aceArtificial intelligence in cancer transcriptomics: methods, applications in oncology, and limitations
Background. Transcriptomic data show which genes are active in cells and tissues. Machine-learning and deep-learning methods use these data to classify samples, integrate different datasets, and predict biological properties and treatment response. In oncology, a major challenge is transferring patterns identified in experimental models to tumor samples from patients. Objective. To critically synthesize current artificial-intelligence methods for transcriptomic analysis, with emphasis on oncology applications, t…
Larger, more complex models are not guaranteed to outperform simpler methods, and moving models from experimental systems to patient tumors requires careful handling of batch effects, information leakage, and external validation.
The source is a narrative review, and it states that AI value depends on data quality, correct task definition, and performance in independent cohorts; it does not provide pooled quantitative effect estimates.
Evidence
- Peer-reviewedPharmacokinetics and Pharmacodynamics2026-10-16
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Truvace Impact Record TRV-2026-1711, v1: “Artificial intelligence in cancer transcriptomics: methods, applications in oncology, and limitations.” Truvace, 2026-10-17. /record/TRV-2026-1711 (accessed at citation time). sha256 77510ff63402304d…
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