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Medical expert systems are a successful field of applied artificial intelligence. We constructed an otoneurological expert system in our previous research, and in this study we consider its reasoning method. The reasoning process can be described as a modified nearest neighbour solution derived from pattern recognition. The expert system was tested and functions reliably.
The computer-automated structure evaluation programs MultiCASE and CASE were used to perform a quantitative structure-activity relationship study on tubulin polymerization inhibitors. A learning set of 536 chemicals (202 active. 27 marginal, and 307 inactive), built using IC50 values for inhibition of tubulin polymerization or mitosis from this and previous studies, was used for artificial intelligence self-teaching. The algorithms successfully predicted the activity of agents in the learning set with > 90% accuracy. Seventeen MultiCASE and twelve CASE (mostly included in the MultiCASE set) biophores (substructures significantly correlated with activity) were identified with a probability > 0.95. Here we present the biophores of podophyllotoxins, colchicinoids, and certain combretastatins, each examined for structure-activity relationships. For the podophyllotoxins and colchicinoids in the learning set, the correlations between observed and predicted potencies were > 0.85. The algorithms recognized the importance of several known site, electronic, and steric effects in the two classes. A predictive QSAR (R2 = 0.98) was developed for combretastain A-2 and dihydrocombretastatin analogues. The MultiCASE/CASE analyzes were used in combination with molecular models to study relative orientations of colchicine, podophyllotoxin, combretastatin A-4, and steganacin at the colchicine site. This resulted in a new hypothesis, consistent with extensive published experimental data, in which the C-ring and part of the B-ring of colchicine overlap with the A- and B-rings of podophyllotoxin. Consequently, the trimethoxyphenyl rings of colchicine and podophyllotoxin occupied different regions of space, each pointing out from a hydrophobic 'core' occupied by the overlapping biophores. The molecular model of the highly potent combretastatin A-4 could fit into the model binding site in at least three different ways. The developed QSARs were used to identify the potent microtubule stabilizer discodermolide. Its identification, in concert with recently reported findings, suggest potential overlap in the colchicine and paclitaxel binding sites on tubulin.
Many computer applications have been developed in radiology and other medical disciplines to help physicians make decisions. Artificial intelligence (AI)--an approach to computer-based manipulation of symbols to simulate human reasoning--forms the basis of many of these systems. This article's goals are to: acquaint the reader with the motivations and opportunities for computer-based medical decision support systems; identify AI techniques and applications in radiology decision making; assess the impact of these technologies; and consider new directions and opportunities for AI in radiology. Among the exciting new directions is the use of AI to integrate radiology reporting, online decision support, and just-in-time learning to provide useful information and continuing education that is embedded within a radiologist's daily workflow.
OBJECTIVE: Develop and evaluate a natural language processing application for classifying chief complaints into syndromic categories for syndromic surveillance. INTRODUCTION: Much of the input data for artificial intelligence applications in the medical field are free-text patient medical records, including dictated medical reports and triage chief complaints. To be useful for automated systems, the free-text must be translated into encoded form. METHODS: We implemented a biosurveillance detection system from Pennsylvania to monitor the 2002 Winter Olympic Games. Because input data was in free-text format, we used a natural language processing text classifier to automatically classify free-text triage chief complaints into syndromic categories used by the biosurveillance system. The classifier was trained on 4700 chief complaints from Pennsylvania. We evaluated the ability of the classifier to classify free-text chief complaints into syndromic categories with a test set of 800 chief complaints from Utah. RESULTS: The classifier produced the following areas under the ROC curve: Constitutional = 0.95; Gastrointestinal = 0.97; Hemorrhagic = 0.99; Neurological = 0.96; Rash = 1.0; Respiratory = 0.99; Other = 0.96. Using information stored in the system's semantic model, we extracted from the Respiratory classifications lower respiratory complaints and lower respiratory complaints with fever with a precision of 0.97 and 0.96, respectively. CONCLUSION: Results suggest that a trainable natural language processing text classifier can accurately extract data from free-text chief complaints for biosurveillance.
Selecting clinically relevant tumor antigens remains a major challenge in the development of therapeutic cancer vaccines. Although computational approaches have considerably improved neoantigen prediction, many candidate epitopes identified in silico are not ultimately presented on the tumor cell surface. The emergence of immunopeptidomics has provided direct access to naturally processed HLA-associated peptides and has offered new opportunities for antigen discovery. Increasing evidence has shown that information derived from the immunopeptidome becomes considerably more informative when interpreted alongside genomic, transcriptomic, and proteomic data. This integrative view has broadened the spectrum of targetable antigens and has also revealed important limitations related to peptide abundance, HLA diversity, tumor heterogeneity, and the imperfect relationship between antigen presentation and immunogenicity. These issues have renewed interest in multi-antigen vaccine strategies designed to better reflect the complexity of tumor antigen landscapes. Advances in bioinformatics and artificial intelligence are facilitating the interpretation of increasingly complex datasets and are beginning to support more systematic approaches to antigen prioritization. In this review, we discuss how immunopeptidomics is contributing to next-generation cancer vaccine development, summarize the major translational challenges, and highlight emerging concepts that may improve the clinical applicability of immunopeptidomics-guided immunotherapy.
The increasing global human population and the intensification of agriculture present unprecedented challenges for pest control. The escalating resistance of pests to conventional synthetic insecticides, coupled with ecological and health concerns, underscores the urgent need for innovative and sustainable management approaches. Insecticidal peptides, due to their structural diversity, molecular specificity, and biodegradability, are emerging as promising candidates for the development of next-generation bioinsecticides. This strategic roadmap synthesizes recent advances in peptide architectures, ranging from pore-forming scaffolds to designs targeting enzyme inhibition and mimicking neuroendocrine actions, with a focus on the molecular mechanisms underpinning their selectivity and efficacy. By integrating structure-function insights with translational frameworks, we identify critical knowledge gaps and propose a pathway toward biotechnological tools, including bioinspired synthesis, artificial intelligence (AI)-guided peptide engineering, and nanodelivery systems for controlled release. Our analysis positions peptide-based insecticides at the forefront of sustainable agriculture, with the potential to minimize off-target effects, reduce environmental impact, and enhance crop resilience in the face of global change.
BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.
High performance sorbents for flue gas desulfurization can be synthesized by hydration of coal fly ash, calcium sulfate, and calcium oxide. In general, higher desulfurization activity correlates with higher sorbent surface area. Consequently, a major aim in sorbent synthesis is to maximize the sorbent surface area by optimizing the hydration conditions. This work presents an integrated modeling and optimization approach to sorbent synthesis based on statistical experimental design and two artificial intelligence techniques: neural network and genetic algorithm. In the first step of the approach, the main and interactive effects of three hydration variables on sorbent surface area were evaluated using a full factorial design. The hydration variables of interest to this study were hydration time, amount of coal fly ash, and amount of calcium sulfate and the levels investigated were 4-32 h, 5-15 g, and 0-12 g, respectively. In the second step, a neural network was used to model the relationship between the three hydration variables and the sorbent surface area. A genetic algorithm was used in the last step to optimize the input space of the resulting neural network model. According to this integrated modeling and optimization approach, an optimum sorbent surface area of 62.2m(2)g(-1) could be obtained by mixing 13.1g of coal fly ash and 5.5 g of calcium sulfate in a hydration process containing 100ml of water and 5 g of calcium oxide for a fixed hydration time of 10 h.
Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality parameters in the urban area are important due to health impact. Artificial intelligent techniques are successfully used in modelling of highly complex and non-linear phenomena. In this study, adaptive neuro-fuzzy logic method has been proposed to estimate the impact of meteorological factors on SO2 and total suspended particular matter (TSP) pollution levels over an urban area. The model forecasts satisfactorily the trends in SO2 and TSP concentration levels, with performance between 75-90% and 69-80 %, respectively.
Gas chromatography and pattern recognition methods were used to develop a potential method for differentiating European honeybees from Africanized honeybees. The test data consisted of 237 gas chromatograms of hydrocarbon extracts obtained from the wax glands, cuticle, and exocrine glands of European and Africanized honeybees. Each gas chromatogram contained 65 peaks corresponding to a set of standardized retention time windows. A genetic algorithm (GA) for pattern recognition was used to identify features in the gas chromatograms characteristic of the genotype. The pattern recognition GA searched for features in the chromatograms that optimized the separation of the European and Africanized honeybees in a plot of the two or three largest principal components of the data. Because the largest principal components capture the bulk of the variance in the data, the peaks identified by the pattern recognition GA primarily contained information about differences between gas chromatograms of European and Africanized honeybees. The principal component analysis routine embedded in the fitness function of the pattern recognition GA acted as an information filter, significantly reducing the size of the search space since it restricted the search to feature sets whose principal component plots showed clustering on the basis of the bees' genotype. In addition, the algorithm focused on those classes and/or samples that were difficult to classify as it trained using a form of boosting. Samples that consistently classify correctly are not as heavily weighted as samples that are difficult to classify. Over time, the algorithm learns its optimal parameters in a manner similar to a neural network. The pattern recognition GA integrates aspects of artificial intelligence and evolutionary computations to yield a "smart" one-pass procedure for feature selection and classification.
Teleological explanations (TEs) account for the existence or properties of an entity in terms of a function: we have hearts because they pump blood, and telephones for communication. While many teleological explanations seem appropriate, others are clearly not warranted--for example, that rain exists for plants to grow. Five experiments explore the theoretical commitments that underlie teleological explanations. With the analysis of [Wright, L. (1976). Teleological Explanations. Berkeley, CA: University of California Press] from philosophy as a point of departure, we examine in Experiment 1 whether teleological explanations are interpreted causally, and confirm that TEs are only accepted when the function invoked in the explanation played a causal role in bringing about what is being explained. However, we also find that playing a causal role is not sufficient for all participants to accept TEs. Experiment 2 shows that this is not because participants fail to appreciate the causal structure of the scenarios used as stimuli. In Experiments 3-5 we show that the additional requirement for TE acceptance is that the process by which the function played a causal role must be general in the sense of conforming to a predictable pattern. These findings motivate a proposal, Explanation for Export, which suggests that a psychological function of explanation is to highlight information likely to subserve future prediction and intervention. We relate our proposal to normative accounts of explanation from philosophy of science, as well as to claims from psychology and artificial intelligence.
The prediction of sensory attributes from ingredient-level formulations is an emerging challenge at the intersection of food science and artificial intelligence. We address the fundamental question of whether the taste of a food can be predicted from its ingredients by treating recipes as composite materials. We apply Hashin-Shtrikman (HS) and Reuss-Voigt (RV) bounds, techniques originally developed for elastic moduli, as a null-hypothesis additive baseline for five taste dimensions (sweetness, sourness, bitterness, umami, saltiness) on a curated dataset of 70 recipes decomposed into 115 distinct ingredients scored against a library of 209 ingredient-level taste references with trained-panel ground truth. This baseline systematically under-predicts perceived taste: 77% of actual taste values exceeded the HS upper bound, with the exceedance rate ranging from 26% (bitterness) to 97% (saltiness). We traced this gap to specific processing chemistry (Maillard reactions, caramelization, evaporative concentration, protein hydrolysis, and nucleotide synergy) and introduced a hybrid model that augments the HS baseline with eight chemistry-proxy features encoding these mechanisms. Our results show that our interpretable hybrid model eliminates the systematic bias and reduces mean absolute error by 27%-62% for sweetness, sourness, umami, and saltiness while using only 10 interpretable features, achieving performance comparable to a black-box Lasso regression on 115 per-ingredient features. We further demonstrate constrained inverse design via Differential Evolution, recovering ingredient formulations that match target taste profiles subject to compositional bounds. Our work demonstrates how key chemical processes during food preparation can inform and augment physics-based and machine learning models, providing a quantitative fingerprint of processing chemistry's contribution to taste perception and paving the way for model-driven food formulation with targeted sensory characteristics.
Immunotherapy has transformed the management of genitourinary cancers, offering durable responses in selected patient groups. However, the clinical benefit of immune checkpoint inhibitors varies significantly across renal cell carcinoma, urothelial carcinoma, and prostate cancer, underscoring the need for reliable predictive biomarkers. This review summarizes current knowledge on established and emerging biomarkers, including PD L1 expression, tumor mutational burden, molecular subtypes, genomic alterations, tumor microenvironment characteristics, circulating biomarkers, microbiome influences, and multi omic integrative approaches. We discuss their potential clinical relevance, limitations, and applicability across different tumor types. Future directions emphasize the development of composite biomarkers, standardization of testing platforms, real time monitoring strategies, and the integration of advanced technologies such as artificial intelligence and spatial profiling. Understanding and validating these biomarkers will be essential for optimizing personalized immunotherapy in genitourinary cancers.
The European Thoracic Oncology Platform (ETOP) International Breast Cancer Study Group (IBCSG) Partners Foundation initiated a series of workshops for experts to review current evidence and offer recommendations to guide future antibody-drug conjugate (ADC) research. Here, we summarise key findings from the third workshop, which included experts in various solid tumours, basic/translational research scientists and pharmaceutical industry representatives. Recent positive phase III trial data have further incorporated ADCs into the standard of care [e.g. lung: sacituzumab tirumotecan; breast: trastuzumab deruxtecan (T-DXd), sacituzumab govitecan, datopotamab deruxtecan; muscle-invasive bladder cancer: enfortumab vedotin; ovarian cancer: mirvetuximab soravastine; cervical cancer: tisotumab vedotin]. Thus, research priorities must be tailored according to tumour type, potentially focussing initially on settings where ADCs could replace chemotherapy. Many phase III ADC trials have been initiated based on positive phase I data and although these trials are larger than those conducted historically, prespecified criteria (e.g. patient numbers and magnitude of efficacy) should be met to justify proceeding directly to phase III. Importantly, although several ADCs have been successfully developed without mandatory biomarker selection, biomarker-driven ADC development enables rational patient selection, as illustrated by multiple ADCs (e.g. T-DXd, mirvetuximab soravtansine and telisotuzumab vedotin). The identification, development and validation of predictive biomarkers are therefore essential, particularly given several critical nuances, including the algorithms used to assess biomarker status and the type of specimen analysed, all of which may be influenced by temporal and spatial heterogeneity. Additional ADC research priorities include the optimisation of ADC constructs to enhance efficacy/tolerability and the identification of reliable ADC targets, including work to elucidate attributes of already-identified targets. Finally, considering the vast amount of ADC-related data being generated, artificial intelligence could be leveraged to analyse combined datasets and generate composite biomarkers, including tumour histology, optimal target expression thresholds, molecular alterations and activated pathways affecting payload activity and target function, to accelerate research.
Toe-walking is one of the most prevalent gait deviations and has been linked to many diseases. Three major ankle kinematic patterns have been identified in toe-walkers, but the relationships between the causes of toe-walking and these patterns remain unknown. This study aims to identify these relationships. Clearly, such knowledge would increase our understanding of this gait deviation, and could help clinicians plan treatment. The large quantity of data provided by gait analysis often makes interpretation a difficult task. Artificial intelligence techniques were used in this study to facilitate interpretation as well as to decrease subjective interpretation. Of the 716 limbs evaluated, 240 showed signs of toe-walking and met inclusion criteria. The ankle kinematic pattern of the evaluated limbs during gait was assigned to one of three toe-walking pattern groups to build the training data set. Toe-walker clinical measurements (range of movement, muscle spasticity and muscle strength) were coded in fuzzy modalities, and fuzzy decision trees were induced to create intelligible rules allowing toe-walkers to be assigned to one of the three groups. A stratified 10-fold cross validation situated the classification accuracy at 81%. Twelve rules depicting the causes of toe-walking were selected, discussed and characterized using kinematic, kinetic and EMG charts. This study proposes an original approach to linking the possible causes of toe-walking with gait patterns.
Randomized controlled trials remain fundamental to evidence generation in oncology but are increasingly complex, costly, and often misaligned with real-world practice. Traditional explanatory trials, designed under ideal, controlled conditions, frequently enroll highly selected populations, limiting generalizability and underrepresenting key groups such as older adults, patients with comorbidities, and those from low- and middle-income countries. Pragmatic clinical trials offer an alternative by evaluating interventions under routine care conditions, with broader eligibility, simplified procedures, and patient-centered outcomes. To address these challenges, the Gynecologic Cancer InterGroup convened an international brainstorming meeting in May 2025 with multi-disciplinary experts, patients, and advocates to define priorities and develop a roadmap for pragmatic trials in gynecologic oncology. Key discussions emphasized embedding trial design within routine care, aligning eligibility criteria and procedures with standard practice, minimizing non-essential data collection, and prioritizing outcomes meaningful to patients, including quality of life. Innovative designs such as registry-based randomized trials, trials-within-cohorts, and cluster randomization were highlighted as feasible approaches to improve efficiency while preserving internal validity. Integration of patient-reported outcomes and real-world data was considered achievable when carefully streamlined. Major challenges identified included regulatory heterogeneity, consent complexity, data interoperability, and funding limitations, particularly in multi-national settings. Proposed solutions include simplified consent models, centralized ethics processes, hybrid funding strategies, and the responsible use of artificial intelligence to enhance patient identification, recruitment, and potential development of synthetic control arms. Patient engagement was recognized as essential to ensure relevance, feasibility, and equity. Incorporation of patient-reported outcomes was discussed as key to informing acceptance and tolerability. In summary, pragmatic trials within Gynecologic Cancer InterGroup represent a critical pathway to generate efficient, inclusive, and practice-changing evidence in gynecologic cancers across diverse health care settings.
OBJECTIVE: To determine the decay rate of Uniform Record Locators (URLs) in the reference section of biomedical informatics journals. METHODS: URL references were collected from printed journal articles of the first and middle issues of 1999-2004 and electronically available in-press articles in January 2005. We limited this set to five biomedical informatics journals: Artificial Intelligence in Medicine, International Journal of Medical Informatics, Journal of the American Medical Informatics Association: JAMIA, Methods of Information in Medicine, and Journal of Biomedical Informatics. During a 1-month period, URL access attempts were performed eight times a day at regular intervals. RESULTS: Of the 19,108 references extracted from 606 printed and 86 in-press articles, 1112 (5.8%) references contained a URL. Of the 1049 unique URLs, 726 (69.2%) were alive, 230 (21.9%) were dead, and 93 (8.9%) were comatose. URLs from in-press articles included 212 URLs, of which 169 (79.7%) were alive, 21 (9.9%) were dead, and 22 (10.4%) were comatose. The average annual decay, or link rot, rate was 5.4%. CONCLUSION: The URL decay rate in biomedical informatics journals is high. A commonly accepted strategy for the permanent archival of digital information referenced in scholarly publications is urgently needed.
As torrents of new data now emerge from microbial genomics, bioinformatic prediction of immunogenic epitopes remains challenging but vital. In silico methods often produce paradoxically inconsistent results: good prediction rates on certain test sets but not others. The inherent complexity of immune presentation and recognition processes complicates epitope prediction. Two encouraging developments - data driven artificial intelligence sequence-based methods for epitope prediction and molecular modeling methods based on three-dimensional protein structures - offer hope for the future.