Search Results (583)

Background/Objectives: The mTOR serine/threonine kinase coordinates protein translation, cell growth, and metabolism, and its dysregulation promotes tumorigenesis. We present a reproducible, pan-cancer, network-aware framework that integrates curated resources with genomics to move beyond pathway curation, yielding falsifiable hypotheses and prioritized candidates for mTOR axis biomarker validation. Materials and Methods: We assembled MTOR-related genes and interactions from GeneCards, KEGG, STRING, UniProt, and PathCards and harmonized identifiers. We formulated a concise working model linking genotype → pathway architecture (mTORC1/2) → expression-level rewiring → phenotype. Three analyses operationalized this model: (i) pan-cancer alteration mapping to separate widely shared drivers from tumor-specific nodes; (ii) expression-based activity scoring to quantify translational/nutrient-sensing modules; and (iii) topology-aware network propagation (personalized PageRank/Random Walk with Restart on a high-confidence STRING graph) to nominate functionally proximal neighbors. Reproducibility was supported by degree-normalized diffusion, predefined statistical thresholds, and sensitivity analyses. Results: Gene ontology analysis demonstrated significant enrichment for mTOR-related processes (TOR/TORC1 signaling and cellular responses to amino acids). Database synthesis corroborated disease associations involving MTOR and its partners (e.g., TSC2, RICTOR, RPTOR, MLST8, AKT1 across selected carcinomas). Across cohorts, our framework distinguishes broadly shared upstream drivers (PTEN, PIK3CA) from lineage-enriched nodes (e.g., RICTOR-linked components) and prioritizes non-mutated, network-proximal candidates that align with mTOR activity signatures. Conclusions: This study delivers a transparent, pan-cancer framework that unifies curated biology, genomics, and network topology to produce testable predictions about the mTOR axis. By distinguishing shared drivers from tumor-specific nodes and elevating non-mutated, topology-inferred candidates, the approach refines biomarker discovery and suggests architecture-aware therapeutic strategies. The analysis is reproducible and extensible, supporting prospective validation of prioritized candidates and the design of correlative studies that align pathway activity with clinical response. Full article

Network traffic analysis is crucial for understanding network behavior and identifying underlying applications, protocols, and service groups. The increasing complexity of network environments, driven by the evolution of the Internet, poses significant challenges to traditional analytical approaches. Graph Neural Networks (GNNs) have recently garnered considerable attention in network traffic analysis due to their ability to model complex relationships within network flows and between communicating entities. This scoping review systematically surveys major academic databases, employing predefined eligibility criteria to identify and synthesize key research in the field, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology. We present a comprehensive overview of a generalized architecture for GNN-based traffic analysis and categorize recent methods into three primary types: node prediction, edge prediction, and graph prediction. We discuss challenges in network traffic analysis, summarize solutions from various methods, and provide practical recommendations for model selection. This review also compiles publicly available datasets and open-source code, serving as valuable resources for further research. Finally, we outline future research directions to advance this field. This work offers an updated understanding of GNN applications in network traffic analysis and provides practical guidance for researchers and practitioners. Full article

The cherry fruit fly (Rhagoletis cerasi L.) poses a major threat to global cherry production, with significant economic implications. This study presents an innovative approach to assist pest control strategies by classifying cherry fruit samples based on pomological data using evolutionary rule-based classification algorithms. A unique dataset comprising 396 samples from five different coloring periods was collected, focusing particularly on the second pomological period when pest activity peaks. Three evolutionary algorithms, CORE (Evolutionary Rule Extractor for Classification), DMEL (Data Mining with Evolutionary Learning for Classification) and OCEC (Organizational Evolutionary Classification), were applied to find interpretable classification rules that find whether an incoming cherry sample belongs to the second pomological period or other periods. Two distinct fitness functions were used to evaluate the algorithms’ performance. The results of the algorithms are compared with various visual graphs and the metric values are compared with visual graphs in a similar fashion. The findings highlight the potential of explainable AI models in enhancing agricultural decision-making and offer a novel, data-based methodology for integrated pest management in cherry production for the prediction of cherry fruit phenology class. Full article

Background: Bioelectrical Impedance Vector Analysis (BIVA) is a qualitative method that standardizes resistance and reactance relative to stature (R/H and Xc/H) and plots them as vectors on an R-Xc graph. This equation-free approach assesses body composition, allowing for the evaluation of hydration status and cellular integrity through tolerance ellipses. This study aimed to systematically map BIVA reference ellipses across general, pediatric, pathological, and athletic populations. Methods: A scoping review was conducted according to PRISMA-ScR guidelines. Five databases were searched. Extracted data included (a) sample characteristics (sample size, age, sex, BMI, country, ethnicity), (b) population type, (c) analyzer specifications, and (d) R/H and Xc/H means, standard deviations, and correlation values. Results: A total of 53 studies published between 1994 and July 2025 were included. From these, 508 tolerance ellipses were identified: 281 for the general population (18–92 years), 133 for children/adolescents (0–18 years), 49 for athletes, and 45 for pathological groups. Studies were primarily conducted in Europe and the Americas, using 11 analyzers with variations in measurement protocols, including body side, posture, and electrode placement. Conclusions: This scoping review categorizes the existing BIVA tolerance ellipses by population type, sex, age, BMI, device used, and measurement protocol. The structured presentation is intended to guide researchers, clinicians, nutritionists, and sports professionals in selecting appropriate reference ellipses tailored to specific populations and contexts. Full article

Cell–cell interactions play a pivotal role in maintaining tissue homeostasis and driving disease progression. Conventional Cell–cell interactions modeling approaches depend on ligand–receptor databases, which often fail to capture context-specific or newly emerging signaling mechanisms. To address this limitation, we propose a data-driven computational framework, data-driven cell–cell interaction inference (DD-CC-II), which employs a graph-based model using eigen-cells to represent cell groups. DD-CC-II uses eigen-cells (i.e., functional module within the cell population) to characterize cell groups and construct correlation coefficient networks to model between-group associations. Correlation coefficient networks between eigen-cells are constructed, and their statistical significance is evaluated via over-representation analysis and hypergeometric testing. Monte Carlo simulations demonstrate that DD-CC-II achieves superior performance in inferring CCIs compared with ligand–receptor-based methods. The application to whole-blood RNA-seq data from the Japan COVID-19 Task Force revealed severity stage-specific interaction patterns. Markers such as FOS, CXCL8, and HLA-A were associated with high severity, whereas IL1B, CD3D, and CCL5 were related to low severity. The systemic lupus erythematosus pathway emerged as a potential immune mechanism underlying disease severity. Overall, DD-CC-II provides a data-centric approach for mapping the cellular communication landscape, facilitating a better understanding of disease progression at the intercellular level. Full article

The emergence of large language models (LLMs) has redefined the potential of artificial intelligence in clinical domains. In this context, retrieval-augmented generation (RAG) systems provide a promising approach to enhance traceability, timeliness, and accuracy in tasks such as biomedical question answering (QA). This article presents a narrative and thematic review of the evolution of these technologies in maternal health, structured across five axes: technical foundations of RAG, advancements in biomedical LLMs, conversational agents in healthcare, clinical validation frameworks, and specific applications in obstetric telehealth. Through a systematic search in scientific databases covering the period from 2022 to 2025, 148 relevant studies were identified. Notable developments include architectures such as BiomedRAG and MedGraphRAG, which integrate semantic retrieval with controlled generation, achieving up to 18% improvement in accuracy compared to pure generative models. The review also highlights domain-specific models like PMC-LLaMA and Med-PaLM 2, while addressing persistent challenges in bias mitigation, hallucination reduction, and clinical validation. In the maternal care context, the review outlines applications in prenatal monitoring, the automatic generation of clinically validated QA pairs, and low-resource deployment using techniques such as QLoRA. The article concludes with a proposed research agenda emphasizing federated evaluation, participatory co-design with patients and healthcare professionals, and the ethical design of adaptable systems for diverse clinical settings. Full article

Non-coding RNAs (ncRNAs) are involved in many biological processes, making their identification and functional characterization a priority. Among them, long non-coding RNAs (lncRNAs) have been shown to regulate diverse cellular processes, such as cell development, stress response, and transcriptional regulation. The continued identification of new lncRNAs highlights the demand for reliable methods for their detection, with structural analysis offering insightful information. Currently, lncRNAs are identified using tools such as LncFinder, whose database has a large collection of lncRNAs from humans, mice, and chickens, among others. In this work, we present a graph-based algorithm to represent and compare RNA secondary structures. Rooted tree graphs were used to compare two groups of Saccharomyces cerevisiae RNA sequences, lncRNAs and not lncRNAs, by searching for structural similarities between each group. When applied to a novel candidate sequence dataset, the algorithm evaluated whether characteristic structures identified in known lncRNAs recurred. If so, the sequences were classified as likely lncRNAs. These results indicate that graph-based structural analysis offers a complementary methodology for identifying lncRNAs and may complement existing sequence-based tools such as lncFinder or PreLnc. Recent studies have shown that tumor cells can secrete lncRNAs into human biological fluids forming circulating lncRNAs which can be used as biomarkers for cancer. Our algorithm could be applied to identify novel lncRNAs with structural similarities to those associated with tumor malignancy. Full article

Background: Ongoing challenges such as geopolitical conflicts, trade disruptions, economic sanctions, and political instability have underscored the urgent need for large manufacturing enterprises to improve resilience and reduce dependence on global supply chains. Integrating regional and local Small- and Medium-Sized Enterprises (SMEs) has been proposed as a strategic approach to enhance supply chain localization, yet barriers such as limited visibility, qualification hurdles, and integration difficulties persist. Methods: This study proposes a comprehensive knowledge graph driven framework for representing and discovering SMEs, implemented as a proof-of-concept in the U.S. BioPharma sector. The framework constructs a curated knowledge graph in Neo4j, converts it to Resource Description Framework (RDF) format, and aligns it with the Schema.org vocabulary to enable semantic interoperability and enhance the discoverability of SMEs. Results: The developed knowledge graph, consisting of 488 nodes and 11,520 edges, enabled accurate multi-hop SME discovery with query response times under 10 milliseconds. RDF serialization produced 16,086 triples, validated across platforms to confirm interoperability and semantic consistency. Conclusions: The proposed framework provides a scalable, adaptable, and generalizable solution for SME discovery and supply chain localization, offering a practical pathway to strengthen resilience in diverse manufacturing industries. Full article

Background and objectives: Large language models (LLMs) show promise in automating open-ended evaluation tasks, yet their reliability in rubric-based assessment remains uncertain. Variability in scoring, feedback, and rubric adherence raises concerns about transparency and pedagogical validity in educational contexts. This study introduces CourseEvalAI, a framework designed to enhance consistency and fidelity in rubric-guided evaluation by fine-tuning a general-purpose LLM with authentic university-level instructional content. Methods: The framework employs supervised fine-tuning with Low-Rank Adaptation (LoRA) on rubric-annotated answers and explanations drawn from undergraduate computer science exams. Responses generated by both the base and fine-tuned models were independently evaluated by two human raters and two LLM judges, applying dual-layer rubrics for answers (technical or argumentative) and explanations. Inter-rater reliability was reported as intraclass correlation coefficient (ICC(2,1)), Krippendorff’s α, and quadratic-weighted Cohen’s κ (QWK), and statistical analyses included Welch’s t tests with Holm–Bonferroni correction, Hedges’ g with bootstrap confidence intervals, and Levene’s tests. All responses, scores, feedback, and metadata were stored in a Neo4j graph database for structured exploration. Results: The fine-tuned model consistently outperformed the base version across all rubric dimensions, achieving higher scores for both answers and explanations. After multiple-testing correction, only the Generative Pre-trained Transformer (GPT-4)—judged Technical Answer contrast remains statistically significant; other contrasts show positive trends without passing the adjusted threshold, and no additional significance is claimed for explanation-level results. Variance in scoring decreased, inter-model agreement increased, and evaluator feedback for fine-tuned outputs contained fewer vague or critical remarks, indicating stronger rubric alignment and greater pedagogical coherence. Inter-rater reliability analyses indicated moderate human–human agreement and weaker alignment of LLM judges to the human mean. Originality: CourseEvalAI integrates rubric-guided fine-tuning, dual-layer evaluation, and graph-based storage into a unified framework. This combination provides a replicable and interpretable methodology that enhances the consistency, transparency, and pedagogical value of LLM-based evaluators in higher education and beyond. Full article

Exploring homogeneous dense groups is one of the important issues in social network structure measurement. k-core decomposition and core member filtering are common methods to uncover homogeneous dense groups in a network. However, existing methods of k-core decomposition struggle to support in-depth exploration of homogeneous dense groups. To address this issue, we store social networks in a graph database, taking advantage of its characteristics such as property indexes and batch queries. Based on this storage, we propose a k-core decomposition algorithm to improve the efficiency of homogeneous dense group detection. Subsequently, we introduce a core member filtering algorithm for identifying core members, a key exploration goal of this study. In experiments, we verify the efficiency of the k-core decomposition algorithm. Finally, we conduct an in-depth analysis of the characteristics of k-cores and their core members, yielding several important conclusions. For example, the relationship between the core number and the number of nodes obeys the power law distribution. In addition, we find that despite the strong connection of the core members, they do not play an important role in the information spreading of social networks. Full article

The accurate prediction of herb–target interactions is essential for the modernization of traditional Chinese medicine (TCM) and the advancement of drug discovery. Nonetheless, the inherent complexity of herbal compositions and diversity of molecular targets render experimental validation both time-consuming and labor-intensive. We propose a graph neural network model, MAMGN-HTI, which integrates metapaths with attention mechanisms. A heterogeneous graph consisting of herbs, efficacies, ingredients, and targets is constructed, where semantic metapaths capture latent relationships among nodes. An attention mechanism is employed to dynamically assign weights, thereby emphasizing the most informative metapaths. In addition, ResGCN and DenseGCN architectures are combined with cross-layer skip connections to improve feature propagation and enable effective feature reuse. Experiments show that MAMGN-HTI outperforms several state-of-the-art methods across multiple metrics, exhibiting superior accuracy, robustness, and generalizability in HTI prediction and candidate drug screening. Validation against literature and databases further confirms the model’s predictive reliability. The model also successfully identified herbs with potential therapeutic effects for hyperthyroidism, including Vinegar-processed Bupleuri Radix (Cu Chaihu), Prunellae Spica (Xiakucao), and Processed Cyperi Rhizoma (Zhi Xiangfu). MAMGN-HTI provides a reliable computational framework and theoretical foundation for applying TCM in hyperthyroidism treatment, providing mechanistic insights while improving research efficiency and resource utilization. Full article

To address the fragmentation and weak correlation of knowledge in the design method domain, this paper proposes a framework for constructing a knowledge graph driven by multi-source heterogeneous data. The process involves collecting multi-source heterogeneous data and subsequently utilizing text mining and natural language processing techniques to extract design themes and method elements. A “theme–stage–attribute” three-dimensional mapping model is established to achieve semantic coupling of knowledge. The BERT-BiLSTM-CRF (Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory-Conditional Random Field) model is employed for entity recognition and relation extraction, while the Sentence-BERT (Sentence Bidirectional Encoder Representations from Transformers) model is used to perform multi-source knowledge fusion. The Neo4j graph database facilitates knowledge storage, visualization, and querying, forming the basis for developing a prototype of a design method recommendation system. The framework’s effectiveness was validated through experiments on extraction performance and knowledge graph quality. The results demonstrate that the framework achieves an F1 score of 91.2% for knowledge extraction, and an 8.44% improvement over the baseline. The resulting graph’s node and relation coverage reached 94.1% and 91.2%, respectively. In complex semantic query tasks, the framework shows a significant advantage over traditional classification systems, achieving a maximum F1 score of 0.97. It can effectively integrate dispersed knowledge in the field of design methods and support method matching throughout the entire design process. This research is of significant value for advancing knowledge management and application in innovative product design. Full article

Recipes available on the web often lack nutritional transparency and clear indicators of dietary suitability. While searching by title is straightforward, exploring recipes that meet combined dietary needs, nutritional goals, and ingredient-level preferences remains challenging. Most existing recipe search systems do not effectively support flexible multi-dietary reasoning in combination with user preferences and restrictions. For example, users may seek gluten-free and dairy-free dinners with suitable substitutions, or compound goals such as vegan and low-fat desserts. Recent systematic reviews report that most food recommender systems are content-based and often non-personalized, with limited support for dietary restrictions, ingredient-level exclusions, and multi-criteria nutrition goals. This paper introduces DietQA, an end-to-end, language-adaptable chatbot system that integrates a Knowledge Graph (KG), Retrieval-Augmented Generation (RAG), and a Large Language Model (LLM) to support personalized, dietary-aware recipe search and question answering. DietQA crawls Greek-language recipe websites to extract structured information such as titles, ingredients, and quantities. Nutritional values are calculated using validated food composition databases, and dietary tags are inferred automatically based on ingredient composition. All information is stored in a Neo4j-based knowledge graph, enabling flexible querying via Cypher. Users interact with the system through a natural language chatbot friendly interface, where they can express preferences for ingredients, nutrients, dishes, and diets, and filter recipes based on multiple factors such as ingredient availability, exclusions, and nutritional goals. DietQA supports multi-diet recipe search by retrieving both compliant recipes and those adaptable via ingredient substitutions, explaining how each result aligns with user preferences and constraints. An LLM extracts intents and entities from user queries to support rule-based Cypher retrieval, while the RAG pipeline generates contextualized responses using the user query and preferences, retrieved recipes, statistical summaries, and substitution logic. The system integrates real-time updates of recipe and nutritional data, supporting up-to-date, relevant, and personalized recommendations. It is designed for language-adaptable deployment and has been developed and evaluated using Greek-language content. DietQA provides a scalable framework for transparent and adaptive dietary recommendation systems powered by conversational AI. Full article

Integrating artificial intelligence (AI) with the Quantitative Structure-Activity Relationship (QSAR) has transformed modern drug discovery by empowering faster, more accurate, and scalable identification of therapeutic compounds. This review outlines the evolution from classical QSAR methods, such as multiple linear regression and partial least squares, to advanced machine learning and deep learning approaches, including graph neural networks and SMILES-based transformers. Molecular docking and molecular dynamics simulations are presented as cooperative tools that boost the mechanistic consideration and structural insight into the ligand-target interactions. Discussions on using PROTACs and targeted protein degradation, ADMET prediction, and public databases and cloud-based platforms to democratize access to computational modeling are well presented with priority. Challenges related to authentication, interpretability, regulatory standards, and ethical concerns are examined, along with emerging patterns in AI-driven drug development. This review is a guideline for using computational models and databases in explainable, data-rich and profound drug discovery pipelines. Full article

In this paper, we develop a systematic review of the existing literature on the mathematical modeling of several aspects of pollinators. We selected the MathSciNet and Wos databases and performed a search for the words “pollinator” and “mathematical model”. This search yielded a total of 236 records. After a detailed screening process, we retained 107 publications deemed most relevant to the topic of mathematical modeling in pollinator systems. We conducted a bibliometric analysis and categorized the studies based on the mathematical approaches used as the central tool in the mathematical modeling and analysis. The mathematical theories used to obtain the mathematical models were ordinary differential equations, partial differential equations, graph theory, difference equations, delay differential equations, stochastic equations, numerical methods, and other types of theories, like fractional order differential equations. Meanwhile, the topics were positive bounded solutions, equilibrium and stability analysis, bifurcation analysis, optimal control, and numerical analysis. We summarized the research findings and identified some challenges that could inform the direction of future research, highlighting areas that will aid in the development of future research. Full article