Author

I. A. Solovev

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Review Open access Oct 2026

Artificial 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 i...

I. A. Solovev, D. Golubev · Pharmacokinetics and Pharmac... · 0 citations

#federated learning Review Open access Sep 2026

Oncology databases for predicting tumor radiosensitivity and chemosensitivity: a critical translational synthesis

A critical translational synthesis of oncology databases and computational resources used to predict tumor radiosensitivity and chemosensitivity and shows homologous recombination deficiency in the context of DNA-damaging therapy and genomic-adjusted radiation dose as a research predictor of radiotherapy benefit is dem...

I. A. Solovev, D. Golubev · Real-World Data & Eviden... · 0 citations

Open access Aug 2026

[Risk factors for adverse outcomes after percutaneous coronary intervention in patients aged over 75 years with non-ST-segment elevation acute coronary syndrome.]

Among patients over 75 years of age, the PCI group had a higher incidence of adverse events, total complications, and mortality compared to the conservative management group, and the strongest predictors of mortality in this group were patient age, elevated CPK-MB levels at admission, and total complications.

A. N. Bogomolov, O. Kurochkina, K. Kozlov et al. · Advances in gerontology = Us... · 0 citations