ArticleCurrent genomics2021
On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples.
Article in Current genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Conformal novelty detection for multiple metabolic networks.BMC bioinformatics · 2024Article
- From Sequence to Solution: Intelligent Learning Engine Optimization in Drug Discovery and Protein Analysis.Biotech (Basel (Switzerland)) · 2024Article
- A Tiny Viral Protein, SARS-CoV-2-ORF7b: Functional Molecular Mechanisms.Biomolecules · 2024Article
- Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network.PloS one · 2024Article
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8 authors.
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Abstract
backgroundThe increasing availability of omics data collected from patients affected by severe pathologies, such as cancer, is fostering the development of data science methods for their analysis.
introductionThe combination of data integration and machine learning approaches can provide new powerful instruments to tackle the complexity of cancer development and deliver effective diagnostic and prognostic strategies.
methodsWe explore the possibility of exploiting the topological properties of sample-specific metabolic networks as features in a supervised classification task. Such networks are obtained by projecting transcriptomic data from RNA-seq experiments on genome-wide metabolic models to define weighted networks modeling the overall metabolic activity of a given sample.
resultsWe show the classification results on a labeled breast cancer dataset from the TCGA database, including 210 samples (cancer
conclusionThese findings demonstrate that the topological properties of sample-specific metabolic networks are effective in classifying cancer and normal samples, suggesting that useful information can be extracted from a relatively limited number of features.
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