Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer remains a clinically important endpoint, but accurate prediction before treatment is challenging. We developed an attention-based multi-omics framework that integrates pretreatment genomics, transcriptomics, proteomics, epigenomics, and clinical variables to predict pCR in early-stage breast cancer. The model was trained on the I-SPY2 neoadjuvant cohort and externally evaluated using The Cancer Genome Atlas Breast Cancer and independent NAC datasets. Performance was assessed using discrimination, calibration, and subtype-specific analyses, while explainability was examined using SHAP-based feature importance and pathway enrichment testing. In the I-SPY2 test set, the multi-omics model achieved an area under the receiver operating characteristic curve of 0.81 and outperformed clinical-only and single-omics baselines across subtypes. Improvements were most apparent in triple-negative and HER2-positive disease. The model showed acceptable calibration and maintained performance in external and transfer analyses, in which higher predicted risk scores were associated with poorer recurrence-related outcomes. Explainability analyses identified proliferation, immune activity, and PI3K/AKT signaling as major contributors to prediction. These findings indicate that integrating pretreatment multi-omics data with clinical variables improves prediction of NAC response while producing interpretable outputs. Further prospective validation is required before clinical application.
J. Fakoya, Catherine Falayi, M. Ajinaja· Cureus Journal of Computer S...· 0 citations
Hantavirus infection remains a rare but potentially fatal zoonosis, and early identification of patients at highest risk of death is essential for timely triage and resource allocation. This study developed an interpretable machine learning (ML) framework for mortality risk stratification using a global hantavirus epidemiology dataset. The dataset included both Hemorrhagic Fever with Renal Syndrome (HFRS) and Hantavirus Cardiopulmonary Syndrome (HCPS) cases. The study conducted a retrospective supervised learning analysis on 10,000 patient records, including demographic, clinical, epidemiological, and treatment variables. The binary outcome was mortality. Preprocessing included identifier removal, missing-value handling, categorical and symptom encoding, feature selection, and class-imbalance correction. Logistic regression, random forest, and extreme gradient boosting (XGBoost) models were trained and compared on a held-out test set using receiver operating characteristic-area under the curve (ROC-AUC), accuracy, precision, recall, F1-score, and Brier score. Model interpretation was planned using SHapley Additive exPlanations-based feature attribution. The cohort included 8,938 recovered and 1,062 deceased cases (mortality rate: 10.62%), comprising 6,460 HFRS cases (64.6%) and 3,540 HCPS cases (35.4%). Logistic regression achieved the highest discrimination, with an ROC-AUC of 0.858 and the highest recall for mortality detection (0.741), but modest precision (0.321) and weaker calibration (Brier score 0.119). Gradient boosting showed comparable discrimination (AUC 0.857) with better precision (0.511) and calibration (Brier score 0.076). Random forest performed less well for mortality detection, with markedly low recall (0.085). The study trained and compared interpretable classifiers using 10,000 global hantavirus cases representing both HFRS (64.6%) and HCPS (35.4%) presentations, and reported pooled performance as well as syndrome-stratified results. Severity, syndrome type, viral load category, age, and geographic setting were the most informative predictors. Mortality risk in hantavirus infection can be modeled using routine clinical and epidemiological features, but clinical deployment should prioritize sensitivity, calibration, and transparency over accuracy alone. These findings support the use of interpretable ML as a practical framework for early hantavirus risk stratification and external validation in independent cohorts.
Yetunde Enigbokan, A. Abiona, M. Ajinaja· Cureus Journal of Computer S...· 0 citations