ArticleBMC genomics2017
HAPPI-2: a Comprehensive and High-quality Map of Human Annotated and Predicted Protein Interactions.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 48 citations in OpenAlex.
- GETgene-AI: a framework for prioritizing actionable cancer drug targets.Frontiers in systems biology · 2025Article
- Temporal GeneTerrain: advancing precision medicine through dynamic gene expression visualization.Frontiers in bioinformatics · 2025Article
- Mondrian Abstraction and Language Model Embeddings for Differential Pathway Analysis.bioRxiv : the preprint server for biology · 2024Article
- Discovering Potential in Non-Cancer Medications: A Promising Breakthrough for Multiple Myeloma Patients.Cancers · 2024Review
- Drug repurposing for cancer therapy.Signal transduction and targeted therapy · 2024Review
- Explorative Discovery of Gene Signatures and Clinotypes in Glioblastoma Cancer Through GeneTerrain Knowledge Map Representation.bioRxiv : the preprint server for biology · 2024Article
- PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis.Frontiers in bioinformatics · 2024Article
- DEMA: a distance-bounded energy-field minimization algorithm to model and layout biomolecular networks with quantitative features.Bioinformatics (Oxford, England) · 2022Article
- TUBB4A interacts with MYH9 to protect the nucleus during cell migration and promotes prostate cancer via GSK3β/β-catenin signalling.Nature communications · 2022Article
- WINNER: A network biology tool for biomolecular characterization and prioritization.Frontiers in big data · 2022Article
- PAGER Web APP: An Interactive, Online Gene Set and Network Interpretation Tool for Functional Genomics.Frontiers in genetics · 2022Article
- A comprehensive review of integrative pharmacology-based investigation: A paradigm shift in traditional Chinese medicine.Acta pharmaceutica Sinica. B · 2021Review
- PAGER-CoV: a comprehensive collection of pathways, annotated gene-lists and gene signatures for coronavirus disease studies.Nucleic acids research · 2021Article
- Biological Network Mining.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Network Biology Approaches to Achieve Precision Medicine in Inflammatory Bowel Disease.Frontiers in genetics · 2021Review
- Drug repurposing for breast cancer therapy: Old weapon for new battle.Seminars in cancer biology · 2021Review
- Cancer Explant Models.Current topics in microbiology and immunology · 2021Article
- Polyvalent therapeutic vaccine for type 2 diabetes mellitus: Immunoinformatics approach to study co-stimulation of cytokines and GLUT1 receptors.BMC molecular and cell biology · 2020Article
- Genome-wide functional association networks: background, data & state-of-the-art resources.Briefings in bioinformatics · 2020Review
- WIPER: Weighted in-Path Edge Ranking for biomolecular association networks.Quantitative biology (Beijing, China) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundHuman protein-protein interaction (PPI) data is essential to network and systems biology studies. PPI data can help biochemists hypothesize how proteins form complexes by binding to each other, how extracellular signals propagate through post-translational modification of de-activated signaling molecules, and how chemical reactions are coupled by enzymes involved in a complex biological process. Our capability to develop good public database resources for human PPI data has a direct impact on the quality of future research on genome biology and medicine.
resultsThe database of Human Annotated and Predicted Protein Interactions (HAPPI) version 2.0 is a major update to the original HAPPI 1.0 database. It contains 2,922,202 unique protein-protein interactions (PPI) linked by 23,060 human proteins, making it the most comprehensive database covering human PPI data today. These PPIs contain both physical/direct interactions and high-quality functional/indirect interactions. Compared with the HAPPI 1.0 database release, HAPPI database version 2.0 (HAPPI-2) represents a 485% of human PPI data coverage increase and a 73% protein coverage increase. The revamped HAPPI web portal provides users with a friendly search, curation, and data retrieval interface, allowing them to retrieve human PPIs and available annotation information on the interaction type, interaction quality, interacting partner drug targeting data, and disease information. The updated HAPPI-2 can be freely accessed by Academic users at http://discovery.informatics.uab.edu/HAPPI .
conclusionsWhile the underlying data for HAPPI-2 are integrated from a diverse data sources, the new HAPPI-2 release represents a good balance between data coverage and data quality of human PPIs, making it ideally suited for network biology.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.