ArticleNature genetics2014
A multiscale statistical mechanical framework integrates biophysical and genomic data to assemble cancer networks.
Article in Nature genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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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
34 citing papers in PubMed.
- Accurate affinity models for SH2 domains from peptide binding assays and free-energy regression.Protein science : a publication of the Protein Society · 2025Article
- Update on Structure and Function of SH2 Domains: Mechanisms and Emerging Targeting Strategies.International journal of molecular sciences · 2025Review
- Leveraging machine learning models for peptide-protein interaction prediction.RSC chemical biology · 2024Review
- Article
- Review
- Multilevel Coarsening for Interactive Visualization of Large Bipartite Networks.Frontiers in research metrics and analytics · 2022Article
- New analysis pipeline for high-throughput domain-peptide affinity experiments improves SH2 interaction data.The Journal of biological chemistry · 2020Article
- Biophysical prediction of protein-peptide interactions and signaling networks using machine learning.Nature methods · 2020Article
- Interplay between HGAL and Grb2 proteins regulates B-cell receptor signaling.Blood advances · 2019Article
- Predicting Antigenicity of Influenza A Viruses Using biophysical ideas.Scientific reports · 2019Article
- Protein ensembles link genotype to phenotype.PLoS computational biology · 2019Review
- Next Generation Networks: Featuring the Potential Role of Emerging Applications in Translational Oncology.Journal of clinical medicine · 2019Article
- Beyond genome-wide significance: integrative approaches to the interpretation and extension of GWAS findings for alcohol use disorder.Addiction biology · 2019Review
- Typing tumors using pathways selected by somatic evolution.Nature communications · 2018Article
- Classifying tumors by supervised network propagation.Bioinformatics (Oxford, England) · 2018Article
- Network spectra for drug-target identification in complex diseases: new guns against old foes.Applied network science · 2018Review
- Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions.Frontiers in immunology · 2018Article
- Understanding cancer complexome using networks, spectral graph theory and multilayer framework.Scientific reports · 2017Article
- The recurrent architecture of tumour initiation, progression and drug sensitivity.Nature reviews. Cancer · 2017Review
- Protein abundance of AKT and ERK pathway components governs cell type-specific regulation of proliferation.Molecular systems biology · 2017Article
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5 authors.
Funding
Abstract
Functional interpretation of genomic variation is critical to understanding human disease, but it remains difficult to predict the effects of specific mutations on protein interaction networks and the phenotypes they regulate. We describe an analytical framework based on multiscale statistical mechanics that integrates genomic and biophysical data to model the human SH2-phosphoprotein network in normal and cancer cells. We apply our approach to data in The Cancer Genome Atlas (TCGA) and test model predictions experimentally. We find that mutations mapping to phosphoproteins often create new interactions but that mutations altering SH2 domains result almost exclusively in loss of interactions. Some of these mutations eliminate all interactions, but many cause more selective loss, thereby rewiring specific edges in highly connected subnetworks. Moreover, idiosyncratic mutations appear to be as functionally consequential as recurrent mutations. By synthesizing genomic, structural and biochemical data, our framework represents a new approach to the interpretation of genetic variation.
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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 Socratic graph.