ArticleNature methods2015
In silico prediction of physical protein interactions and characterization of interactome orphans.
Article in Nature methods, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
71 citing papers in PubMed, 1 synthesis or guideline pooled it, 164 citations in OpenAlex.
- Transcriptomic analysis of castration, chemo-resistant and metastatic prostate cancer elucidates complex genetic crosstalk leading to disease progression.Functional & integrative genomics · 2021Pooled it
- Network resilience of the human interactome to pan-cancer mutations reveals conserved pathway vulnerabilities.iScience · 2026Article
- The CSF Levels of Mitochondrial Phosphoenolpyruvate Carboxykinase 2 as a Novel Biomarker in Alzheimer's Disease.Molecular neurobiology · 2025Article
- PathDIP 5: improving coverage and making enrichment analysis more biologically meaningful.Nucleic acids research · 2024Article
- CrMP-Sol database: classification, bioinformatic analyses and comparison of cancer-related membrane proteins and their water-soluble variant designs.BMC bioinformatics · 2023Article
- Multidimensional computational study to understand non-coding RNA interactions in breast cancer metastasis.Scientific reports · 2023Article
- Review
- ARL6IP1 mediates small-molecule-induced alleviation of Alzheimer pathology through FXR1-dependent BACE1 translation initiation.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- ProtInteract: A deep learning framework for predicting protein-protein interactions.Computational and structural biotechnology journal · 2023Article
- Could Amyloid-β 1-42 or α-Synuclein Interact Directly with Mitochondrial DNA? A Hypothesis.ACS chemical neuroscience · 2022Review
- A review of causal discovery methods for molecular network analysis.Molecular genetics & genomic medicine · 2022Review
- Article
- Multi-view heterogeneous molecular network representation learning for protein-protein interaction prediction.BMC bioinformatics · 2022Article
- Computational analysis of protein-protein interaction network of differentially expressed genes in benign prostatic hyperplasia.Molecular biology research communications · 2022Article
- JAK-STAT core cancer pathway: An integrative cancer interactome analysis.Journal of cellular and molecular medicine · 2022Review
- Deciphering the Host-Pathogen Interactome of the Wheat-Common Bunt System: A Step towards Enhanced Resilience in Next Generation Wheat.International journal of molecular sciences · 2022Article
- IID 2021: towards context-specific protein interaction analyses by increased coverage, enhanced annotation and enrichment analysis.Nucleic acids research · 2022Article
- Deep learning frameworks for protein-protein interaction prediction.Computational and structural biotechnology journal · 2022Review
- Preliminary exploration of the co-regulation of Alzheimer's disease pathogenic genes by microRNAs and transcription factors.Frontiers in aging neuroscience · 2022Article
- Homodimeric and Heterodimeric Interactions among Vertebrate Basic Helix-Loop-Helix Transcription Factors.International journal of molecular sciences · 2021Review
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 7 institutions in 3 countries.
Funding
Abstract
Protein-protein interactions (PPIs) are useful for understanding signaling cascades, predicting protein function, associating proteins with disease and fathoming drug mechanism of action. Currently, only ∼ 10% of human PPIs may be known, and about one-third of human proteins have no known interactions. We introduce FpClass, a data mining-based method for proteome-wide PPI prediction. At an estimated false discovery rate of 60%, we predicted 250,498 PPIs among 10,531 human proteins; 10,647 PPIs involved 1,089 proteins without known interactions. We experimentally tested 233 high- and medium-confidence predictions and validated 137 interactions, including seven novel putative interactors of the tumor suppressor p53. Compared to previous PPI prediction methods, FpClass achieved better agreement with experimentally detected PPIs. We provide an online database of annotated PPI predictions (http://ophid.utoronto.ca/fpclass/) and the prediction software (http://www.cs.utoronto.ca/~juris/data/fpclass/).
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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.