ArticlePatterns (New York, N.Y.)2020
Cellular State Transformations Using Deep Learning for Precision Medicine Applications.
Article in Patterns (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 12 citations in OpenAlex.
- Artificial intelligence virtual extracellular vesicles (AIVEVs).Bioactive materials · 2026Review
- Predicting condition-aware drug-induced transcriptional responses via a latent diffusion model.Bioinformatics (Oxford, England) · 2026Article
- GENESIS-SHIELD: an interpretable ensemble for anomaly detection in CRISPR genomic-workflow security.Frontiers in artificial intelligence · 2026Article
- GEMDiff: a diffusion workflow bridges between normal and tumor gene expression states: a breast cancer case study.Briefings in bioinformatics · 2025Article
- Homogeneous Space Construction and Projection for Single-Cell Expression Prediction Based on Deep Learning.Bioengineering (Basel, Switzerland) · 2023Article
- Simulating the restoration of normal gene expression from different thyroid cancer stages using deep learning.BMC cancer · 2022Article
- Stabilizing deep tomographic reconstruction: Part B. Convergence analysis and adversarial attacks.Patterns (New York, N.Y.) · 2022Article
- A primer on machine learning techniques for genomic applications.Computational and structural biotechnology journal · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.,
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Identifiers
What OpenQuestion 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 OpenQuestion graph.