Cellular State Transformations Using Deep Learning for Precision Medicine Applications.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 33205131.
- Also identified by DOI 10.1016/j.patter.2020.100087 and PMC identifier 7660411.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
We introduce the Transcriptome State Perturbation Generator (TSPG) as a novel deep-learning method to identify changes in genomic expression that occur between tissue states using generative adversarial networks. TSPG learns the transcriptome perturbations from RNA-sequencing data required to shift from a source to a target class. We apply TSPG as an effective method of detecting biologically relevant alternate expression patterns between normal and tumor human tissue samples. We demonstrate that the application of TSPG to expression data obtained from a biopsy sample of a patient's kidney cancer can identify patient-specific differentially expressed genes between their individual tumor sample and a target class of healthy kidney gene expression. By utilizing TSPG in a precision medicine application in which the patient sample is not replicated (i.e., <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>n</mi> <mo>=</mo> <mn>1</mn></mrow> </math> ), we present a novel technique of determining significant transcriptional aberrations that can be used to help identify potential targeted therapies.