multinational vendors are increasingly targeting Asia Pacific for strategic expansions, recognizing the vast untapped potential and the ongoing shift towards integrated digital health ecosystems.

Emerging economies in Latin America and the Middle East & Africa are witnessing a gradual uptick in PHI tokenization platform adoption, albeit from a relatively low base. Adoption challenges in these regions stem from limited healthcare IT budgets, fragmented regulatory landscapes, and a shortage of skilled cybersecurity professionals. However, localized demand is rising as healthcare providers seek to modernize their data infrastructure and comply with evolving privacy regulations. International development agencies and regional governments are also playing a key role by launching awareness programs and offering incentives for digital health transformation. While growth rates are promising, market penetration remains constrained by infrastructural and policy-related hurdles, underscoring the need for tailored solutions and capacity-building initiatives.

Report Scope

Phi Phi Trading Company Limited Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Table provides RRES v5 gene ID, PHI identifier ID from PHI-base, Uniprot protein ID, gene function, mutant phenotype, experimental technique, author reference, and year published. (XLSX)

This dataset includes the comparative qRT-PCR analysis of Phi, Dfvf, and Cazy gene expression. It also features the associated database containing relevant gene information, used to evaluate gene activity across different conditions.

Approved curation sessions from the PHI-Canto curation tool, as of 13 December 2022. PHI-Canto is used to curate literature on pathogen–host interactions, and supplies data to PHI-base, the Pathogen–Host Interactions Database.

The curated data is exported in JSON format, and contained in a single JSON object. The object's keys are the identifiers for individual curation sessions, where each curation session corresponds to one publication. The export contains the raw data exported by PHI-Canto: no further processing has been applied.

There is a JSON Schema file included that describes the data fields used in the export file.

Phi Nguyen Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

View details of Phi import data and shipment reports in US with product description, price, date, quantity, major us ports, countries and US buyers/importers list, overseas suppliers/exporters list.

DNA sequence and relationships for PHI (protein)