Possible drug–food constituent interactions (DFIs) could change the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions leads to the rise of drug–drug interactions (DDIs) and DFIs. These adverse interactions lead to other implications, e.g., the decline in medicament’s effect, the withdrawals of various medications, and harmful impacts on the patients’ health. However, the importance of DFIs remains underestimated, as the number of studies on these topics is constrained. Recently, scientists have applied artificial intelligence-based models to study DFIs. However, there were still some limitations in data mining, input, and detailed annotations. This study proposed a novel prediction model to address the limitations of previous studies. In detail, we extracted 70,477 food compounds from the FooDB database and 13,580 drugs from the DrugBank database. We extracted 3780 features from each drug–food compound pair. The optimal model was eXtreme Gradient Boosting (XGBoost). We also validated the performance of our model on one external test set from a previous study which contained 1922 DFIs. Finally, we applied our model to recommend whether a drug should or should not be taken with some food compounds based on their interactions. The model can provide highly accurate and clinically relevant recommendations, especially for DFIs that may cause severe adverse events and even death. Our proposed model can contribute to developing more robust predictive models to help patients, under the supervision and consultants of physicians, avoid DFI adverse effects in combining drugs and foods for therapy.

The tumor immune microenvironment (TME) influences cancer progression and treatment. RNA-Seq has identified six immune subtypes: Wound Healing (WH, C1), IFN- Dominant (IFNG, C2), Inflammatory (INF, C3), Lymphocyte Depleted (LD, C4), Immunologically Quiet (IQ, C5), and TGF-β Dominant (TGFb, C6). This study uses a Convolutional Neural Network (CNN) to classify these subtypes from RNA-Seq data. The model, with ReLU activation and dropout, achieved a 10-fold F1-score of 0.9483 and AUC of 0.9969. Results show CNN’s effectiveness in handling class imbalance and modeling complex gene interactions, outperforming XGBoost, Random Forest, and TabNet. Future work will validate results on independent datasets and incorporate multi-omics data to improve accuracy.

This study aims to employ computational methods for the accurate identification of vesicular transport proteins. The identification of these proteins holds great significance in enhancing our understanding of their protein family structure, thereby enabling the design of more effective drug targets for individuals afflicted with endocrine disorders. In recent times, researchers in the field of biology have increasingly sought to leverage deep learning techniques to address this challenge. In order to further enhance the classification performance, we investigated the following models incorporating distinct features: (1) We devised a novel protein feature called AAC_PSSM by amalgamating amino acid composition (AAC) and position-specific scoring matrix (PSSM) features. Subsequently, a gated recurrent unit (GRU) model was employed to learn such features; (2) An ensemble model was constructed by combining the existing GRU model with the model of a neural network featuring the AAC feature; (3) Random forest analysis was conducted using the pseudo-amino acid composition (PseAAC) feature; (4) Furthermore, we explored a natural language processing (NLP) approach by considering the protein sequence as a natural language and applying various neural network architectures. Upon analyzing the results obtained from the different models, it was observed that the ensemble model incorporating PSSM and AAC features exhibited the highest sensitivity of 81.03% and accuracy of 82.43%. Notably, our proposed model surpassed the performance of state-of-the-art models addressing the same problem and datasets, thus establishing its superiority.

Knee injury emerges as one of the most common diseases, causing dislocation of knee joints, immobility, etc., in which the anterior cruciate ligament (ACL) injury is the most common one. The development of various artificial intelligence (AI) frameworks gained enormous attention in many areas, including injury prediction and health management via medical image analysis. The objective of the current study is to focus on a comprehensive high accurate prediction of ACL injury based on MRI medical images, and also demonstrate the ability of AI in practical and outline conceptual prediction and diagnosis frameworks for other types of knee injuries in the future. Our dataset comprised of knee MRI reports from Cho Ray Hospital, Vietnam which are composed of ACL and non-ACL injury patients. The MRI images were used as supporting data in the deep learning classification model with DenseNet-121 algorithm. The successful establishment of an ACL injury diagnosis model from MRI will pave the way for us to develop more diagnostic models of other injuries in the body as well as the prediction of bone diseases.

Objectives: Machine learning can predict severe tooth loss (STL, ≥6 missing teeth), but opaque black-box models neglecting complex survey designs limit clinical adoption. This study developed and externally validated an intrinsically interpretable, survey-weighted framework for population-level STL prediction, capturing complex socio-behavioral and systemic health determinants. Methods: We analyzed nationally representative data from BRFSS 2022 (derivation, N=433,772), BRFSS 2024 (temporal validation, N=448,213), and the clinically examined NHANES 2015-2018 (cross-domain validation, N=10,775). Missing data were resolved using an anti-leakage HistGradientBoosting MICE pipeline, preserving multivariate epidemiological variance. An Explainable Boosting Machine (EBM, GA ² M) was natively trained by integrating complex survey weights. For external clinical validation, structural domain shift was addressed through non-parametric Isotonic Regression recalibration. Results: The EBM achieved strong temporal stability on BRFSS 2024 (AUC: 0.8627; Brier Score: 0.0845). Upon cross-domain validation against NHANES 2015-2018, the calibrated model demonstrated robust transportability (AUC: 0.7504; Brier Score: 0.1358). Notably, the zero-shot EBM (AUC: 0.7591) closely matched the predictive ceiling of a black-box stacked meta-ensemble (AUC: 0.7706), eliminating the need for unstable post-hoc approximations. Fully auditable shape functions explicitly revealed non-linear risk thresholds and synergistic pairwise interactions for key predictors including age, smoking, income, and diabetes. Decision curve analysis confirmed substantial positive net clinical benefit across a 5%–50% risk threshold continuum. Conclusions: The MICE-EBM framework predicts STL with complete intrinsic transparency and robust probabilistic reliability. By successfully generalizing across unobserved temporal and clinical cohorts, this TRIPOD+AI compliant framework provides a clinically deployable tool to optimize targeted dental public health interventions.