ArticleCell systems2016
Compact Integration of Multi-Network Topology for Functional Analysis of Genes.
Article in Cell systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers, 1 of them a synthesis that pooled it.
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
115 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Synthetic lethal gene pairs: Experimental approaches and predictive models.Frontiers in genetics · 2022Pooled it
- Supervised Machine-Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations.CPT: pharmacometrics & systems pharmacology · 2017Trial
- Enhancing link prediction in biomedical knowledge graphs with BioPathNet.Nature biomedical engineering · 2026Article
- Revisiting co-expression-based automated function prediction in yeast with neural networks and updated Gene Ontology annotations.PloS one · 2026Article
- REPEL - Random Embedding Perturbation for Enhanced Learning of Protein Function.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- MGANSL: multi-network representation generating with generative adversarial network for synthetic lethality prediction.BMC bioinformatics · 2025Article
- IMAGO: An Improved Model Based on Attention Mechanism for Enhanced Protein Function Prediction.Biomolecules · 2025Article
- Biology-driven insights into the power of single-cell foundation models.Genome biology · 2025Article
- A multi-layer encoder prediction model for individual sample specific gene combination effect (MLEC-iGeneCombo).PLoS computational biology · 2025Article
- Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives.Journal of advanced research · 2025Review
- A compendium of synthetic lethal gene pairs defined by extensive combinatorial pan-cancer CRISPR screening.Genome biology · 2025Article
- Pisces: A multi-modal data augmentation approach for drug combination synergy prediction.Cell genomics · 2025Article
- The synthetic lethal interaction between CDS1 and CDS2 is a vulnerability in uveal melanoma and across multiple tumor types.Nature genetics · 2025Article
- GOBeacon: An ensemble model for protein function prediction enhanced by contrastive learning.Protein science : a publication of the Protein Society · 2025Article
- Shared host genetic landscape of respiratory viral infection.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Integrative network analysis reveals novel moderators of Aβ-Tau interaction in Alzheimer's disease.Alzheimer's research & therapy · 2025Article
- A Survey of Biological Function Prediction Methods with Focus on Natural Language Processing (NLP) and Large Language Models (LLM).Methods in molecular biology (Clifton, N.J.) · 2025Article
- Deep learning methods for protein function prediction.Proteomics · 2025Review
- Graph Neural Network-Based Approaches for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning.Frontiers in pharmacology · 2025Article
55 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
3 authors.
Funding
Abstract
The topological landscape of molecular or functional interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, a pressing yet-unsolved challenge is how to combine multiple heterogeneous networks, each having different connectivity patterns, to achieve more accurate inference. Here, we describe the Mashup framework for scalable and robust network integration. In Mashup, the diffusion in each network is first analyzed to characterize the topological context of each node. Next, the high-dimensional topological patterns in individual networks are canonically represented using low-dimensional vectors, one per gene or protein. These vectors can then be plugged into off-the-shelf machine learning methods to derive functional insights about genes or proteins. We present tools based on Mashup that achieve state-of-the-art performance in three diverse functional inference tasks: protein function prediction, gene ontology reconstruction, and genetic interaction prediction. Mashup enables deeper insights into the structure of rapidly accumulating and diverse biological network data and can be broadly applied to other network science domains.
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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.