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A kernel-based integration of genome-wide data for clinical decision support. | LitMetric

AI Article Synopsis

  • Microarray technology alone has limitations in capturing the full biological complexity of tumors due to factors like alternative splicing and post-translational modifications, necessitating the integration of multiple omics data sources.
  • A kernel-based approach was proposed for clinical decision support, which combines various genome-wide data before applying a weighted least squares support vector machine to predict outcomes for rectal and prostate cancers.
  • Results showed improved prediction accuracy when integrating different types of omics data, indicating that using comprehensive multi-omics data sets enhances the performance of clinical models in cancer prediction.

Article Abstract

Background: Although microarray technology allows the investigation of the transcriptomic make-up of a tumor in one experiment, the transcriptome does not completely reflect the underlying biology due to alternative splicing, post-translational modifications, as well as the influence of pathological conditions (for example, cancer) on transcription and translation. This increases the importance of fusing more than one source of genome-wide data, such as the genome, transcriptome, proteome, and epigenome. The current increase in the amount of available omics data emphasizes the need for a methodological integration framework.

Methods: We propose a kernel-based approach for clinical decision support in which many genome-wide data sources are combined. Integration occurs within the patient domain at the level of kernel matrices before building the classifier. As supervised classification algorithm, a weighted least squares support vector machine is used. We apply this framework to two cancer cases, namely, a rectal cancer data set containing microarray and proteomics data and a prostate cancer data set containing microarray and genomics data. For both cases, multiple outcomes are predicted.

Results: For the rectal cancer outcomes, the highest leave-one-out (LOO) areas under the receiver operating characteristic curves (AUC) were obtained when combining microarray and proteomics data gathered during therapy and ranged from 0.927 to 0.987. For prostate cancer, all four outcomes had a better LOO AUC when combining microarray and genomics data, ranging from 0.786 for recurrence to 0.987 for metastasis.

Conclusions: For both cancer sites the prediction of all outcomes improved when more than one genome-wide data set was considered. This suggests that integrating multiple genome-wide data sources increases the predictive performance of clinical decision support models. This emphasizes the need for comprehensive multi-modal data. We acknowledge that, in a first phase, this will substantially increase costs; however, this is a necessary investment to ultimately obtain cost-efficient models usable in patient tailored therapy.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2684660PMC
http://dx.doi.org/10.1186/gm39DOI Listing

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