ArticleScientific reports2020
Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.Bioinformatics (Oxford, England) · 2026Article
- miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources.Bioinformatics (Oxford, England) · 2025Article
- Multi-Omic Graph Diagnosis (MOGDx): a data integration tool to perform classification tasks for heterogeneous diseases.Bioinformatics (Oxford, England) · 2024Article
- Exploring the similarity between genetic diseases improves their differential diagnosis and the understanding of their etiology.European journal of human genetics : EJHG · 2024Article
- LanDis: the disease landscape explorer.European journal of human genetics : EJHG · 2024Article
- Topology and redescriptions detect multiple alternative biological pathways from clinical phenotypes.Experimental biology and medicine (Maywood, N.J.) · 2022Article
- Heterogeneous data integration methods for patient similarity networks.Briefings in bioinformatics · 2022Review
- Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments.IEEE access : practical innovations, open solutions · 2020Article
- Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 3 countries.
Funding
Abstract
Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled in a graph-structured space that represents gene expression relationships between patients. Then a kernel-based semi-supervised transductive algorithm is applied to the graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of patients. Experimental tests involving several publicly available datasets of patients afflicted with pancreatic, breast, colon and colorectal cancer show that our proposed method is competitive with state-of-the-art supervised and semi-supervised predictive systems. Importantly, P-Net also provides interpretable models that can be easily visualized to gain clues about the relationships between patients, and to formulate hypotheses about their stratification.
Indexed as
Identifiers
What Socratic 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 Socratic graph.