{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T04:59:24Z","timestamp":1790830764657,"version":"4.1.0"},"reference-count":59,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mol. Sci.","IJMS"],"abstract":"<jats:p>Post-traumatic stress disorder (PTSD) is associated with immune and autonomic disturbances, but molecular programs linking PTSD-related blood transcriptional signals with autonomic\u2013cardiac biology remain incompletely characterized. The public Gene Expression Omnibus (GEO) cohorts analyzed here did not directly measure palpitations or autonomic dysfunction. Peripheral-blood transcriptomes from GSE81761 and GSE63878 were integrated with a prespecified pathway-derived autonomic\u2013cardiac gene set constructed from Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Genes overlapping the curated pathway set were assessed using functional enrichment and protein\u2013protein interaction (PPI) analyses. Multi-algorithm comparison and a separate random-forest\/SHapley Additive exPlanations (SHAP) analysis were performed for internal model assessment and biological prioritization. GSE64813 and GSE97356 were analyzed using targeted single-gene analyses and cohort-specific multivariable logistic models. Model performance was examined using repeated nested cross-validation, a fully nested sensitivity analysis, and locked-transfer testing to GSE64813 and GSE97356. Interleukin 15 (IL15) and protein phosphatase 2 scaffold subunit Aalpha (PPP2R1A) were evaluated using external-cohort analyses, immune-cell deconvolution, and descriptive postmortem hippocampal single-nucleus data. Among 1679 nominally significant PTSD-associated candidate genes, 92 overlapped the curated pathway set and were enriched for cytokine signaling, chemotaxis, apoptosis, calcium transport, and PP2A-related functions. PPI analysis yielded 37 recurrent candidate hub genes. The original model comparison ranked support-vector machine (SVM) the highest across the merged and source-cohort summaries [mean area under the receiver-operating-characteristic curve (AUC) 0.853], but these estimates represent internal discovery-stage performance. IL15 was recurrently prioritized by network-based analyses, whereas PPP2R1A was a component of an enriched phosphatase-related module. IL15 and PPP2R1A showed modest single-gene effects in GSE64813 (AUC 0.566 and 0.612) and GSE97356 (AUC 0.558 and 0.593). Cohort-specific refitted models showed apparent AUC values of 0.834 and 0.749, respectively. Repeated nested cross-validation conditional on the preselected 29-gene feature set identified L2-regularized logistic regression as the best-performing algorithm (mean AUC = 0.690). Importantly, a more stringent fully nested sensitivity analysis, in which differential-expression screening and pathway intersection were repeated within each outer training fold, yielded a mean repeated outer-cross-validated AUC of 0.593 [standard deviation (SD) = 0.033; range, 0.541\u20130.625], indicating limited predictive performance. Locked-transfer AUCs were 0.664 in GSE64813 and 0.534 in GSE97356. Single-nucleus summaries suggested donor- and nucleus-type-dependent expression patterns for IL15 and PPP2R1A. These findings identify IL15-related cytokine signaling and PPP2R1A-related phosphatase regulation as candidate molecular programs linking PTSD-associated transcriptional variation with pathway-derived autonomic\u2013cardiac biology. The results should therefore be interpreted as hypothesis-generating rather than direct molecular evidence for unmeasured palpitation symptoms. Prospective validation in independent, clinically well-characterized cohorts with standardized autonomic and cardiac phenotyping is warranted.<\/jats:p>","DOI":"10.3390\/ijms27198710","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T12:09:25Z","timestamp":1790683765000},"page":"8710","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrative Transcriptomics and Machine Learning Nominate IL15- and PPP2R1A-Centered Programs in PTSD Using Pathway-Informed Autonomic\u2013Cardiac Gene Prioritization"],"prefix":"10.3390","volume":"27","author":[{"given":"Yihan","family":"Guo","sequence":"first","affiliation":[{"name":"School of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China"},{"name":"Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 201108, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Dongdong","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China"},{"name":"Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 201108, China"},{"name":"Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Lanying","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China"},{"name":"Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 201108, China"},{"name":"Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4319-5314","authenticated-orcid":false,"given":"Zhen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China"},{"name":"Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 201108, China"},{"name":"Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1038\/s41582-022-00635-8","article-title":"Post-traumatic stress disorder: Clinical and translational neuroscience from cells to circuits","volume":"18","author":"Ressler","year":"2022","journal-title":"Nat. Rev. Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15057","DOI":"10.1038\/nrdp.2015.57","article-title":"Post-traumatic stress disorder","volume":"1","author":"Yehuda","year":"2015","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1037\/hea0001127","article-title":"Post-traumatic stress disorder (PTSD) as a systemic disorder: Pathways to cardiovascular disease","volume":"41","author":"Krantz","year":"2022","journal-title":"Health Psychol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1017\/S003329172000207X","article-title":"Autonomic dysfunction in posttraumatic stress disorder indexed by heart rate variability: A meta-analysis","volume":"50","author":"Schneider","year":"2020","journal-title":"Psychol. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9","DOI":"10.30773\/pi.2019.0112","article-title":"Posttraumatic Stress Disorder and Alterations in Resting Heart Rate Variability: A Systematic Review and Meta-Analysis","volume":"17","author":"Ge","year":"2020","journal-title":"Psychiatry Investig."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Roer, G.E., Lien, L., Bolstad, I., Aaseth, J.O., and Abebe, D.S. (2023). The impact of PTSD on risk of cardiometabolic diseases: A national patient cohort study in Norway. BMC Psychiatry, 23.","DOI":"10.1186\/s12888-023-04866-x"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e011040","DOI":"10.1161\/CIRCOUTCOMES.124.011040","article-title":"Posttraumatic Stress Disorder and the Risk of Heart Failure Hospitalizations Among Individuals with Coronary Artery Disease","volume":"17","author":"Almuwaqqat","year":"2024","journal-title":"Circ. Cardiovasc Qual. Outcomes"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3150","DOI":"10.1038\/s41380-022-01564-0","article-title":"Inflammatory and oxidative stress markers in post-traumatic stress disorder: A systematic review and meta-analysis","volume":"27","author":"Peruzzolo","year":"2022","journal-title":"Mol. Psychiatry"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1038\/npp.2017.220","article-title":"PTSD Blood Transcriptome Mega-Analysis: Shared Inflammatory Pathways across Biological Sex and Modes of Trauma","volume":"43","author":"Breen","year":"2018","journal-title":"Neuropsychopharmacology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3337","DOI":"10.1038\/s41380-019-0496-z","article-title":"Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder","volume":"25","author":"Dean","year":"2020","journal-title":"Mol. Psychiatry"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1038\/s41398-021-01431-6","article-title":"Mapping the transcriptomics landscape of post-traumatic stress disorder symptom dimensions in World Trade Center responders","volume":"11","author":"Kuan","year":"2021","journal-title":"Transl. Psychiatry"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3077","DOI":"10.1038\/s41380-021-01084-3","article-title":"Integration of peripheral transcriptomics, genomics, and interactomics following trauma identifies causal genes for symptoms of post-traumatic stress and major depression","volume":"26","author":"Wuchty","year":"2021","journal-title":"Mol. Psychiatry"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"eadh3707","DOI":"10.1126\/science.adh3707","article-title":"Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood","volume":"384","author":"Daskalakis","year":"2024","journal-title":"Science"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41593-020-00748-7","article-title":"Transcriptomic organization of the human brain in post-traumatic stress disorder","volume":"24","author":"Girgenti","year":"2021","journal-title":"Nat. Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1038\/s41380-022-01488-9","article-title":"Genetically regulated multi-omics study for symptom clusters of posttraumatic stress disorder highlights pleiotropy with hematologic and cardio-metabolic traits","volume":"27","author":"Pathak","year":"2022","journal-title":"Mol. Psychiatry"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3075","DOI":"10.1038\/s41380-022-01544-4","article-title":"Integrating human brain proteomes with genome-wide association data implicates novel proteins in post-traumatic stress disorder","volume":"27","author":"Wingo","year":"2022","journal-title":"Mol. Psychiatry"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4558","DOI":"10.1038\/s41467-019-12576-w","article-title":"International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci","volume":"10","author":"Nievergelt","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s13073-025-01473-1","article-title":"A multi-omic approach implicates novel protein dysregulation in post-traumatic stress disorder","volume":"17","author":"Wang","year":"2025","journal-title":"Genome Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5631","DOI":"10.1038\/s41467-025-60487-w","article-title":"Shared genetic architecture of posttraumatic stress disorder with cardiovascular imaging, risk, and diagnoses","volume":"16","author":"Shen","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111705","DOI":"10.1016\/j.cellsig.2025.111705","article-title":"Identification of potential biomarkers in cardiovascular calcification based on bioinformatics combined with single-cell RNA-seq and multiple machine learning analysis","volume":"131","author":"Guo","year":"2025","journal-title":"Cell Signal"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"q902","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e082505","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: An updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","year":"2025","journal-title":"BMJ"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e11","DOI":"10.4110\/in.2024.24.e11","article-title":"IL-15 in T-Cell Responses and Immunopathogenesis","volume":"24","author":"Lee","year":"2024","journal-title":"Immune Netw."},{"key":"ref_24","first-page":"E396","article-title":"Astrocyte-derived interleukin-15 exacerbates ischemic brain injury via propagation of cellular immunity","volume":"114","author":"Li","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"110213","DOI":"10.1016\/j.cellsig.2021.110213","article-title":"Protein phosphatase 2A in the healthy and failing heart: New insights and therapeutic opportunities","volume":"91","author":"Sergienko","year":"2022","journal-title":"Cell Signal"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e185602","DOI":"10.1172\/JCI185602","article-title":"Ppp2r1a haploinsufficiency increases excitatory synaptic transmission and decreases spatial learning by impairing endocannabinoid signaling","volume":"135","author":"Wang","year":"2025","journal-title":"J. Clin. Investig."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1038\/s41392-025-02317-5","article-title":"Astrocytic gamma-aminobutyric acid dysregulation as a therapeutic target for posttraumatic stress disorder","volume":"10","author":"Yoon","year":"2025","journal-title":"Signal Transduct. Target Ther."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, L., Li, P., Wilkerson, B.A., Wu, Y., Liu, M., Shi, R., Hamlett, E.D., Carroll, S.L., LaRue, A.C., and Wang, Z. (2026). Immune imbalance in the human hippocampus in PTSD revealed by single-nucleus transcriptomics. Front. Immunol., 16.","DOI":"10.3389\/fimmu.2025.1697171"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5692","DOI":"10.1038\/s41467-021-25960-2","article-title":"Confronting false discoveries in single-cell differential expression","volume":"12","author":"Squair","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1038\/s41576-023-00586-w","article-title":"Best practices for single-cell analysis across modalities","volume":"24","author":"Heumos","year":"2023","journal-title":"Nat. Rev. Genet."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5550","DOI":"10.1038\/s41380-026-03639-8","article-title":"Dynamic immune and metabolic dysregulation in women with post-traumatic stress disorder: Longitudinal transcriptomic insights following sexual assault","volume":"31","author":"Bugiga","year":"2026","journal-title":"Mol. Psychiatry"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"115881","DOI":"10.1016\/j.bbr.2025.115881","article-title":"Aberrations in peripheral blood interleukin-6 levels in post-traumatic stress disorder: A systematic review and meta-analysis","volume":"497","author":"Yang","year":"2026","journal-title":"Behav. Brain Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Al Nakhebi, O.A.S., Albu-Kalinovic, R., Neda-Stepan, O., Giurgi-Oncu, C., Cri\u0219an, C.A., Enatescu, V.R., and Marinescu, I. (2025). Post-Traumatic Stress Disorder (PTSD) and Cardiovascular Diseases: A Systematic Review and Meta-Analysis. J. Clin. Med., 14.","DOI":"10.3390\/jcm14227979"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sundararaj, S., Gazi, A.H., Vaccarino, V., Shah, A.J., Inan, O.T., and Bremner, J.D. (2025). Accrued reductions in heart rate following transcutaneous vagal nerve stimulation in adults with posttraumatic stress disorder. Front. Neurosci., 19.","DOI":"10.3389\/fnins.2025.1456662"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Casmey, K., Zimmermann, M., Xie, Y., Codeluppi-Arrowsmith, S.A., and Turecki, G. (2025). A Single-Cell Omics Technical Guide for Advancing Neuropsychiatric Research. Genes, 16.","DOI":"10.3390\/genes16121394"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7792","DOI":"10.1038\/s41467-026-74757-8","article-title":"Integrated proteomic and metabolomic analyses implicate redox-metabolic pathways in PTSD-associated multisystem disease and accelerated aging","volume":"17","author":"Kuan","year":"2026","journal-title":"Nat. Commun."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"D545","DOI":"10.1093\/nar\/gkaa970","article-title":"KEGG: Integrating viruses and cellular organisms","volume":"49","author":"Kanehisa","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"D991","DOI":"10.1093\/nar\/gks1193","article-title":"NCBI GEO: Archive for functional genomics data sets\u2014Update","volume":"41","author":"Barrett","year":"2013","journal-title":"Nucleic Acids Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1093\/biostatistics\/kxj037","article-title":"Adjusting batch effects in microarray expression data using empirical Bayes methods","volume":"8","author":"Johnson","year":"2007","journal-title":"Biostatistics"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1093\/bioinformatics\/bts034","article-title":"The sva package for removing batch effects and other unwanted variation in high-throughput experiments","volume":"28","author":"Leek","year":"2012","journal-title":"Bioinformatics"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e47","DOI":"10.1093\/nar\/gkv007","article-title":"Limma powers differential expression analyses for RNA-sequencing and microarray studies","volume":"43","author":"Ritchie","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. Ser. B Methodol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/s41569-024-01024-y","article-title":"Stress and cardiovascular disease: An update","volume":"21","author":"Vaccarino","year":"2024","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"iyad031","DOI":"10.1093\/genetics\/iyad031","article-title":"The Gene Ontology knowledgebase in 2023","volume":"224","author":"Aleksander","year":"2023","journal-title":"Genetics"},{"key":"ref_45","first-page":"100141","article-title":"clusterProfiler 4.0: A universal enrichment tool for interpreting omics data","volume":"2","author":"Wu","year":"2021","journal-title":"Innovation"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2498","DOI":"10.1101\/gr.1239303","article-title":"Cytoscape: A software environment for integrated models of biomolecular interaction networks","volume":"13","author":"Shannon","year":"2003","journal-title":"Genome Res."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chin, C.H., Chen, S.H., Wu, H.H., Ho, C.W., Ko, M.T., and Lin, C.Y. (2014). cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol., 8.","DOI":"10.1186\/1752-0509-8-S4-S11"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization Paths for Generalized Linear Models via Coordinate Descent","volume":"33","author":"Friedman","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.C., and M\u00fcller, M. (2011). pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform., 12.","DOI":"10.1186\/1471-2105-12-77"},{"key":"ref_53","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Van Calster, B., McLernon, D., van Smeden, M., Wynants, L., Steyerberg, E., and Topic Group \u2018Evaluating diagnostic tests and prediction models\u2019 of the STRATOS initiative (2019). Calibration: The Achilles heel of predictive analytics. BMC Med., 17.","DOI":"10.1186\/s12916-019-1466-7"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: A novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med. Decis. Mak."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1016\/j.cell.2021.04.048","article-title":"Integrated analysis of multimodal single-cell data","volume":"184","author":"Hao","year":"2021","journal-title":"Cell"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1038\/nmeth.3337","article-title":"Robust enumeration of cell subsets from tissue expression profiles","volume":"12","author":"Newman","year":"2015","journal-title":"Nat. Methods"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"W214","DOI":"10.1093\/nar\/gkq537","article-title":"The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function","volume":"38","author":"Donaldson","year":"2010","journal-title":"Nucleic Acids Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"D1257","DOI":"10.1093\/nar\/gkac833","article-title":"Comparative Toxicogenomics Database (CTD): Update 2023","volume":"51","author":"Davis","year":"2023","journal-title":"Nucleic Acids Res."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/19\/8710\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T04:37:02Z","timestamp":1790829422000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/19\/8710"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,29]]},"references-count":59,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["ijms27198710"],"URL":"https:\/\/doi.org\/10.3390\/ijms27198710","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,29]]}}}