{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T20:15:17Z","timestamp":1793132117825,"version":"4.5.0"},"reference-count":101,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012190","name":"Ministry of Science and Higher Education of the Russian Federation","doi-asserted-by":"publisher","award":["075-15-2025-013"],"award-info":[{"award-number":["075-15-2025-013"]}],"id":[{"id":"10.13039\/501100012190","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012190","name":"Ministry of Science and Higher Education of the Russian Federation","doi-asserted-by":"publisher","award":["075-15-2024-631"],"award-info":[{"award-number":["075-15-2024-631"]}],"id":[{"id":"10.13039\/501100012190","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diseases"],"abstract":"<jats:p>Background\/Objectives: Latent tuberculosis infection (LTBI) represents a critical reservoir for subsequent development of active tuberculosis (ATB) and poses significant challenges for early diagnosis and disease prevention. Traditional immunological assays, such as interferon-gamma release assays (IGRAs), are limited in their ability to reliably distinguish LTBI from ATB. Recent advances in high-throughput omics technologies and machine learning (ML) approaches offer new opportunities for precise, biomarker-based differential diagnostics. Methods: Transcriptomic and proteomic profiling of host immune responses has revealed reproducible gene and protein signatures associated with LTBI and ATB. The integration of ML techniques\u2014including feature selection, dimensionality reduction, multimodal learning, and explainable AI\u2014facilitates the construction of robust diagnostic models. Single-modality signatures, derived from RNA-seq, microarrays, or proteomic assays, are complemented by multimodal approaches that incorporate soluble mediators, immunological readouts, and imaging-derived features. Deep learning frameworks, such as convolutional neural networks and transformer-based architectures, enhance the extraction of complex molecular and structural patterns from high-dimensional datasets. Results: ML-driven analyses of transcriptomic and proteomic data consistently outperform conventional immunological tests in terms of sensitivity, specificity, and clinical applicability. Multimodal integration further improves diagnostic accuracy and robustness. These advances support the translational development of concise, quantitative reverse transcription PCR (qRT-PCR)-based biomarker panels suitable for routine clinical application, enabling early and reliable differentiation between LTBI and ATB. Overall, the combination of high-throughput omics and AI-based analytical frameworks provides a promising pathway for enhancing global tuberculosis diagnostics. Conclusions: This review provides a structured and critical synthesis of transcriptomic and proteomic biomarker research for LTBI and ATB discrimination, with a particular emphasis on machine learning\u2013based analytical frameworks. Unlike previous narrative reviews, we systematically compare data-generating platforms, modelling strategies, validation approaches, and sources of heterogeneity across studies. We further identify key translational barriers, including cohort homogeneity, platform dependency, and limited external validation, and propose directions for future research aimed at improving clinical applicability.<\/jats:p>","DOI":"10.3390\/diseases14020066","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T13:25:49Z","timestamp":1770816349000},"page":"66","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Detection for New Biomarkers of Tuberculosis Infection Activity Using Machine Learning Methods"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9023-6986","authenticated-orcid":false,"given":"Anna An.","family":"Starshinova","sequence":"first","affiliation":[{"name":"Department of Mathematics and Computer Science, Saint Petersburg State University, 199034 Saint Petersburg, Russia"},{"name":"Department of Medicine, Almazov National Medical Research Center of the Ministry of Health of the Russian Federation, 197341 Saint Petersburg, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adilya","family":"Sabirova","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Saint Petersburg State University, 199034 Saint Petersburg, Russia"},{"name":"Medical Department, Bashkir State Medical University, 450000 Ufa, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olesya","family":"Koroteeva","sequence":"additional","affiliation":[{"name":"Department of Medicine, Almazov National Medical Research Center of the Ministry of Health of the Russian Federation, 197341 Saint Petersburg, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Igor","family":"Kudryavtsev","sequence":"additional","affiliation":[{"name":"Institute of Experimental Medicine, Acad. Pavlov St. 12, 197376 Saint Petersburg, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8493-5211","authenticated-orcid":false,"given":"Artem","family":"Rubinstein","sequence":"additional","affiliation":[{"name":"Institute of Experimental Medicine, Acad. Pavlov St. 12, 197376 Saint Petersburg, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6516-7184","authenticated-orcid":false,"given":"Arthur","family":"Aquino","sequence":"additional","affiliation":[{"name":"Department of Medicine, Almazov National Medical Research Center of the Ministry of Health of the Russian Federation, 197341 Saint Petersburg, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrey S.","family":"Trulioff","sequence":"additional","affiliation":[{"name":"Medical Department, Bashkir State Medical University, 450000 Ufa, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ekaterina","family":"Belyaeva","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Saint Petersburg State University, 199034 Saint Petersburg, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anastasia","family":"Kulpina","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Saint Petersburg State University, 199034 Saint Petersburg, Russia"},{"name":"Department of Medicine, Almazov National Medical Research Center of the Ministry of Health of the Russian Federation, 197341 Saint Petersburg, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raul A.","family":"Sharipov","sequence":"additional","affiliation":[{"name":"Medical Department, Bashkir State Medical University, 450000 Ufa, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ravil K.","family":"Tukfatullin","sequence":"additional","affiliation":[{"name":"Medical Department, Bashkir State Medical University, 450000 Ufa, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1071-2680","authenticated-orcid":false,"given":"Nikolay Y.","family":"Nikolenko","sequence":"additional","affiliation":[{"name":"The Moscow Research and Clinical Center for Tuberculosis Control of the Moscow Government Department of Health, 127006 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8986-5953","authenticated-orcid":false,"given":"Anton","family":"Mikhalev","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Center, Siberian Federal University, 660041 Krasnoyarsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5829-672X","authenticated-orcid":false,"given":"Andrey A.","family":"Savchenko","sequence":"additional","affiliation":[{"name":"Federal Research Center \u00abKrasnoyarsk Science Center\u00bb of the Siberian Branch of the Russian Academy of Sciences, Scientific Research Institute of Medical Problems of the North, 660036 Krasnoyarsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandr","family":"Borisov","sequence":"additional","affiliation":[{"name":"Federal Research Center \u00abKrasnoyarsk Science Center\u00bb of the Siberian Branch of the Russian Academy of Sciences, Scientific Research Institute of Medical Problems of the North, 660036 Krasnoyarsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1878-4467","authenticated-orcid":false,"given":"Dmitry","family":"Kudlay","sequence":"additional","affiliation":[{"name":"Department of Pharmacology, Institute of Pharmacy, Sechenov University, 119002 Moscow, Russia"},{"name":"NRC Institute of Immunology FMBA of Russia, 115522 Moscow, Russia"},{"name":"Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119991 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2022). 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