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PubMed2005

Knowledge Discovery on Functional Disabilities: Clustering Based on Rules versus other Approaches.

In Europe senior citizens are a fast growing part of population, increasing proportion of disabled persons and that of persons with reduced quality of life. The concept of disability itself is not always precise and quantifiable. To improve agreement on it, the World Health Organization (WHO) developed the clinical test WHO Disability Assessment Schedule, (WHO-DASII) that includes physical, mental, and social well-being, as a generic measure of functioning. From the medical point of view, the purpose of this work is to extract knowledge about performance of the WHO-DASII using a sample of patients from an italian hospital.This Knowledge Discovery problem has been faced by using clustering based on rules, an hybrid AI and Statistics technique introduced by Gibert (1994), which combines some Inductive Learning (from AI) with clustering (from Statistics) to extract knowledge from certain complex domains in form of tipical profiles. In this paper, the results of applying this technique to the WHO-DASII results is presented together with a comparison of other more classical analysis approaches.

Cluster Analysis↗
PubMed2026

The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure-activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation drug development.

big data analytics↗
PubMed2005

Adjuvant endocrine therapy in postmenopausal women with early breast cancer: where are we now?

Tamoxifen has been the standard of care for adjuvant endocrine therapy of early breast cancer. In postmenopausal women, data now suggest that alternative agents (aromatase inhibitors [AIs]) may have improved long-term risk:benefit profiles and thus have the potential to improve outcome. The 'Arimidex', Tamoxifen, alone or in combination (ATAC) trial has shown that anastrozole provides improved disease-free survival (DFS) and time to recurrence, significantly reduced time to distant metastases and superior overall tolerability compared with tamoxifen when used as initial adjuvant therapy. Results have already led to a reconsideration of current recommendations for adjuvant therapy. Other ongoing trials include studies that are evaluating the benefits of sequencing of endocrine agents both within the standard 5-year adjuvant treatment period and as additional therapy in the post-adjuvant period. Three recently reported trials have suggested that switching from tamoxifen to an AI after 2-3 years of treatment leads to better outcomes than 5 years of tamoxifen. Finally, the NCIC MA 17 trial has shown that switching to an AI after 5 years of tamoxifen improves DFS compared with placebo. These are momentous discoveries that have improved our biological understanding and will inevitably change the management of breast cancer in the near future.

Anastrozole↗
PubMed2026

RP3Net: a deep learning model for predicting recombinant protein production in Escherichia coli.

MOTIVATION: Recombinant protein expression can be a limiting step in the production of protein reagents for drug discovery and other biotechnology applications. We introduce RP3Net (Recombinant Protein Production Prediction Network), an AI model of small-scale heterologous soluble protein expression in Escherichia coli. RP3Net utilizes the most recent protein and genomic foundational models. A curated dataset of internal experimental results from AstraZeneca and publicly available data from the Structural Genomics Consortium was used for training, validation and testing of RP3Net. RESULTS: RP3Net achieves an increase in area under the receiver operator curve (AUROC) of 0.15, compared to a baseline model. When experimentally validated on an independent, prospective, manually selected set of 97 constructs, RP3Net outperformed currently available models, with an AUROC of 0.83, delivering accurate predictions in 77% of the cases, and correctly identifying successfully expressing constructs in 92% of cases. AVAILABILITY AND IMPLEMENTATION: The model, along with installation and running instructions, is available under an MIT licence at https://github.com/RP3Net/RP3Net, DOI 10.5281/zenodo.17243498.

Escherichia coli↗
PubMedDate not supplied

Appreciative inquiry and leadership transitions.

In times of accelerated change accompanied by leadership transitions, appreciative inquiry (AI) and sensemaking skills are necessary. AI is a philosophy, a model of change, and a set of tools and techniques that support discovery, dreaming, design, and creation of a vision that inspires people in an organization to move toward a collective destiny. Sensemaking involves sizing up a situation to create a framework for decision-making, creating a context for communication, linking with others, and focusing on what is and what could be. Sensemaking can be facilitated by applying appreciative leadership techniques. In this article, the story of the University of Utah College of Nursing's and the faculty's experience with an AI process illustrates the application of the AI leadership strategy to navigating organizational change and a leadership transition.

Education, Nursing↗
PubMed2003

Niacin, lipids, and heart disease.

Niacin is the most effective medication in current clinical use for increasing high-density lipoprotein (HDL) cholesterol. It has the broadest effect on the lipid profile, reducing all atherogenic apolipoprotein (apo) B and increasing all antiatherogenic apo AI-containing lipoproteins, resulting in significant reduction in atherosclerotic complications and total mortality in trials. Recent research indicates novel major target sites of action in the liver to 1) directly inhibit diacylglycerol acyltransferase 2 (DGAT2), explaining its effect on triglycerides and apo B lipoproteins, and 2) inhibit the HDL apo AI catabolism pathway, resulting in higher HDL levels. Such information may lead to new drug discovery and supply the rationale for combination with other lipid regulators that are known to have different mechanisms of action. Trial evidence shows that niacin is not only safe to use in persons with diabetes, but that its combination with 3-hydroxy-3-methyl-glutaryl coenzyme A (HMG CoA) reductase inhibitors (statins) is also safe and effective. Recently, a new formulation of niacin has made it easier to tolerate and administer. Clinical trials are needed to determine whether niacin in combination with other lipid-modulating agents decreases the risk of cardiovascular events beyond the approximately 30% that has been noted with monotherapy.

Drug Therapy, Combination↗
PubMed2006

Identification of previously unrecognized antiestrogenic chemicals using a novel virtual screening approach.

The physiological roles of estrogen in sexual differentiation and development, female and male reproductive processes, and bone health are complex and diverse. Numerous natural and synthetic chemical compounds, commonly known as endocrine disrupting chemicals (EDCs), have been shown to alter the physiological effects of estrogen in humans and wildlife. As such, these EDCs may cause unanticipated and even undesirable effects. Large-scale in vitro and in vivo screening of chemicals to assess their estrogenic activity would demand a prodigious investment of time, labor, and money and would require animal testing on an unprecedented scale. Approaches in silico are increasingly recognized as playing a vital role in screening and prioritizing chemicals to extend limited resources available for experimental testing. Here, we evaluated a multistep procedure that is suitable for in silico (virtual) screening of large chemical databases to identify compounds exhibiting estrogenic activity. This procedure incorporates Shape Signatures, a novel computational tool that rapidly compares molecules on the basis of similarity in shape, polarity, and other bio-relevant properties. Using 4-hydroxy tamoxifen (4-OH TAM) and diethylstilbestrol (DES) as input queries, we employed this scheme to search a sample database of approximately 200,000 commercially available organic chemicals for matches (hits). Of the eight compounds identified computationally as potentially (anti)estrogenic, biological evaluation confirmed two as heretofore unknown estrogen antagonists. Subsequent radioligand binding assays confirmed that two of these three compounds exhibit antiestrogenic activities comparable to 4-OH TAM. Molecular modeling studies of these ligands docked inside the binding pocket of estrogen receptor alpha (ERalpha) elucidated key ligand-receptor interactions that corroborate these experimental findings. The present study demonstrates the utility of our computational scheme for this and related applications in drug discovery, predictive toxicology, and virtual screening.

Binding, Competitive↗
PubMed2026

Artificial intelligence for translational personalized neoantigen cancer vaccine development.

Personalized neoantigen cancer vaccine is a promising strategy for precision immunotherapy by targeting patient-specific and mutation-derived tumor antigens. Early clinical studies have demonstrated the feasibility, safety, and immunogenicity of these vaccines across multiple solid tumors, with encouraging outcomes particularly when combined with immune checkpoint blockade. However, broader clinical translation remains limited by sequential bottlenecks across the vaccine development pipeline, including false-positive neoantigen selection,  imperfect modeling of antigen processing and HLA presentation, limited prediction of T-cell receptor recognition, and challenges in formulation, delivery, and manufacturing. Artificial intelligence and advanced computational workflows are increasingly integrated into this pipeline to improve candidate prioritization and support more reproducible decision-making. In this review, we summarize clinical progress and key translational barriers in personalized neoantigen vaccination, and discuss how AI-enabled approaches may contribute across four major stages: multi-omics integration for neoantigen discovery, processing-aware HLA presentation prediction, structure-aware and TCR-informed immunogenicity modeling, and data-driven formulation optimization, particularly for lipid nanoparticle-based delivery systems. These approaches are able to help narrow biological and chemical search spaces, improve prioritization, and provide mechanistic insights into antigen presentation and immune recognition rather than replacing experimental validation. This articlefurther addresses future implementation challenges, including dataset diversity, model interpretability, prospective benchmarking, manufacturing traceability, and evolving regulatory frameworks for individualized mRNA cancer immunotherapies. Integrating computational innovation with rigorous immunological validation, scalable manufacturing, and regulatory oversight will be essential for advancing personalized neoantigen vaccines toward broader clinical implementation.

Cancer Vaccines↗
PubMed2026

AI-Based 3D Heterogeneous Network Model for Functional Prediction of Epigenetics.

Human biology and diseases are the result of constantly evolving processes within an intricately complex molecular network of interactions, such as epigenetic regulation. Epigenetics refers to heritable changes in gene expression that occur without alterations to the underlying DNA sequence. These changes, driven by mechanisms such as DNA methylation, histone modifications, and noncoding RNAs, play critical roles in regulating chromatin structure and gene activity. Epigenetic regulation offers valuable insights into biological systems, and when integrated with sophisticated analyses, it enables us to gain insights into gene regulation and cellular behavior. Here, we describe an artificial intelligence (AI)-based model that is capable of generating 3-dimensional (3D) heterogeneous network by integrating multimodal data for the functional prediction of epigenetic mechanisms, emphasizing its applications in medicine, developmental biology, and personalized therapeutics. Heterogeneous networks in biology are powerful tools for understanding the complex interactions and interdependencies within biological systems. Key advancements in AI and multiomics data integration have propelled this field, offering new insights into disease mechanisms, biomarker discovery, and therapeutic interventions.

Epigenesis, Genetic↗
PubMed2026

Exposome influences: a multi-omics perspective on the combined toxic effects of pharmaceuticals and personal care products in Alzheimer's disease.

According to WHO data, approximately 57 million people worldwide were affected by dementia in 2021, with prevalence projected to rise. Alzheimer's disease (AD), responsible for 60%-80% of dementia cases, continues to be a leading cause of mortality, with current treatments offering limited efficacy and disease-modifying therapies lacking widespread adoption or conclusive safety evidence, shifting the focus toward prevention and risk modification. Risk factors for AD include both non-modifiable elements, such as age, genetics, and gender, and modifiable factors, like environmental pollution, health status, and diet. While age remains the primary non-modifiable risk factor, early-onset dementia represents only up to 9% of cases. Addressing modifiable factors is essential, as it could prevent or delay almost half of dementia cases, with interventions-such as increased physical activity, smoking cessation, alcohol limitation, and overall health management-being significantly associated with a reduced risk. In this context, the exposome approach offers a comprehensive, integrative framework in which both modifiable and non-modifiable risk factors interact to influence individual susceptibility. Within the neural exposome, chronic low-dose exposure to xenobiotics-such as industrial chemicals, pesticides, metals, pharmaceuticals and personal care products (PPCPs), and air pollutants-may induce neurodegeneration via mechanisms including oxidative stress, neuroinflammation, proteinopathies, and epigenetic modifications, although establishing causality remains challenging. Integration of genomics, transcriptomics, proteomics, metabolomics, and lipidomics, combined with artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL), provides promising avenues for biomarker discovery, enhanced preventive strategies, early non-invasive diagnosis, and therapeutic target identification by integrating multi-layered biological data with exposure profiles. This review highlights emerging AD risk factors-including PPCPs-underscoring complex, multifactorial nature of AD and exposome, and the requirement for an interdisciplinary research approach, while also addressing several critical research gaps and methodological limitations.

Alzheimer’s disease↗
PubMed2002

Artificial intelligence techniques for bioinformatics.

This review provides an overview of the ways in which techniques from artificial intelligence (AI) can be usefully employed in bioinformatics, both for modelling biological data and for making new discoveries. The paper covers three techniques: symbolic machine learning approaches (nearest neighbour and identification tree techniques), artificial neural networks and genetic algorithms. Each technique is introduced and supported with examples taken from the bioinformatics literature. These examples include folding prediction, viral protease cleavage prediction, classification, multiple sequence alignment and microarray gene expression analysis.

Algorithms↗
PubMed2026

Beyond data and technology: the need for new thinking to enable the era of precision prevention.

BACKGROUND: Global flagship initiatives increasingly advocate for proactive health maintenance to alleviate the growing burden on reactive, disease-focused healthcare systems. Precision prevention is conceived as the targeted modulation of causal pathways across the disease continuum, from latent risk and pre-disease states to clinical manifestation, surpassing conventional public health prevention strategies that prioritise managing population-level risk factors. Traditional discovery and implementation models, however, remain poorly aligned with the pace and breadth of scientific and technological advances. This review outlines key barriers to scaling precision prevention and argues for the integration of conceptual, methodological, and policy perspectives into a single implementation‑oriented framework. MAIN: Individualised risk stratification lies at the core of precision prevention. Genomics serves as a stable substrate for lifetime susceptibility assessment, while meaningful prediction in multifactorial chronic disease requires additional risk monitoring using dynamic intermediate molecular markers and high-resolution exposomic data. Machine learning and other artificial intelligence (AI) methods are increasingly helpful tools for integrating large, heterogeneous and temporally structured real-world data to generate personalised predictions of health trajectories. Trustworthy AI-enabled risk prediction or decision-support systems are expected to provide transparency about model logic, assumptions and performance. In discovery, existing diagnostic classifications and conventional case-control designs can obscure mechanistic heterogeneity. Shifting toward precision phenotyping and biologically grounded disease redefinition could reveal a new layer of molecular understanding. Evidence generation strategies that reflect the temporal change of disease, including high‑risk enrichment, surrogate endpoints, and adaptive, trajectory-based monitoring, are particularly important for common conditions with prolonged latency periods (e.g., cancer, cardiovascular disease). Features often dismissed as "noise", such as stochastic molecular variation and minimal exposures, may in fact encode meaningful individual-level signals and thus merit investigation. CONCLUSION: To shift healthcare from reactive treatment toward proactive health maintenance requires coordinated action from stakeholders to reshape the pillars of discovery, reform outcome assessments and modernise implementation strategies.

Humans↗
PubMed2026

PAT: An Image Analysis Tool for Automated Scoring of Pollen in Alexander-Stained Anthers.

Quantitative pollen viability analysis is a critical but labor-intensive step in plant reproductive biology. Existing deep-learning Segment Anything Models (SAM) fail to reliably segment viable pollen in Alexander-stained anthers. To address this, we fine-tuned an existing Cellpose-SAM model for pollen segmentation. We integrated it into PAT (Pollen Analysis Tool), a cross-platform desktop application. PAT features instance segmentation with interactive quality control, an in-app model retraining module, and publication-ready statistical outputs. We deployed PAT in an EMS suppressor screen of semi-sterile Arabidopsis smg7-6 mutants, enabling efficient candidate prioritization for whole-genome sequencing and mapping of the candidate mutation. This screen led to the identification of a point mutation in CAP-D2 (capd2-2), a Condensin I subunit, that rescues the smg7-6 meiotic phenotype. Notably, mutation in a Condensin II subunits (CAP-D3 and CAP-H2) does not confer rescue. Further characterization suggests the capd2-2 allele is hypomorphic, showing no defects in vegetative growth, chromocenter compaction, or transposable element silencing. Collectively, we demonstrate that accessible AI tools have the potential to bridge gaps in plant phenotyping and accelerate the pace of biological discovery.

Alexander staining↗
PubMed2026

The mighty microproteins: from versatile cellular regulators to precision medicine therapeutics.

Microproteins, are tiny proteins encoded by small open reading frame (sORF), translation of these non-canonical open reading frames (ncORFs) has been implicated in diverse biological processes and diseases. This review summarizes recent developments in the discovery, biogenesis, and functional characterization of microproteins, and their involvement in various disease, with special focus on their roles in cancer, cardiovascular, metabolic, neurodegenerative and immune-related disorders. We emphasize the regulation of key cellular pathways by microproteins, including mitochondrial homeostasis, apoptosis, metabolic reprogramming, and immune signaling, all of which affect disease initiation and progression. Emerging evidence also supports their potential as disease biomarkers and therapeutic candidates for precision medicine. Finally, the review critically discusses the current challenges including discrepancies in microprotein annotation, the limitations of ribosome profiling and proteogenomic approaches, the gap between computationally predicted and experimentally validated microproteins, and the need for rigorous orthogonal validation by means of CRISPR-based genome editing, ribosome release assays, mutational analysis, high-resolution mass spectrometry, and functional studies. Finally, we review recent development of AI-assisted ORF prediction, single-cell translatomics, spatial proteomics, and integrated multi-omics as emerging technologies reshaping. Microprotein discovery and functional annotation. Finally, we discuss the translational potential of microproteins and highlight the remaining challenges to clinical application, including peptide stability, pharmacokinetics, tissue-specific delivery, immunogenicity, and the need for rigorous preclinical and clinical validation. Together, this review provides an updated and critical overview of the rapidly evolving microprotein field and highlights future research priorities for translating these molecules into clinically useful biomarkers and precision therapeutics.

Microproteins↗
PubMed2026

Mössbauer spectroscopy in drug discovery: revealing Fe- and Fe-S cluster dependent targets.

INTRODUCTION: Iron- and iron-sulfur cluster (Fe-S)-containing proteins are essential for diverse biological processes, including electron transfer, genome maintenance, metabolism, cellular signaling, and host-pathogen interactions. Despite their broad biological importance and growing links to human disease, Fe-S cluster-dependent proteins remain underexplored as therapeutic targets, largely because it is difficult to define their metal-dependent chemistry using conventional biochemical, spectroscopic, and structural approaches. AREAS COVERED: This review examines how Mössbauer spectroscopy can be integrated into workflows for metalloprotein characterization, target validation, and drug discovery. Using representative Fe-S cluster-containing proteins, the practical considerations for implementing Mössbauer spectroscopy are outlined, including 57Fe-enriched expression, sample preparation, and spectroscopic analysis. Two case studies of experimentally challenging viral Fe-S cluster proteins are then highlighted, the Hepatitis B virus X protein and the Porcine Reproductive and Respiratory Syndrome Virus Nsp1α protease, which demonstrate how direct characterization of metal cofactors can reveal previously unrecognized therapeutic avenues. Relevant literature published through March 2026 was identified using PubMed and Google Scholar with keywords related to Mössbauer spectroscopy, iron-sulfur proteins, viral metalloproteins, and drug discovery. EXPERT OPINION: As drug discovery increasingly seeks to exploit metal-dependent biology, Mössbauer spectroscopy will play an important role in identifying cryptic metalloproteins, defining their native states, and uncovering Fe- and Fe-S cluster-dependent targets. Mössbauer spectroscopy can also be complementary, and integrated with structural and AI-driven approaches to answer emerging challenges in medicinal chemistry.

Humans↗
PubMed2003

Rapid discovery and identification of a tissue-specific tumor biomarker from 39 human cancer cell lines using the SELDI ProteinChip platform.

Useful biomarkers are needed for early detection of cancers. To demonstrate the potential diagnostic usefulness of a new proteomic technology, we performed Expression Difference Mapping analysis on 39 cancer cell lines from 9 different tissues using ProteinChip technology. A protein biomarker candidate of 12kDa was found in colon cancer cells. We then optimized the purification conditions for this biomarker by utilizing Retentate Chromatography mass spectrometry (RC-MS). The optimized purification conditions developed "on-chip" were directly transferred to conventional chromatography to purify the biomarker, which was identified as prothymosin-alpha by ProteinChip time-of-flight mass spectrometry (TOF MS) and ProteinChip-Tandem MS systems. The relative expression level of prothymosin-alpha between colon cancer cells and normal colon mucosal cells was evaluated on the same ProteinChip platform. Prothymosin-alpha expression in colon cancer cells was clearly higher than in normal colon cells. These results indicate that prothymosin-alpha could be a potential biomarker for colon cancer, and that the ProteinChip platform could perform the whole process of biomarker discovery from screening to evaluation of the identified marker.

Biomarkers, Tumor↗
PubMed1991

Artificial intelligence methods for theory representation and hypothesis formation.

This article describes artificial intelligence methods for representing theories in molecular biology, and for improving the predictive power of these theories using experimental data. A program called GENSIM provides a framework for representing theories that includes descriptions of classes of biological objects (genes, enzymes, etc.), and processes that specify potential interactions among these objects (such as enzymatic reactions). GENSIM can employ a theory specified within this framework to predict the outcomes of biological experiments. A program called HYPGENE comes into play when the observed outcome of an experiment does not match the outcome predicted by GENSIM. HYPGENE works backward from the error in GENSIMs prediction to postulate changes to both the theory embodied by GENSIM, and the presumed initial conditions of the experiment. I view HYPGENEs hypothesis generation task as a design problem, and I have adapted AI methods developed for design and planning to this task. These techniques were developed in conjunction with an in-depth study of the discovery of the gene regulation mechanism of attenuation in the E. coli tryptophan operon. Both GENSIM and HYPGENE have been tested on sample problems from the history of attenuation, and produced many of the same solutions as biologists did.

Artificial Intelligence↗
PubMed2002

Target sites for herbicides: entering the 21st century.

At present the use-rate of modern herbicides is in the range of 100-300 g AI ha-1, with a tendency to decline. The low use-rate (ca 10 g AI ha-1) of the original sulfonylurea and cyclic imide herbicides prompted agrochemical scientists to look for even more active compounds which led to the successive discoveries of many new herbicidal acetolactate synthase inhibitors and no less than 18 cyclic imides in the class of protoporphyrinogen-IX oxidase inhibitors in the 1990s. In this paper, mechanisms of action related to function and biosynthesis of chlorophylls, carotenoids, plastoquinone, amino acids, fatty acids and photosynthetic electron transport and other metabolic processes are discussed as plant-specific herbicidal target domains.

Acetolactate Synthase↗