phkineticaФармакокинетика и ФармакодинамикаPharmacokinetics and Pharmacodynamics2587-78362686-8830ООО «Издательство ОКИ»10.37489/2587-7836-2026-3-84-94DFQKQAphkinetica-553Research ArticleМЕТОДЫ РАЗРАБОТКИ НОВЫХ ЛЕКАРСТВЕННЫХ СРЕДСТВMETHODS FOR DEVELOPMENT OF NEW DRUGSИскусственный интеллект в онкотранскриптомике: методы, применение в онкологии и ограниченияArtificial intelligence in cancer transcriptomics: methods, applications in oncology, and limitationshttps://orcid.org/0000-0002-1038-2271СоловьевИ. А.SolovevI. A.

Соловьев Илья Андреевич — к. б. н., в. н. с. научно-исследовательской лаборатории «Трансляционная биоинформатика и системная биология»

Сыктывкар 

Ilya A. Solovev — PhD, Cand. Sci. (Biol.), Leading Researcher, Translational Bioinformatics and Systems Biology Research Laboratory

Syktyvkar 

i@ilyasolovev.ruhttps://orcid.org/0000-0003-0570-8211ГолубевД. А.GolubevD. A.

Голубев Денис Анатольевич — м. н. с. научно-исследовательской лаборатории «Трансляционная биоинформатика и системная биология»

Сыктывкар 

Denis A. Golubev — Junior Researcher, Translational Bioinformatics and Systems Biology Research Laboratory

Syktyvkar 

denismeatboy@gmail.comФГБОУ ВО «Сыктывкарский государственный университет имени Питирима Сорокина»РоссияPitirim Sorokin Syktyvkar State UniversityRussian Federation202616102026038494Copyright © Соловьев И.А., Голубев Д.А., 20262026Соловьев И.А., Голубев Д.А.Solovev I.A., Golubev D.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.pharmacokinetica.ru/jour/article/view/553

Актуальность. Транскриптомные данные показывают, какие гены активны в клетках и тканях. Методы машинного и глубокого обучения позволяют использовать эти данные для классификации образцов, объединения разных наборов данных и прогнозирования биологических свойств и ответа на лечение. В онкологии ключевой проблемой остаётся перенос закономерностей, найденных в экспериментальных моделях, на реальные опухолевые образцы пациентов.Цель. Критически систематизировать современные методы искусственного интеллекта для анализа транскриптомных данных с акцентом на применение в онкологии, перенос моделей на данные пациентов и требования к клинической проверке.Материалы и методы. Выполнен критический нарративный обзор первичных исследований и методологических работ. Дополнительно описана авторская выборка публикаций за 2021–2026 гг. Содержательная часть обзора актуализирована по состоянию на 13 августа 2026 г.Результаты. Наиболее развитые направления включают анализ данных отдельных клеток, пространственную диагностику опухолей и прогнозирование лекарственного ответа. Независимые сравнительные испытания показывают, что увеличение размера и сложности модели не гарантирует преимущества над более простыми методами. Для переноса результатов в клинику необходимо учитывать клеточный состав образца и технические различия между сериями измерений, сопоставлять экспериментальные и клинические показатели ответа, предотвращать утечку информации и проводить внешнюю валидацию.Заключение. Искусственный интеллект следует рассматривать как инструмент анализа данных, а не как замену экспериментальной и клинической проверке. Его ценность определяется качеством данных, корректностью постановки задачи и способностью модели сохранять качество на независимых выборках.

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, transfer of models to patient data, and requirements for clinical validation.Materials and Methods. We performed a critical narrative review of primary studies and methodological papers and additionally described the authors' publication sample for 2021–2026. The literature review was updated through August 13, 2026.Results. The most developed areas include single-cell data analysis, spatially resolved tumor diagnostics, and drug-response prediction. Independent comparative evaluations show that increasing model size and complexity does not guarantee better performance than simpler methods. Clinical translation requires accounting for cellular composition and technical differences between measurement batches, aligning experimental and clinical response measures, preventing information leakage, and performing external validation.Conclusion. Artificial intelligence should be viewed as a data-analysis tool rather than a substitute for experimental or clinical validation. Its value depends on data quality, correct task definition, and the ability to maintain performance in independent cohorts.

транскриптомикаискусственный интеллектмашинное обучениеглубокое обучениеонкологияодноклеточное секвенирование РНКпространственная транскриптомикафармакогеномикаклиническая проверка моделейtranscriptomicsartificial intelligencemachine learningdeep learningoncologysingle-cell RNA-seqspatial transcriptomicspharmacogenomicsclinical validationИсследование выполнено в рамках государственного задания по теме «Создание первой в мире ИИ-системы подбора состава и дозировок радио- и химиотерапии, обученной на основе всех интернет-доступных транскриптомных данных о радиочувствительности и химиочувствительности различных злокачественных опухолей».The study was carried out within the framework of the state assignment on the topic “Creation of the world’s first AI system for selecting the composition and dosage of radio- and chemotherapy, trained on the basis of all internet-available transcriptomic data on radiosensitivity and chemosensitivity of various malignant tumors”.ReferencesSubramanian A, Narayan R, Corsello SM, et al. 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The authors declare that there are no conflicts of interest present.