PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning is a research paper published in Journal of Molecular Biology (2022). On theSindex it has a DataRank of 0. It has been cited 64 times.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
National Institute of Allergy and Infectious Diseases
Grant: AI134678
National Science Foundation
Grant: ACI1548562
National Science Foundation
Grant: DBI2030790
National Science Foundation
Grant: IIS1901191
National Science Foundation
Grant: MTM2025426
National Institute of General Medical Sciences
Grant: GM136422
National Institute of General Medical Sciences
Grant: S10OD026825
National Institutes of Health
Grant: 5R35GM136422-02
Advanced approaches to protein structure prediction
National Institutes of Health
Grant: 1S10OD026825-01A1
High-Performance Computing Cluster for Biomedical Research
National Science Foundation
Grant: 1901191
III: Medium: Collaborative Research: Multi-level computational approaches to protein function prediction
National Science Foundation
Grant: 2025426
MTM 2: Combining structural informatics and crosslinking mass spectrometry to predict the key protein-protein interactions shaping symbiotic microbial communities
National Science Foundation
Grant: 2030790
IIBR: Informatics: RAPID: Genome-wide Structure and Function Modeling of the SARS-CoV-2 Virus
National Institutes of Health
Grant: 5R01AI134678-03
Structure-based functional annotation of microbial genomes
FWCI
8.12
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals