Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction.
Algorithms
Artificial Intelligence
Breast Neoplasms
/ diagnosis
Colorectal Neoplasms
/ diagnosis
Computational Biology
/ methods
Datasets as Topic
Female
Gene Regulatory Networks
Humans
Individuality
Male
Neural Networks, Computer
Pancreatic Neoplasms
/ diagnosis
Phenotype
Prognosis
Transcriptome
Treatment Outcome
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
27 02 2020
27 02 2020
Historique:
received:
23
01
2019
accepted:
05
11
2019
entrez:
29
2
2020
pubmed:
29
2
2020
medline:
15
12
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32107391
doi: 10.1038/s41598-020-60235-8
pii: 10.1038/s41598-020-60235-8
pmc: PMC7046773
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3612Subventions
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/F00964X/1
Pays : United Kingdom
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