Drug repurposing, the identification of new therapeutic applications for existing drugs, has emerged as a pragmatic and cost-efficient strategy to accelerate oncology drug discovery. Faced with rising development costs, protracted timelines, and high attrition rates associated with traditional de novo drug development, repurposing leverages known pharmacokinetics, safety profiles, and manufacturing processes to expedite clinical translation. This review synthesizes current advances in computational and experimental methodologies and mechanistic insights that drive drug repurposing for cancer therapy. In silico strategies, including molecular docking, machine learning, transcriptomic-proteomic signature reversal, and network-based modeling, have enabled rapid identification of repurposable agents by mining multi-omics and historical pharmacological data. Experimental pipelines spanning high-throughput screening, phenotypic assays, biochemical validations, and functional animal models remain essential to establish efficacy and delineate mechanisms of action. Notably, repurposed drugs exhibit anticancer activity by modulating key pathways, including phosphatidylinositol 3-kinase/Ak strain transforming/mechanistic target of rapamycin, mitogen-activated protein kinase/extracellular signal-regulated kinase, wingless-related integration site/β-catenin signaling, as well as redox homeostasis and DNA response. Despite their promise, repurposed candidates face barriers including limited intellectual property protections, dose optimization challenges, and regulatory uncertainty. Moreover, clinical translation is often hindered by insufficient mechanistic understanding and a lack of predictive biomarkers. Integration of multi-omics datasets, explainable artificial intelligence, patient-derived organoids, and clustered regularly interspaced short palindromic repeats-based genetic screens now offers unprecedented precision in identifying context-specific drug effects and synthetic lethal interactions. With cancer causing one in six global deaths and marked by therapeutic resistance and molecular heterogeneity, drug repurposing provides a scalable solution. This approach bridged preclinical insight with clinical application, potentially transforming cancer therapeutics through rational, data-driven innovation.

Adaptive fusion of EEG and NIRS with explainable AI reveals neurophysiological markers of cognitive flexibility.

This study presents an integrated Water-Energy-Food-Ecosystem (WEFE) engineering education framework that combines risk-aware optimization, machine learning surrogates, explainable artificial intelligence (XAI), digital twin, and virtual reality (VR). A synthetic WEFE scenario library of 10,200 operating conditions and 20,000 intervention policies was generated to support solver-based labeling, XAI training, and immersive VR labs without using real learner data. Benchmark experiments show that the optimization core reduces aggregate water and food deficits by 25–45% while lowering WEFE loss tail risk (CVaR) by 15–30% across baseline, stress, and extreme regimes. Surrogate policies achieve 86–92% fidelity to the optimizer, with objective-performance gaps below 5%, enabling real-time VR interaction at frame rates above 60 fps for more than 500 concurrent “what-if” evaluations per session. The framework is instantiated across 6 program streams at the Hijjawi Faculty for Engineering Technology, with 12–16 contact hours per course allocated to WEFE-XR labs aligned with ABET-related outcomes in systems design, sustainability, data-driven decision making, and ethical responsibility. Analytics from more than 1,000 synthetic scenario playbacks indicate consistent selection of risk-aware strategies, with simulated policy trajectories showing, on average, 18–25% lower tail risk than naive baselines. By quantitatively evaluating the technical performance of each layer, the study shows that integrated WEFE technologies can be embedded in engineering curricula as a reproducible and measurable instructional infrastructure rather than as a standalone enrichment tool.

Enterprise efficiency analysis based on explainable artificial intelligence: From predictive algorithms to mechanisms

Addictive behaviors, including both substance use disorders and behavioral addictions, arise from complex interactions among biological, psychological, social, and environmental factors including digital ones. This review focuses on the assessment of social and psychological risk and protective factors, highlighting how artificial intelligence and machine learning approaches complement conventional qualitative and quantitative methodologies. The aim is to clarify how these tools can enhance understanding, prediction, and prevention of addictive behaviors. Recent research identifies impulsivity, emotion dysregulation, peer norms, and family functioning as central psychosocial risk factors for addictive behaviors. Protective factors - such as self-efficacy, social support, and family cohesion - moderate these risks. Conventional analyses provide foundational evidence, while ML methods (predictive machine learning, explainable artificial intelligence, reinforcement learning) now enable integration of multimodal data, detection of nonlinear patterns, and identification of latent psychosocial profiles. Emerging studies demonstrate potential for early-warning prediction and personalized intervention design. AI/ML offers unprecedented opportunities to advance addiction science by handling high-dimensional psychosocial and behavioral data. Yet, ethical, interpretative, and causal challenges persist. The most promising path forward lies in synergizing theory-driven analytics with data-driven AI approaches to achieve more precise and contextually grounded prevention and intervention strategies for addictive behaviors.

Engineering synthetic biology sensors with artificial intelligence: From programmable circuits to next-generation biosensing.

Challenges in translating AI-driven ASD/ADHD diagnosis: A methodological systematic review.

Leveraging machine learning algorithms and explainable AI for predicting mental health disorder treatment at the workplace.

Lining surface defects adversely affect boiler operation and efficiency, and pose safety risks. The existing crack detection methods for refractory materials lack intelligence and quantitative precision. To address these limitations, well-designed crack detection methods are required for defect identification and maintenance. An explainable AI (XAI) model was developed to detect lining surface defects in a 700 MW circulating fluidized bed (CFB) boiler in Yunnan, China. The hybrid model combines deep learning (i.e., faster region-based convolutional neural network, FRCNN) and image processing to automatically identify and classify defects. To enhance explainability, FRCNN and Vision Transformer (ViT) are used to highlight the contribution of each feature to defect identification. The experimental results show that the hybrid model has high precision and recall rate in defect detection. A relative improvement of 28.01% in overall detection performance is achieved by the proposed ViT-FRCNN hybrid model, in comparison with the single detection method FRCNN. Meanwhile, an overall detection precision of over 95% is attained by the same hybrid model. This work supports CFB boiler operation and maintenance and offers innovative defect detection approaches for related fields. Future work will focus on lightweight Transformer adaptations to enhance computational efficiency for real-time industrial deployment. • Innovative XAI model for detecting surface defects in 700 MW CFB boilers. • Integration of FRCNN and ViT for enhanced defect identification. • Improved detection efficiency by 28.01% and accuracy over 95%. • Provides visual explanations for defect identification results. • Offers new ideas for defect detection in related industrial fields.

Accurate determination of rice protein is essential for quality control. This study evaluated machine learning models (partial least squares regression, PLSR; support vector machine, SVM) combined with feature selection algorithms (random frog, RF; competitive adaptive reweighted sampling, CARS; Monte-Carlo uninformative wavelength elimination, MCUVE) and explainable artificial intelligence method (SHapley Additive exPlanations, SHAP) analysis to predict protein content. Results indicated that CARS-PLSR achieved the highest accuracy (RMSEP=0.266, R2P=0.976, residual prediction deviation [RPD]=6.612). Statistical analysis (F-test and t-test) verified the model's reliability. Furthermore, SHAP analysis revealed that wavelengths at 1218, 1688, and 1209nm made the highest contributions, corresponding to N-H and C-H vibrations. This study not only provides a rapid detection method but also elucidates the chemical basis of the model's high predictive performance. PRACTICAL APPLICATIONS: This study provides a high-speed, non-destructive method for accurately measuring rice protein content, allowing food processors to monitor grain quality in real-time. By using "explainable" AI to reveal the specific chemical markers driving the results, this technology offers a transparent and reliable alternative to slow, expensive laboratory testing.

Revealing the spatiotemporal evolution of landscape ecological resilience in China's Yellow River Basin using remote sensing and explainable AI

Unveiling varietal specificity in non-destructive grape quality monitoring: Explainable AI and feature selection for sugar and organic acid prediction using NIR spectroscopy.

Key predictive factors of breast cancer based on race using machine learning models.

Adaptive reinforcement learning for energy-efficient high-recovery closed-circuit reverse osmosis.

Cancer remains a leading cause of death globally, with nearly 10 million deaths in 2020. Advances in genomic technologies have revolutionized cancer research, shifting focus towards precision medicine based on comprehensive tumour genomic profiling. Concurrently, deep learning (DL) has emerged as a powerful paradigm for complex biological data. This review critically assesses recent advances in DL applications for tumour genomics, emphasizing four key domains: DNA sequencing analysis for mutation detection, gene expression profiling for cancer subtype classification, methylation function prediction for epigenetic characterization and integrative multi-omics approaches for comprehensive tumour profiling. We systematically analyse how different DL architectures-including convolutional neural networks, recurrent neural networks, graph neural networks, autoencoders and transformers-address specific challenges in cancer genomics. Our review highlights how these approaches significantly enhance detection sensitivity for genomic alterations, improve cancer subtype stratification, identify novel biomarkers and optimize therapeutic target selection. We examine technical challenges in DL implementation, including model interpretability, data scarcity, computational requirements and integration issues, alongside emerging solutions such as explainable AI, federated learning, and multi-modal frameworks. By synthesizing methodological innovations and identifying research directions, this review provides bioinformaticians and cancer researchers with a roadmap for leveraging DL to advance precision oncology.

Explainable artificial intelligence for enhancing system understanding and interpretability of numerical crash simulations

Effective project risk management (PRM) necessitates accurate prediction and actionable insights. Machine learning (ML) models improve risk assessment by uncovering complex patterns; however, their black-box nature limits interpretability, making it difficult for stakeholders to trust predictions. Traditional explainable artificial intelligence (XAI) methods highlight influential risk factors yet often fail to provide actionable recommendations, focusing on model behavior rather than practical interventions. Counterfactual explanations (CEs) aim to bridge this gap by suggesting modifications to risk factors or project conditions that could alter outcomes. However, existing CE methods in PRM often lack domain specificity, overlook interdependencies, and ignore temporal constraints, producing recommendations that are unrealistic or infeasible. To address these limitations, we propose Counterfactual Reasoning with Risk Temporal Knowledge Graph (CR-RTKG), a framework that integrates counterfactual reasoning with a Risk Temporal Knowledge Graph (RTKG) to improve interpretability and actionability of risk mitigation. The RTKG encodes domain knowledge, models causal dependencies and cascading effects, and classifies risks by temporal horizon, supporting prioritization based on urgency and systemic influence. By embedding stakeholder-defined constraints into a multi-objective optimization process, CR-RTKG generates context-sensitive and feasible counterfactuals. Unlike conventional methods, it aligns recommendations with real-world project constraints. Experimental results show that CR-RTKG achieves higher plausibility (96%) and feasibility (93%), outperforming baselines including Diverse Counterfactual Explanations (DiCE) and Flow-based Counterfactual Explanation (CeFlow).

Goal-oriented explainable artificial intelligence for sustainable viticulture: Ideal chemical profiles with C-VAE and AIME

Towards interpretable AI in personalized medicine through a radiological-biological radiomics dictionary linking semantic Lung-RADS and imaging radiomics features.

Applying explainable artificial intelligence and ensemble boosting based machine learning models in predicting the thermal conductivity of nano enhanced phase change materials