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PubMed1993

Neural Network Analysis of DNA flow cytometry histograms.

A pattern recognition system based on Neural Network Analysis, a form of artificial intelligence, was used to search DNA flow cytometry histograms for features that correlated with breast cancer patients' risk of relapse. DNA flow cytometry histograms and clinical follow-up information from 796 breast cancer patients were used to train a Neural Network to predict the clinical outcome of patients in a separate independent set of 794 patients. Median follow-up in this patient data base was short, 23 months. Neural Network Analysis resulted in a model that evaluated DNA flow cytometry histograms differently than conventional analysis, which categorizes the histograms by ploidy and S-phase fraction. Neural Network Analysis appeared to identify low risk and high risk subsets of patients as accurately as conventional analysis. Neural Network Analysis placed heavy emphasis on the region to the right of the diploid G2/M peak, where a subpopulation of nuclei with high DNA content is seen even in many histograms scored as diploid by conventional techniques. The number of nuclei in this region was found to be a powerful predictor of patient outcome, and multivariate analysis showed that the number of nuclei in this region and the S-phase fraction both were independently predictive of relapse. This pilot study suggests that conventional analysis (based on a mechanistic interpretation of regions in flow cytometry histograms) might be used in conjunction with and improved by pattern recognition systems or insights derived from them.

Breast Neoplasms↗
PubMed1996

Computer-assisted interpretation of flow cytometry data in hematology.

A computer program has been developed for computer-assisted diagnosis (including subclassification) of flow cytometry data of acute leukaemias and non-Hodgkin lymphomas by means of artificial intelligence. The knowledge base for the system has been formulated as semantic networks that describe physiological hematopoiesis as well as the pathological situation (e.g., aberrant antigen expression) of hematological disorders. The semantic networks reflect the hierarchy of cells and their occurrence in diseases, the normal and pathological antigen expression patterns of cells, cell maturation, and the frequency of cell populations in normal blood and bone marrow. Using these semantic networks, the diagnosis algorithm compares the characteristic antigen expression pattern of a disease with the actual findings in the blood or bone marrow sample. The algorithm can separate mixed populations by taking double staining findings into account. Finally, a diagnosis text is generated that describes all identified cell populations and the resulting diagnosis. The validation of the program showed a correct diagnosis (disease group and subclassification) in 97% of the cases (n = 633) with slight differences between the disease groups (e.g., B-NHL: 99%, B-cell ALL: 84%).

Acute Disease↗
PubMed2026

The Subtle Crisis: Public Domain Genomes and the Ethics of Translational Infrastructure.

Public domain human genomic resources are infrastructure: tools researchers use to ask basic biological questions whose answers are then translated into products and care. Translational science now asks them to support an expanding set of tasks, including clinical variant interpretation for diverse populations, pharmacogenomic prescribing, polygenic risk prediction, and the training of clinical artificial intelligence. The corpus of public domain genomes, due in large part to upstream recruitment choices, is not fit for these purposes, and the gap between discovery and translation is widening. This essay argues that closing the gap requires treating public domain genomic infrastructure as a particular object of translational bioethics rather than a technical precondition for it. The limited number of genomes in the public domain relative to the broader genomic record, and the typology-friendliness of how that record represents human variation, are two faces of the same set of upstream choices. Reversing them is not a matter of more sampling under existing terms; it is a matter of building infrastructure of a particular kind; infrastructure made from people. That category, common in genomics but absent from the rest of science, demands an ethical apparatus the field has not yet built. Here we consider the commitments such an apparatus requires, and argue that where, how, and with whom we build genomic infrastructure is itself an ethics question the field has largely declined to ask.

Humans↗
PubMed1986

NPPD (spy dust) is predicted to be a mutagen.

With the aid of CASE, the Computer Automated Structure Evaluation system, a new artificial intelligence procedure to study structure-activity relationships and a data base consisting of 233 monocyclic nitroarenes, it is predicted that 5-(4-nitrophenyl)-2,4-pentadienal will be mutagenic for Salmonella typhimurium but that the activity will be very low.

Computers↗
PubMed2026

The Rise of Plant Pan-Genomes: From Genome Variation to Predictive Breeding.

Plant pan-genomics is entering a new phase beyond genome variation discovery, requiring a shift from cataloguing genomic diversity toward understanding how variation generates biological function and breeding value. Here, we propose that the future of plant pan-genomics will be shaped by three conceptual transitions. First, structural variation (SV), presence-absence variation (PAV), and haplotype diversity should be interpreted not merely as genomic differences, but as regulatory components that influence gene networks, chromatin organization, and complex traits. Second, the expansion from species-level pan-genomes to genus-level super pan-genomes provides an evolutionary framework for uncovering adaptive genetic modules preserved in wild relatives and overlooked during domestication. Third, integrating pan-genomes with pan-omics, three-dimensional genome analyses, and artificial intelligence will enable the transformation of genomic variation into predictive models for crop improvement. We further propose that the ultimate value of pan-genomes lies not in generating increasingly complete genome collections, but in establishing a mechanistic bridge between genome diversity, biological function, and breeding decisions. This transition will move crop improvement from empirical selection toward rational genome design, where evolutionary diversity can be systematically interpreted, predicted, and engineered.

Journal Article↗
PubMed2005

Value of two noninvasive methods to detect progression of fibrosis among HCV carriers with normal aminotransferases.

The course of hepatitis C virus (HCV) infection carriers with normal/near-normal aminotransferases (NALT) is usually mild; however, in a few, fibrosis progression occurs. We aimed to verify whether monitoring by liver biopsy might be replaced by noninvasive methods and to identify factors associated with fibrosis progression in patients with persistently normal alanine aminotransferases. We studied 40 untreated HCV-RNA-positive subjects (22 male; median age, 44 years), who underwent two liver biopsies, with a median interval of 78.5 months, during which alanine aminotransferase concentrations (median number of determinations: 12) never exceeded 1.2 times the upper normal limit. Within 9 months from the second biopsy, they were tested by the shear elasticity probe (Fibroscan) and the artificial intelligence algorithm FibroTest. METAVIR fibrosis scores were analyzed in relationship to demographic, clinical, and viral parameters. Weighted kappa analysis was used to verify whether the results of noninvasive methods agreed with histology. Significant fibrosis (> or = F2), present at the first biopsy in only one patient (2.5%), was observed at the second biopsy in 14 patients (35%). At multivariate analysis, excess alcohol consumption in the past (>20 g/d; P = .017) and viral load (>8.0 x 10(6) copies/mL; P = .021) were independent predictors of progression. In identifying patients with significant fibrosis, inter-rater agreement was excellent for Fibroscan (weighted kappa = 1.0), and poor for FibroTest (weighted kappa = -0.041). In conclusion, among HCV carriers with NALT, Fibroscan is superior to the FibroTest in the noninvasive identification of fibrosis, for which excess alcohol consumption in the past and high viral load represent risk factors.

Adult↗
PubMed2026

(Re)imagining the Future of Genetic Counseling: A Reflexive Qualitative Analysis of Sociopolitical Power, Cultural Safety, Systemic Racism, and Comparative Practice in the United Kingdom, Aotearoa New Zealand and, Australia.

Genetic counseling is undergoing a rapid transformation as genomic medicine becomes embedded within mainstream healthcare systems. At the same time, the profession is being challenged to respond to systemic racism, colonial legacies, technological change, and evolving expectations regarding equity and justice. Historically, genetic counseling emerged within twentieth-century medical genetics and was influenced by political, social, scientific, and medical forces that included eugenic ideology, values, and practices. The profession has since evolved substantially toward psychosocial, patient-centered, and non-directive models of care. Contemporary debates regarding "newgenics" or "neugenics" further demonstrate how concerns regarding equity, reproductive ethics, disability, and genomic stratification continue to shape genomic healthcare discourse. This qualitative reflexive practice paper explores how systemic racism, colonial legacy, cultural safety and structural power shape genetic counseling practice in the United Kingdom (UK), Aotearoa New Zealand and Australia, and how these forces continue to reshape the profession's future identity. A reflexive, narrative, and comparative qualitative approach was employed, grounded in the authors' lived professional experiences across UK and Australasian contexts and informed by purposively selected policy, professional and scholarly literature relating to cultural safety, dignity, anti-racism, and Human Rights-Based Decision-Making. Through iterative reflexive dialogue, comparative analysis, and thematic synthesis, four interrelated themes were developed examining sociopolitical context, systemic racism, cultural safety and technologization within contemporary genetic counseling practice. Comparative analysis identified substantial differences in how culturally responsive practice is conceptualized and operationalized across settings. In Aotearoa, cultural safety is strongly shaped by Te Tiriti o Waitangi, bicultural accountability, and Māori sovereignty frameworks. In Australia, culturally safer genomic care has increasingly developed through Indigenous-led initiatives and workforce reform, including the Australian Alliance for Indigenous Genomics (ALIGN). In contrast, UK practice remains largely situated within equality, diversity, and inclusion (EDI) frameworks that may insufficiently address systemic racism and structural power within increasingly diverse populations. Reflexive clinical examples demonstrated how inequities may emerge through undocumented patient values, standardized pathways, assumptions regarding autonomy, and misinterpretation of culturally specific communication styles. Re-imagining the future of genetic counseling requires more than just technological advancement. It requires reflexive engagement with dignity, inequity, and the sociopolitical realities of the populations served. These insights re-imagine a culturally grounded, socially responsive future for genetic counseling in an era shaped by genomic mainstreaming, digital transformation, artificial intelligence and workforce reform and one in which the profession remains ethically anchored, relationally attuned, and committed to justice-oriented practice.

Humans↗
PubMed2002

(Physio)logical circuits: the intellectual origins of the McCulloch-Pitts neural networks.

This article examines the intellectual and institutional factors that contributed to the collaboration of neuropsychiatrist Warren McCulloch and mathematician Walter Pitts on the logic of neural networks, which culminated in their 1943 publication, "A Logical Calculus of the Ideas Immanent in Nervous Activity." Historians and scientists alike often refer to the McCulloch-Pitts paper as a landmark event in the history of cybernetics, and fundamental to the development of cognitive science and artificial intelligence. This article seeks to bring some historical context to the McCulloch-Pitts collaboration itself, namely, their intellectual and scientific orientations and backgrounds, the key concepts that contributed to their paper, and the institutional context in which their collaboration was made. Although they were almost a generation apart and had dissimilar scientific backgrounds, McCulloch and Pitts had similar intellectual concerns, simultaneously motivated by issues in philosophy, neurology, and mathematics. This article demonstrates how these issues converged and found resonance in their model of neural networks. By examining the intellectual backgrounds of McCulloch and Pitts as individuals, it will be shown that besides being an important event in the history of cybernetics proper, the McCulloch-Pitts collaboration was an important result of early twentieth-century efforts to apply mathematics to neurological phenomena.

Biology↗
PubMed2006

Interpretation of surface EMGs in children with cerebral palsy: An initial study using a fuzzy expert system.

Surface EMG detected simultaneously at different muscles has become an important tool for analysing the gait of children with cerebral palsy (CP), as it offers essential information about muscular coordination. However, the interpretation of surface EMG is a difficult task that assumes extensive knowledge and experience. As such, this noninvasive procedure is not frequently used in the general clinical routine. An Artificial Intelligence (AI) system for interpreting surface EMG signals and the resulting muscular coordination patterns could overcome these limitations. To support such interpretation, an expert system based on fuzzy inference methodology was developed. The knowledge-base of the system implemented 15 rules, from which the fuzzy inference methodology performs a prediction of the effectiveness of the muscular coordination during gait. Our aim was to assess the feasibility and value of such an expert system in clinical applications. Surface EMG signals were recorded from the tibialis anterior, soleus muscle, and gastrocnemius muscles of children with CP to assess muscular coordination patterns of ankle movement during gait. Nineteen children underwent 114 surface EMG measurements. Simultaneously, the gait cycles of each patient were determined using foot switches and videotapes. From the EMG signals, the effectiveness of the ankle movement was predicted by the expert system, and predictions were classified using a three-point ordinal scale. In 91 cases (80%), the clinical findings matched the predictions of the expert system. In 23 cases (20%) the predictions of the expert system differed from the clinical findings with 12 cases revealing worse and 11 cases revealing better results in comparison to the clinical findings. As this study is a first attempt to verify the feasibility and correctness of this expert system, the results are promising. Further study is required to assess the correlation with the kinematic data and to include the whole leg.

Cerebral Palsy↗
PubMed1991

Peak assignment in automatic data analysis.

Linear prediction algorithms are able to identify peaks in an NMR spectrum, but are not able to assign these peaks to components anticipated in the spectrum. We have developed an artificial-intelligence protocol which uses the output parameter list from an LPSVD algorithm, and automatically assigns the peaks on the basis of an anticipated list of components. To overcome the influence of experimental conditions on the absolute values of frequency, integrated area, and linewidth, the assignment routine performs an internal scaling of the data by comparing all possible pairs of peaks in the spectrum. Completely automated analysis of large numbers of in vivo FIDs is now possible.

Adenosine Triphosphate↗
PubMed2026

Application of Omics Technologies for Cowpea Improvement.

Cowpea (Vigna unguiculata) is a vital crop for food security, nutrition, and climate resilience in sub-Saharan African and other semi-arid regions. However, its improvement is constrained by the complexity of polygenic traits such as drought tolerance, pest resistance, and seed quality. Conventional breeding, while foundational, remains insufficient to address these challenges at the required pace. Recent advances in multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, provide new opportunities to dissect complex traits, identify candidate genes, and accelerate the development of resilient, high-yielding cultivars. This review presents a critical synthesis of current applications of omics technologies in cowpea improvement, highlighting their contributions to stress adaptation, nutritional enhancement, and precision breeding. The review also examines key technical and institutional constraints limiting the adoption of omics-assisted breeding in cowpea, including inadequate research infrastructure, challenges in multi-omics data integration, and limited technical capacity across breeding programs in sub-Saharan Africa. It discusses strategies to address these barriers through regional collaboration, investment in bioinformatics capacity, and the integration of computational approaches into breeding pipelines. Overall, the review concludes that combining multi-omics technologies with artificial intelligence and machine learning has strong potential to improve genotype-phenotype prediction, accelerate breeding decisions, and support the development of climate-resilient and nutritionally enhanced cowpea cultivars.

cowpea↗
PubMed2003

Pattern analysis of serum proteome distinguishes renal cell carcinoma from other urologic diseases and healthy persons.

Despite having a relatively low incidence, renal cell carcinoma (RCC) is one of the most lethal urologic cancers. For successful treatment including surgery, early detection is essential. Currently there is no screening method such as biomarker assays for early diagnosis of RCC. Surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF) is a recent technical advance that can be used to identify biomarkers for cancers. In this study, we investigated whether SELDI protein profiling and artificial intelligence analysis of serum could distinguish RCC from healthy persons and other urologic diseases (nonRCC). The SELDI-TOF data was acquired from a total of 36 serum samples with weak cation exchange-2 protein chip arrays and filtered using ProteinChip software. We used a decision tree algorithm c4.5 to classify the three groups of sera. Five proteins were identified with masses of 3900, 4107, 4153, 5352, and 5987 Da. These biomarkers can correctly separate RCC from healthy and nonRCC samples.

Biomarkers, Tumor↗
PubMed2026

Clinical Variable-Based Machine Learning for Predicting Early mCRPC Using Exclusively Clinical Variables: Development and Multicenter External Validation.

BACKGROUND AND OBJECTIVE: Metastatic hormone-sensitive prostate cancer (mHSPC) exhibits heterogeneous progression patterns, with early progression to metastatic castration-resistant prostate cancer (mCRPC) within 12 months indicating aggressive tumor biology and poor prognosis. Current risk stratification tools (CHAARTED, LATITUDE) offer limited individualized prediction. Machine learning approaches are increasingly applied to predict prostate cancer progression, but most models show modest performance (AUC 0.68-0.72), limited external validation, or require genomic variables unavailable in routine practice. This study aimed to develop and externally validate a novel RINH algorithm for predicting early mCRPC progression (≤ 12 months) using exclusively clinical variables, positioning it as a superior alternative to conventional ML classifiers. METHODS: This multicenter study enrolled 412 patients with de novo mHSPC from seven Spanish academic centers using mixed retrospective-prospective data collection. Twenty clinical variables were recorded, including demographics, PSA, ISUP grade, metastatic localization, CHAARTED/LATITUDE classifications, and treatment modalities. Following RINH-based outlier exclusion (55 patients), 357 patients (29 with early progression, 8.1%) were used to train six ML algorithms: RINH, Logistic Regression, Linear Discriminant, Support Vector Machine, Random Forest, and Subspace Discriminant. A two-tiered validation strategy integrated stratified fivefold cross-validation across all centers and formal external validation using center 1 (n = 121, 19 events) for training and centers 2-7 (n = 207, 10 events) for independent testing. Performance metrics included AUC, sensitivity, specificity, accuracy, and F1-score. KEY FINDINGS AND LIMITATIONS: Artificial intelligence and machine learning (ML) are transforming oncology, promising personalized risk stratification beyond traditional clinical criteria. In metastatic hormone-sensitive prostate cancer (mHSPC), early progression to castration resistance (mCRPC) within 12 months signals aggressive biology and poor prognosis, yet current tools (CHAARTED, LATITUDE) offer limited individualized prediction. Multiple ML models have been proposed with variable success: most achieve modest performance (AUC 0.68-0.72), lack robust external validation, or rely on genomic variables inaccessible in routine practice. We propose a novel approach using the Rivality Index Neighborhood (RINH) algorithm, demonstrating superior predictive capacity in an initial multicenter validation with exclusively clinical variables. This study provides rigorous multicenter external validation, advancing toward implementable precision oncology tools. CONCLUSIONS AND CLINICAL IMPLICATIONS: The RINH algorithm achieves superior predictive performance for early mCRPC progression using exclusively clinical variables, representing a significant advance toward implementable risk stratification. However, low reliability scores in external validation underscore that excellent performance metrics alone do not guarantee stability. Before clinical deployment, validation in substantially larger cohorts with higher progression events is essential. If validated, this model could enable personalized, risk-adapted therapeutic strategies, refining patient selection for treatment intensification or de-escalation.

Humans↗
PubMed1991

An expert system for inferring structures of organic compounds from their mass spectra.

Development of an 'expert system' for elucidation of structures of acyclic organic compounds is described. An expert system is a computer program that embodies some of the heuristic problem-solving knowledge of human experts so that it can effectively be used as an aid to decision making. The expert system described in this paper is intended to assist a chemist in arriving at plausible structures, the input data being the mass spectrum, molecular formula and presence (if known) of functional groups. The program generates chemically possible structures for the given molecular formula and can use, where available, the constraints imposed by the mass spectrum of the compound and by any known functional groups. The program makes use of a new algorithm for obtaining a canonical representation of structures and a new heuristic for incorporating constraints of the mass spectrum. This work constitutes a case study of the application of artificial intelligence techniques in chemistry and the material presented highlights this motivation.

Chemistry, Organic↗
PubMed2003

Approaches towards the automated interpretation and prediction of electrospray tandem mass spectra of non-peptidic combinatorial compounds.

Combinatorial chemistry is widely used within the pharmaceutical industry as a means of rapid identification of potential drugs. With the growth of combinatorial libraries, mass spectrometry (MS) became the key analytical technique because of its speed of analysis, sensitivity, accuracy and ability to be coupled with other analytical techniques. In the majority of cases, electrospray mass spectrometry (ES-MS) has become the default ionisation technique. However, due to the absence of fragment ions in the resulting spectra, tandem mass spectrometry (MS/MS) is required to provide structural information for the identification of an unknown analyte. This work discusses the first steps of an investigation into the fragmentation pathways taking place in electrospray tandem mass spectrometry. The ultimate goal for this project is to set general fragmentation rules for non-peptidic, pharmaceutical, combinatorial compounds. As an aid, an artificial intelligence (AI) software package is used to facilitate interpretation of the spectra. This initial study has focused on determining the fragmentation rules for some classes of compound types that fit the remit as outlined above. Based on studies carried out on several combinatorial libraries of these compounds, it was established that different classes of drug molecules follow unique fragmentation pathways. In addition to these general observations, the specific ionisation processes and the fragmentation pathways involved in the electrospray mass spectra of these systems were explored. The ultimate goal will be to incorporate our findings into the computer program and allow identification of an unknown, non-peptidic compound following insertion of its ES-MS/MS spectrum into the AI package. The work herein demonstrates the potential benefit of such an approach in addressing the issue of high-throughput, automated MS/MS data interpretation.

Automation↗
PubMed2026

Reassessing Semen Analysis: Clinical Insights Beyond Sperm Count and Motility.

BACKGROUND: Semen analysis (SA), recognized by the World Health Organization (WHO) as the cornerstone of male infertility evaluation, remains indispensable in reproductive medicine. However, advances in assisted reproductive technology (ART) and artificial intelligence (AI) have highlighted the limitations of relying solely on conventional semen parameters. OBJECTIVE: To critically review the evolving clinical role of SA by integrating conventional assessment with emerging functional, molecular, and computational approaches that improve diagnostic accuracy and individualized patient care. METHODS: A narrative review of contemporary evidence was conducted, focusing on conventional semen parameters, biofunctional sperm testing, omics technologies, AI-assisted analysis, and broader clinical applications of SA. RESULTS: Conventional parameters, including sperm concentration, motility, and morphology, remain essential but inadequately reflect fertilizing capacity. Adjunctive assessments, including oxidative stress biomarkers and sperm DNA fragmentation, provide valuable insights into sperm function and reproductive potential. Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, deepen mechanistic understanding, while AI enhances diagnostic precision, reproducibility, and standardization. Beyond infertility evaluation, SA also supports male contraceptive assessment, natural conception, ART, and patient counseling. CONCLUSIONS: Integrating conventional SA with functional, molecular, and AI-driven diagnostics provides a comprehensive framework for evaluating male fertility, advancing precision reproductive medicine and personalized clinical management.

cryopreservation↗
PubMed2026

Big data and psychiatry: advances, constraints and future directions.

Early work in psychiatry research, often involving single sites, small samples, and limited variables, has shifted to contemporary research involving multiple sites, large samples, and many variables. Such research raises important questions, including concerns about data quality and methodological rigor, uncertainty about its key lessons, issues regarding clinical relevance, and questions about how to optimize future advances. Here we consider these questions and concerns against the context of big data work on community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials. The development of large datasets allowing well-powered analyses is a major milestone, but sample size alone does not guarantee more precise estimates, and ongoing attention to the quality and rigor of big data collation and analysis is needed. Big data research has fostered trans-disciplinarity and given insights into mechanisms underlying psychiatric disorders, but also emphasizes the intricacy, heterogeneity and variability of such mechanisms, and the importance of triangulating between large-scale and small-scale research. The complexity of psychiatric phenotypes and psychobiological mechanisms contributes to the difficulty in bridging from big data to clinical application; big data research reinforces the importance of holding our diagnoses of psychiatric disorders lightly and providing explanations of these conditions humbly; and future work needs to be more attentive to clinical issues. There is enormous scope for further building databases relevant to psychiatry, but advances in conceptual models and asking the right questions are equally valuable. The full impact of big data, including artificial intelligence analyses, remains to be seen, but overenthusiastic support should be tempered by a better understanding of its strengths and limitations. At its best, such work will contribute in an iterative and integrative way to advancing our knowledge of psychiatric disorders and mental health.

Big data↗
PubMed1993

An evaluation of explanations of probabilistic inference.

Providing explanations of the conclusions of decision-support systems can be viewed as presenting inference results in a manner that enhances the user's insight into how these results were obtained. The ability to explain inferences has been demonstrated to be an important factor in making medical decision-support systems acceptable for clinical use. Although many researchers in artificial intelligence have explored the automatic generation of explanations for decision-support systems based on symbolic reasoning, research in automated explanation of probabilistic results has been limited. We present the results of an evaluation study of INSITE, a program that explains the reasoning of decision-support systems based on Bayesian belief networks. In the domain of anesthesia, we compared subjects who had access to a belief network with explanations of the inference results to control subjects who used the same belief network without explanations. We show that, compared to control subjects, the explanation subjects demonstrated greater diagnostic accuracy, were more confident about their conclusions, were more critical of the belief network, and found the presentation of the inference results more clear.

Bayes Theorem↗