ArticleBMC bioinformatics2017
SPRINT: ultrafast protein-protein interaction prediction of the entire human interactome.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed.
- A robust framework for protein-protein interaction prediction with multi-objective ensemble learning and embedding-based representations.Scientific reports · 2026Article
- Sequence-Based Protein-Protein Interaction Prediction and Its Applications in Drug Discovery.Cells · 2025Review
- DensePPI-2: a bio-inspired update for sequence-based PPI prediction leveraging mutation rates.Briefings in bioinformatics · 2025Article
- Comprehensive network pharmacology and experimentation to unveil the therapeutic efficacy and mechanisms of gypenoside LI in anaplastic thyroid cancer.BMC cancer · 2025Article
- Advances in protein-protein interaction prediction: a deep learning perspective.Frontiers in bioinformatics · 2025Review
- INTREPPPID-an orthologue-informed quintuplet network for cross-species prediction of protein-protein interaction.Briefings in bioinformatics · 2024Article
- Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.Chemical reviews · 2024Review
- High-Throughput Interactome Determination via Sulfur Anomalous Scattering.The journal of physical chemistry letters · 2024Article
- Cracking the black box of deep sequence-based protein-protein interaction prediction.Briefings in bioinformatics · 2024Article
- In silico protein function prediction: the rise of machine learning-based approaches.Medical review (2021) · 2023Review
- Assessment of GO-Based Protein Interaction Affinities in the Large-Scale Human-Coronavirus Family Interactome.Vaccines · 2023Article
- Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode.Frontiers in bioinformatics · 2023Article
- Recent developments of sequence-based prediction of protein-protein interactions.Biophysical reviews · 2022Review
- Ten quick tips for sequence-based prediction of protein properties using machine learning.PLoS computational biology · 2022Article
- Review
- PITHIA: Protein Interaction Site Prediction Using Multiple Sequence Alignments and Attention.International journal of molecular sciences · 2022Article
- Reciprocal perspective as a super learner improves drug-target interaction prediction (MUSDTI).Scientific reports · 2022Article
- PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning.Journal of molecular biology · 2022Article
- Assessing sequence-based protein-protein interaction predictors for use in therapeutic peptide engineering.Scientific reports · 2022Article
- The RelB-BLNK Axis Determines Cellular Response to a Novel Redox-Active Agent Betamethasone during Radiation Therapy in Prostate Cancer.International journal of molecular sciences · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundProteins perform their functions usually by interacting with other proteins. Predicting which proteins interact is a fundamental problem. Experimental methods are slow, expensive, and have a high rate of error. Many computational methods have been proposed among which sequence-based ones are very promising. However, so far no such method is able to predict effectively the entire human interactome: they require too much time or memory.
resultsWe present SPRINT (Scoring PRotein INTeractions), a new sequence-based algorithm and tool for predicting protein-protein interactions. We comprehensively compare SPRINT with state-of-the-art programs on seven most reliable human PPI datasets and show that it is more accurate while running orders of magnitude faster and using very little memory.
conclusionSPRINT is the only sequence-based program that can effectively predict the entire human interactome: it requires between 15 and 100 min, depending on the dataset. Our goal is to transform the very challenging problem of predicting the entire human interactome into a routine task. AVAILABILITY: The source code of SPRINT is freely available from https://github.com/lucian-ilie/SPRINT/ and the datasets and predicted PPIs from www.csd.uwo.ca/faculty/ilie/SPRINT/ .
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.