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PubMed2026

Therapeutic melanoma vaccines: Platforms, neoantigen strategies, and emerging combination immunotherapies.

Melanoma has emerged as a major focus of cancer immunotherapy research because of its highly immunogenic nature and responsiveness to immune-based treatments. Therapeutic melanoma vaccines are designed to stimulate tumor-specific immune responses through the delivery of Tumor-Associated Antigens (TAAs), Tumor-Specific Antigens (TSAs), and personalized neoantigens. This narrative review provides an overview of current melanoma vaccine strategies, including peptide-based vaccines, dendritic cell vaccines, nucleic acid-based platforms such as mRNA, DNA, and viral vector vaccines. Recent advances in vaccine engineering and tumor genomics have accelerated the development of personalized neoantigen vaccines capable of targeting mutations unique to individual tumors. In parallel, Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being incorporated into neoantigen identification pipelines to improve epitope prediction and optimize vaccine design. Combination strategies involving Immune Checkpoint Inhibitors (ICIs), particularly anti-PD-1 and anti-CTLA-4 therapies, have further enhanced interest in melanoma vaccines by helping overcome tumor-induced immune suppression and augment T-cell activation. In addition to reviewing vaccine mechanisms and emerging technologies, this manuscript examines the evolving clinical trial landscape through analysis of melanoma vaccine studies registered on ClinicalTrials.gov. Although many studies have reported encouraging safety and immunogenicity findings, challenges related to tumor heterogeneity, immune evasion, biomarker selection, and manufacturing complexity continue to limit widespread clinical implementation. Ongoing advances in computational immunology, biomaterial engineering, and precision oncology are expected to further refine melanoma vaccine development and improve therapeutic efficacy. Collectively, these innovations may help establish melanoma vaccines as an increasingly important component of future personalized cancer immunotherapy strategies.

DNA vaccines↗

PubMed1996

Comparison of an expert system with other clinical scores for the evaluation of severity of asthma.

"Asthmaexpert" was produced at the special request of several clinicians in order to obtain a better understanding of the medical decisions taken by clinical experts in the management of asthmatic patients. In order to assess the severity of asthma, a new score called Artificial Intelligence score (AI score), produced by Asthmaexpert, was compared with three other scores (Aas, Hargreave and Brooks). One hundred patients were enrolled prospectively in the study during their first consultation in the out-patient clinic. Distribution of severity level according to the different scores was studied, and the reliability between AI and other scores was evaluated by Kappa and MacNemar tests. Correlations with functional parameters were performed. The AI score assessed higher levels of severity than the other scores (Kappa = 18, 28 and 10% for Aas, Hargreave and Brooks, respectively) with significant MacNemar test in all cases. There was a significant correlation between AI score and forced expiratory volume in one second (FEV1) (r = 0.73). These data indicate that the AI score is a severity score which defines higher levels of severity than the chosen scores. Correlations for functional parameters are good. This score appears easy to use for the first consultation of an asthmatic patient.

Adult↗

PubMed2005

GenClust: a genetic algorithm for clustering gene expression data.

BACKGROUND: Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering. RESULTS: GenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, compact and easy to update; (b) it can be used naturally in conjunction with data driven internal validation methods. We have experimented with the FOM methodology, specifically conceived for validating clusters of gene expression data. The validity of GenClust has been assessed experimentally on real data sets, both with the use of validation measures and in comparison with other algorithms, i.e., Average Link, Cast, Click and K-means. CONCLUSION: Experiments show that none of the algorithms we have used is markedly superior to the others across data sets and validation measures; i.e., in many cases the observed differences between the worst and best performing algorithm may be statistically insignificant and they could be considered equivalent. However, there are cases in which an algorithm may be better than others and therefore worthwhile. In particular, experiments for GenClust show that, although simple in its data representation, it converges very rapidly to a local optimum and that its ability to identify meaningful clusters is comparable, and sometimes superior, to that of more sophisticated algorithms. In addition, it is well suited for use in conjunction with data driven internal validation measures and, in particular, the FOM methodology.

Algorithms↗

PubMed2003

EURISWEB--Web-based epidemiological surveillance of antibiotic-resistant pneumococci in day care centers.

BACKGROUND: EURIS (European Resistance Intervention Study) was launched as a multinational study in September of 2000 to identify the multitude of complex risk factors that contribute to the high carriage rate of drug resistant Streptococcus pneumoniae strains in children attending Day Care Centers in several European countries. Access to the very large number of data required the development of a web-based infrastructure - EURISWEB - that includes a relational online database, coupled with a query system for data retrieval, and allows integrative storage of demographic, clinical and molecular biology data generated in EURIS. METHODS: All components of the system were developed using open source programming tools: data storage management was supported by PostgreSQL, and the hypertext preprocessor to generate the web pages was implemented using PHP. The query system is based on a software agent running in the background specifically developed for EURIS. RESULTS: The website currently contains data related to 13,500 nasopharyngeal samples and over one million measures taken from 5,250 individual children, as well as over one thousand pre-made and user-made queries aggregated into several reports, approximately. It is presently in use by participating researchers from three countries (Iceland, Portugal and Sweden). CONCLUSION: An operational model centered on a PHP engine builds the interface between the user and the database automatically, allowing an easy maintenance of the system. The query system is also sufficiently adaptable to allow the integration of several advanced data analysis procedures far more demanding than simple queries, eventually including artificial intelligence predictive models.

Child↗

PubMed2006

Context-sensitive autoassociative memories as expert systems in medical diagnosis.

BACKGROUND: The complexity of our contemporary medical practice has impelled the development of different decision-support aids based on artificial intelligence and neural networks. Distributed associative memories are neural network models that fit perfectly well to the vision of cognition emerging from current neurosciences. METHODS: We present the context-dependent autoassociative memory model. The sets of diseases and symptoms are mapped onto a pair of basis of orthogonal vectors. A matrix memory stores the associations between the signs and symptoms, and their corresponding diseases. A minimal numerical example is presented to show how to instruct the memory and how the system works. In order to provide a quick appreciation of the validity of the model and its potential clinical relevance we implemented an application with real data. A memory was trained with published data of neonates with suspected late-onset sepsis in a neonatal intensive care unit (NICU). A set of personal clinical observations was used as a test set to evaluate the capacity of the model to discriminate between septic and non-septic neonates on the basis of clinical and laboratory findings. RESULTS: We show here that matrix memory models with associations modulated by context can perform automatic medical diagnosis. The sequential availability of new information over time makes the system progress in a narrowing process that reduces the range of diagnostic possibilities. At each step the system provides a probabilistic map of the different possible diagnoses to that moment. The system can incorporate the clinical experience, building in that way a representative database of historical data that captures geo-demographical differences between patient populations. The trained model succeeds in diagnosing late-onset sepsis within the test set of infants in the NICU: sensitivity 100%; specificity 80%; percentage of true positives 91%; percentage of true negatives 100%; accuracy (true positives plus true negatives over the totality of patients) 93,3%; and Cohen's kappa index 0,84. CONCLUSION: Context-dependent associative memories can operate as medical expert systems. The model is presented in a simple and tutorial way to encourage straightforward implementations by medical groups. An application with real data, presented as a primary evaluation of the validity and potentiality of the model in medical diagnosis, shows that the model is a highly promising alternative in the development of accuracy diagnostic tools.

Decision Support Systems, Clinical↗

PubMed2005

Scanning the horizon: emerging hospital-wide technologies and their impact on critical care.

This commentary represents a selective survey of developments relevant to critical care. Selected themes include advances in point-of-care diagnostic testing, glucose control, novel microbiological diagnostics and infection control measures, and developments in information technology that have implications for intensive care. The latter encompasses an early example of an artificially intelligent clinical decision support mechanism, the introduction of a national health care information technology programme (UK NPfIT) and its implications, and exotic threats to patient safety due to emergent behaviour in complex information systems.

Biomedical Technology↗

PubMed2024

Private detection of relatives in forensic genomics using homomorphic encryption.

BACKGROUND: Forensic analysis heavily relies on DNA analysis techniques, notably autosomal Single Nucleotide Polymorphisms (SNPs), to expedite the identification of unknown suspects through genomic database searches. However, the uniqueness of an individual's genome sequence designates it as Personal Identifiable Information (PII), subjecting it to stringent privacy regulations that can impede data access and analysis, as well as restrict the parties allowed to handle the data. Homomorphic Encryption (HE) emerges as a promising solution, enabling the execution of complex functions on encrypted data without the need for decryption. HE not only permits the processing of PII as soon as it is collected and encrypted, such as at a crime scene, but also expands the potential for data processing by multiple entities and artificial intelligence services. METHODS: This study introduces HE-based privacy-preserving methods for SNP DNA analysis, offering a means to compute kinship scores for a set of genome queries while meticulously preserving data privacy. We present three distinct approaches, including one unsupervised and two supervised methods, all of which demonstrated exceptional performance in the iDASH 2023 Track 1 competition. RESULTS: Our HE-based methods can rapidly predict 400 kinship scores from an encrypted database containing 2000 entries within seconds, capitalizing on advanced technologies like Intel AVX vector extensions, Intel HEXL, and Microsoft SEAL HE libraries. Crucially, all three methods achieve remarkable accuracy levels (ranging from 96% to 100%), as evaluated by the auROC score metric, while maintaining robust 128-bit security. These findings underscore the transformative potential of HE in both safeguarding genomic data privacy and streamlining precise DNA analysis. CONCLUSIONS: Results demonstrate that HE-based solutions can be computationally practical to protect genomic privacy during screening of candidate matches for further genealogy analysis in Forensic Genetic Genealogy (FGG).

Humans↗

PubMed2025

Advances in tumor subclone formation and mechanisms of growth and invasion.

Tumor subclones refer to distinct cell populations within the same tumor that possess different genetic characteristics. They play a crucial role in understanding tumor heterogeneity, evolution, and therapeutic resistance. The formation of tumor subclones is driven by several key mechanisms, including the inherent genetic instability of tumor cells, which facilitates the accumulation of novel mutations; selective pressures from the tumor microenvironment and therapeutic interventions, which promote the expansion of certain subclones; and epigenetic modifications, such as DNA methylation and histone modifications, which alter gene expression patterns. Major methodologies for studying tumor subclones include single-cell sequencing, liquid biopsy, and spatial transcriptomics, which provide insights into clonal architecture and dynamic evolution. Beyond their direct involvement in tumor growth and invasion, subclones significantly contribute to tumor heterogeneity, immune evasion, and treatment resistance. Thus, an in-depth investigation of tumor subclones not only aids in guiding personalized precision therapy, overcoming drug resistance, and identifying novel therapeutic targets, but also enhances our ability to predict recurrence and metastasis risks while elucidating the mechanisms underlying tumor heterogeneity. The integration of artificial intelligence, big data analytics, and multi-omics technologies is expected to further advance research in tumor subclones, paving the way for novel strategies in cancer diagnosis and treatment. This review aims to provide a comprehensive overview of tumor subclone formation mechanisms, evolutionary models, analytical methods, and clinical implications, offering insights into precision oncology and future translational research.

Humans↗

PubMed2025

DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.

DNA methylation is an epigenetic modification that regulates gene expression by adding methyl groups to DNA, affecting cellular function and disease development. Machine learning, a subset of artificial intelligence, analyzes large datasets to identify patterns and make predictions. Over the past two decades, advances in bioinformatics technologies for arrays and sequencing have generated vast amounts of data, leading to the widespread adoption of machine learning methods for analyzing complex biological information for medical problems. This review explores recent advancements in DNA methylation studies that leverage emerging machine learning techniques for more precise, comprehensive, and rapid patient diagnostics based on DNA methylation markers. We present a general workflow for researchers, from clinical research questions to result interpretation and monitoring. Additionally, we showcase successful examples in diagnosing cancer, neurodevelopmental disorders, and multifactorial diseases. Some of these studies have led to the development of diagnostic platforms that have entered the global healthcare market, highlighting the promising future of this field.

Humans↗

PubMed2026

Improving recombinant protein productivity in CHO cells via multi-omics data integration.

Chinese hamster ovary (CHO) cells represent the dominant host system for the production of recombinant therapeutic proteins. In recent decades, extensive research has focused on process/media optimization and cell line engineering to improve both the productivity and quality of biopharmaceutical proteins produced in CHO cells. Nevertheless, the inherent complexity of biological pathways and the heterogeneous cellular responses to different environmental conditions have posed substantial challenges to traditional methodologies. Recent advances in omics technologies have enabled comprehensive characterization of CHO cell physiology, providing multidimensional molecular and phenotypic insights that facilitate the enhancement of recombinant protein production. This review first summarizes the methodologies and advances in CHO omics research, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics. It then examines contemporary approaches to integrate and analyze multi-omics data in CHO cells. The review further elucidates how these multi-omics datasets can be strategically applied across various developmental stages, including cell line selection, genetic engineering, expression vector design, and bioprocess optimization. Finally, we explore the transformative potential of integrating multi-omics with artificial intelligence and discuss promising future research directions in CHO cell studies. These emerging paradigms offer novel opportunities for data-driven cell engineering and bioprocess optimization in CHO-based biomanufacturing.

Bioprocessing↗

PubMed2004

Accuracy of references in five biomedical informatics journals.

OBJECTIVE: To determine the rate and type of errors in biomedical informatics journal article references. METHODS: References in articles from the first 2004 issues of five biomedical informatics journals, Journal of the American Medical Informatics Association, Journal of Biomedical Informatics, International Journal of Medical Informatics, Methods of Information in Medicine, and Artificial Intelligence in Medicine were compared with MEDLINE for journal, authors, title, year, volume, and page number accuracy. If discrepancies were identified, the reference was compared with the original publication. Two reviewers independently evaluated each reference. RESULTS: The five journal issues contained 37 articles. Among the 656 eligible references, 225 (34.3%) included at least one error. Among the 225 references, 311 errors were identified. One or more errors were found in the bibliography of 31 (84%) of the 37 articles. The reference error rates by journal ranged from 22.1% to 40.7%. Most errors (39.0%) occurred in the author element, followed by the journal (31.2%), title (17.7%), page (7.4%), year (3.5%), and volume (1.3%) information. CONCLUSION: The study identified a considerable error rate in the references of five biomedical informatics journals. Authors are responsible for the accuracy of references and should more carefully check them, possibly using informatics-based assistance.

Bibliographies as Topic↗

PubMed2006

Humans can adopt optimal discounting strategy under real-time constraints.

Critical to our many daily choices between larger delayed rewards, and smaller more immediate rewards, are the shape and the steepness of the function that discounts rewards with time. Although research in artificial intelligence favors exponential discounting in uncertain environments, studies with humans and animals have consistently shown hyperbolic discounting. We investigated how humans perform in a reward decision task with temporal constraints, in which each choice affects the time remaining for later trials, and in which the delays vary at each trial. We demonstrated that most of our subjects adopted exponential discounting in this experiment. Further, we confirmed analytically that exponential discounting, with a decay rate comparable to that used by our subjects, maximized the total reward gain in our task. Our results suggest that the particular shape and steepness of temporal discounting is determined by the task that the subject is facing, and question the notion of hyperbolic reward discounting as a universal principle.

Adaptation, Physiological↗

PubMed2025

AWGE-ESPCA: An edge sparse PCA model based on adaptive noise elimination regularization and weighted gene network for Hermetia illucens genomic data analysis.

Hermetia illucens is an important insect resource. Studies have shown that exploring the effects of Cu2+-stressed on the growth and development of the Hermetia illucens genome holds significant scientific importance. There are three major challenges in the current studies of Hermetia illucens genomic data analysis: firstly, the lack of available genomic data which limits researchers in Hermetia illucens genomic data analysis. Secondly, to the best of our knowledge, there are no Artificial Intelligence (AI) feature selection models designed specifically for Hermetia illucens genome. Unlike human genomic data, noise in Hermetia illucens data is a more serious problem. Third, how to choose those genes located in the pathway enrichment region. Existing models assume that each gene probe has the same priori weight. However, researchers usually pay more attention to gene probes which are in the pathway enrichment region. Based on the above challenges, we initially construct experiments and establish a new Cu2+-stressed Hermetia illucens growth genome dataset. Subsequently, we propose AWGE-ESPCA: an edge Sparse PCA model based on adaptive noise elimination regularization and weighted gene network. The AWGE-ESPCA model innovatively proposes an adaptive noise elimination regularization method, effectively addressing the noise challenge in Hermetia illucens genomic data. We also integrate the known gene-pathway quantitative information into the Sparse PCA(SPCA) framework as a priori knowledge, which allows the model to filter out the gene probes in pathway-rich regions as much as possible. Ultimately, this study conducts five independent experiments and compared four latest Sparse PCA models as well as representative supervised and unsupervised baseline models to validate the model performance. The experimental results demonstrate the superior pathway and gene selection capabilities of the AWGE-ESPCA model. Ablation experiments validate the role of the adaptive regularizer and network weighting module. To summarize, this paper presents an innovative unsupervised model for Hermetia illucens genome analysis, which can effectively help researchers identify potential biomarkers. In addition, we also provide a working AWGE - ESPCA model code in the address: https://github.com/yhyresearcher/AWGE_ESPCA.

Animals↗

PubMed1993

[Integrated gait analysis for future routine clinical use].

To describe the phenomenon of human movement by means of kinematic and kinetic measurements in a more objective manner, so-called integrated gait analysis systems have been developed. Although the commercially available equipment has already reached high technical levels, it does not play an important role in routine clinical applications. Due to this fact, the main arguments of the practitioners still address the practicability of the devices as well as the lack of meaningful software support for data assessment. The former concern has led to the integration of two KISTLER platforms beneath a treadmill belt in order to continuously capture 3D ground reaction force data. The latter one has prompted the development of an additional software kit, which statistically preprocesses the raw data set for further computations and which should prospectively allow a detailed analysis with artificial intelligence techniques. This new software becomes even more meaningful from the practitioner's point of view, because it may also be used as a stand alone unit for a preliminary assessment of gait patterns. The first part deals with the design of integrating two KISTLER platforms beneath the treadmill belt. A description of the analysis software follows, which removes noise, especially the influence of friction, from the raw data set. Thereby the 3D ground reaction force, which is originally caused by the patient while walking, is reconstructed. Preliminary results are presented. In the second part methods of automatically preprocessing the kinematic data set are focused upon.

Electromyography↗

PubMed1999

Neural networks for the biochemical prediction of bone mass loss.

Neural networks are specialized artificial intelligence techniques that have shown high efficiency in dealing with complex problems. Paradigms such as backpropagation have been successfully applied in a number of biomedical applications, but not in attempts to identify women at risk of postmenopausal osteoporotic complications. In this paper, several neural networks were trained using different combinations of biochemical variables as inputs. Bone densitometric measurements in Ward's triangle and in the spinal column were used as separate classification criteria (outputs) between slow and fast bone mass losers. The most parsimonious model with the best performance included plasma concentrations of estrone, estradiol, osteocalcin, parathyrin and urine concentrations of calcium and hydroxyproline (expressed as ratio to creatinine excretion) as input neurons; ten neurons in a single hidden layer; and one neuron in the output layer. Diagnostic efficiency was 76% in Ward's triangle and 74% in the spinal column; sensitivity was 70 and 81%, and specificity was 77 and 65%, respectively. Linear discriminant analysis showed a diagnostic efficiency of 66% in Ward's triangle and 64% in the spinal column, sensitivity was 55 and 86%, and specificity was 75 and 13%, respectively. We conclude that performance of the stepwise discriminant analysis was not superior to the neural networks.

Adult↗

PubMed1987

Dreams of the rarebit fiend: neuromedical synthesis of unconscious meaning.

A promising new "ecumenical" movement in psychiatry attempts to synthesize the two great intellectual traditions, psychoanalysis and neurobiology, so that we may avoid splitting the care of the patient into the partial domains of biotherapy that lacks the understanding of mental interrelations and purely psychological psychotherapy that lacks an appreciation of the embeddedness of mental processes in brain function. Recent synthetic work is assessed, taking as a point of departure an historic symposium in Pittsburgh, October 26-27, 1984, entitled "Neurobiology and the Unconscious: Psychoanalysis Looks Toward the Future." The means of representation of meaning (whose description was begun by Freud) in such unconscious material as dreams and folklore show the imprint of the brain function in which they are imbedded. Our afferent and efferent processes, including language, are patterned by their neuromedical basis. Linkages will be sought of representational images to visual, vestibular, and neuromotor traces: evidence that the human "thinking machine" is a very human body rather than some disembodied psychological self or computer simulation by artificial intelligence programming. Illustrative material is in part drawn from the popular dream episodes cartooned by Winsor McCay, which were considered graphic masterpieces, and incorporated representations of many normal unconscious brain mechanisms, including unusual perspectives, vestibular sensations, neuromotor inhibitions, transformations; visual and linguistic distortions, bizarre bodily intrusions, and sexual symbols. J. Allen Hobson and R.W. McCarley's 1977 arguments for the determining the significance of the pontine dream generator may have been anticipated by McCay's 1905 Dreams of the Rarebit Fiend in which, at the end of each very Freudian nightmare, the dreamer wakes and swears off eating welsh rarebits as if they caused all his unconscious images. To avoid a biological reductiveness, Freud, whom Sulloway (1983) has described as a biologist of the mind, resorted to presenting psychoanalysis as a pure psychology, not because he ever doubted the neurological imbeddedness of mind, but only because he felt medical psychoanalysts were too easily seduced and distracted by neurological mechanisms of his day to appreciate properly the importance of psychodynamics. Increasing recognition of the imbeddedness of psychoanalysis in brain function is now timely and likely, providing fresh directions for both medical psychoanalysis and the neurosciences.(ABSTRACT TRUNCATED AT 400 WORDS)

Brain↗

PubMed2002

Clinical potential of proteomics in the diagnosis of ovarian cancer.

The need for specific and sensitive markers of ovarian cancer is critical. Finding a sensitive and specific test for its detection has an important public health impact. Currently, there are no effective screening options available for patients with ovarian cancer. CA-125, the most widely used biomarker for ovarian cancer, does not have a high positive predictive value and it is only effective when used in combination with other diagnostic tests. However, pathologic changes taking place within the ovary may be reflected in biomarker patterns in the serum. Combination of mass spectra generated by new proteomic technologies, such as surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) and artificial-intelligence-based informatic algorithms, have been used to discover a small set of key protein values and discriminate normal from ovarian cancer patients. Serum proteomic pattern analysis might be applied ultimately in medical screening clinics, as a supplement to the diagnostic work-up and evaluation.

Biomarkers, Tumor↗

PubMed1998

[Identification of psychosocial problems in patients with Hansen's disease by analysis of computerized resources].

The purpose of this study was to identify leprosy patients' psychosocial problems experienced after they were informed about their diagnosis. We focused attention upon concerns and behavioral changes related to their families, friends, jobs and to themselves. Data were obtained by a two opened questions interview and they were analysed with the aid of artificial intelligence techniques. These intelligence tools were used to discover the most frequent words, phrases and concepts existing in the interview reports. The results showed that after being informed about their diagnosis, the majority of the patients referred some concerns and behavioral changes related to their families, friends, jobs and to themselves. The main concerns of the population were related to the disease (transmission, the treatment extension, the possibility of hospitalization, the uncertainty about the cure). These facts induced some of the patients to avoid telling people about the disease they have.

Adolescent↗