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Results for “generative artificial intelligence”

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PubMed2026

AI-genomics synergy for drug repurposing in breast cancer: an interpretability-driven framework.

Breast cancer's genomic heterogeneity complicates drug discovery, making repurposing an attractive but challenging strategy. Advances in artificial intelligence now enable integration of multi-omics data to reveal drug-gene-disease relationships and generate subtype-specific repurposing hypotheses. In this Review, we examine AI-driven computational approaches from signature-based to multi-modal frameworks and propose an integrated interpretability-driven framework linking mechanistic validation with clinical translation toward more transparent and actionable precision oncology.

Journal Article↗

PubMed2025

Smarter stomata: emergent technologies unlocking yield potential in a changing climate.

Stomata, the gatekeepers of leaf gas exchange, regulate carbon dioxide uptake and water loss, functions increasingly critical as crops face more frequent, intense heat and drought. Under dry conditions, stomatal conductance (g s) typically decreases, limiting carbon assimilation and yield. Heat stress, in contrast, elicits variable g S responses: sometimes increasing to facilitate transpirational cooling, while at other times decreasing, especially when combined with drought. Heat and drought also induce complex, context-dependent shifts in stomatal anatomy. Smaller, denser stomata improve drought resilience in some cases, while reduced density confers greater tolerance in others. The optimal stomatal ideotype remains unknown, and different or even opposing traits may confer resilience dependent on the environmental scenario. Substantial genotypic variation in g s and stomatal anatomy, high heritability and co-localized quantitative trait loci for stomatal traits and yield highlight their untapped potential as breeding targets for climate-resilient crops. However, stomatal traits remain largely absent from breeding pipelines due to challenges of phenotyping at scale. This is changing rapidly. Advances in deep learning, porometry, digital microscopy, and remote sensing now enable high-throughput measurement of stomatal physiology and anatomy. Next-generation breeding technologies including clustered regularly interspaced short palindromic repeats (CRISPR), multi-omics approaches, and artificial intelligence-driven ideotype selection models could revolutionize breeding, allowing precise engineering of stomatal traits for resilience to environmental stress. The time has come to move beyond characterizing stomatal traits and start actively incorporating them into breeding strategies. By leveraging these technologies, stomatal traits can become high value targets, unlocking their potential to enhance crop performance in a hotter, drier future.

abiotic stress↗

PubMed2026

Plant cis-regulatory grammar: Decoding the multidimensional code of transcriptional regulation for programmable crop engineering.

Cis-regulatory elements (CREs) orchestrate the spatiotemporal precision of gene expression that underlies plant development, adaptation, and domestication. Decoding the cis-regulatory grammar of plant genomes remains a central challenge in modern biology, with profound implications for programmable crop engineering. Here, recent conceptual and technological advances are synthesized to reshape our understanding of plant CREs. This review first argues that CRE function is not only an intrinsic property of DNA sequence alone but also emerges from a multidimensional context, including chromatin accessibility, histone modifications, three-dimensional genome topology, and cell type-specific regulatory landscapes. Furthermore, the convergence of single-cell epigenomics, high-throughput functional assays, and CRISPR-based dissection has begun to unravel this contextual grammar, revealing the computational principles governing transcriptional regulation. Critically, we propose that artificial intelligence (AI) platforms are catalyzing an ongoing transition from descriptive discovery to predictive engineering, wherein these platforms outperform natural evolution in designing synthetic CREs. Finally, a roadmap is outlined toward a plant regulatory grammar foundation model, which will enable truly predictive engineering of gene expression when fine-tuned for specific tasks. Collectively, the integration of single-cell resolution maps, precise genome editing, AI-driven design, and regulatory-compliant delivery systems promises to transform our ability to reprogram plant gene regulation for next-generation agriculture, bridging the gap between foundational regulatory biology and tangible crop improvement.

artificial intelligence↗

PubMed1992

Applications of rule-induction in the derivation of quantitative structure-activity relationships.

Recently, methods have been developed in the field of Artificial Intelligence (AI), specifically in the expert systems area using rule-induction, designed to extract rules from data. We have applied these methods to the analysis of molecular series with the objective of generating rules which are predictive and reliable. The input to rule-induction consists of a number of examples with known outcomes (a training set) and the output is a tree-structured series of rules. Unlike most other analysis methods, the results of the analysis are in the form of simple statements which can be easily interpreted. These are readily applied to new data giving both a classification and a probability of correctness. Rule-induction has been applied to in-house generated and published QSAR datasets and the methodology, application and results of these analyses are discussed. The results imply that in some cases it would be advantageous to use rule-induction as a complementary technique in addition to conventional statistical and pattern-recognition methods.

Algorithms↗

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↗

PubMedDate not supplied

An artificial intelligence approach to DNA sequence feature recognition.

The ultimate goal of the Human Genome project is to extract the biologically relevant information recorded in the estimated 100,000 genes encoded by the 3 x 10(9) bases of the human genome. This necessitates development of reliable computer-based methods capable of analysing and correctly identifying genes in the vast amounts of DNA-sequence data generated. Such tools may save time and labour by simplifying, for example, screening of cDNA libraries. They may also facilitate the localization of human disease genes by identifying candidate genes in promising regions of anonymous DNA sequence.

Artificial Intelligence↗

PubMed1976

Towards the simulation of clinical cognition. Taking a present illness by computer.

Remarkably little is known about the cognitive processes which are employed in the solution of clinical problems. This paucity of information is probably accounted for in large part by the lack of suitable analytic tools for the study of the physician's thought processes. Here we report on the use of the computer as a laboratory for the study of clinical cognition. Our experimental approach has consisted of several elements. First, cognitive insights gained from the study of clinicians' behavior were used to develop a computer program designed to take the present illness of a patient with edema. The program was then tested with a series of prototypical cases, and the present illnesses generated by the computer were compared to those taken by the clinicians in our group. Discrepant behavior on the part of the program was taken as a stimulus for further refinement of the evolving cognitive theory of the present illness. Corresponding refinements were made in the program, and the process of testing and revision was continued until the program's behavior closely resembled that of the clinicians. The advances in computer science that made this effort possible include "goal-directed" programming, pattern-matching and a large associative memory, all of which are products of research in the field known as "artificial intelligence". The information used by the program is organized in a highly connected set of associations which is used to guide such activities as checking the validity of facts, generating and testing hypotheses, and constructing a coherent picture of the patient. As the program pursues its interrelated goals of information gathering and diagnosis, it uses knowledge of diseases and pathophysiology, as well as "common sense", to dynamically assemble many small problem-solving strategies into an integrated history-taking process. We suggest that the present experimental approach will facilitate accomplishment of the long-term goal of disseminating clinical expertise via the computer.

Computers↗

PubMed2026

Metal-Organic Framework-Based and Metal-Organic Framework-Derived Nanomaterials for Cancer Theranostics and Antibacterial Applications: Advances, Challenges, and Perspectives.

Metal-organic frameworks (MOFs), constructed through coordination-driven self-assembly of metal ions/clusters and organic linkers, have emerged as a uniquely versatile class of porous nanomaterials with broad biomedical potential. Despite substantial clinical progress, both oncological treatment and antimicrobial intervention remain constrained by inadequate tumor-targeting selectivity, multidrug resistance, immunosuppressive tumor microenvironments, and the global proliferation of antibiotic-resistant pathogens, limitations that conventional nanocarrier platforms have addressed only in part. MOF-based and MOF-derived nanomaterials, distinguished by tunable pore architecture, structurally and compositionally adaptable metal nodes, high surface areas, and stimulus-responsive degradability, offer a rational framework for overcoming these barriers. This review systematically examines the synthetic strategies underlying MOF-based and MOF-derived nanomaterials, including pyrolysis, chemical etching, composite modification, and functional group introduction, and their structural determinants of performance. In cancer theranostics, we critically evaluate their roles as multimodal imaging contrast agents, stimulus-responsive drug delivery carriers, and platforms for combination therapies encompassing photodynamic, photothermal, chemodynamic, and immunomodulatory modalities. In antibacterial applications, we analyze the mechanistic basis of MOF-based and MOF-derived activity, including physical membrane disruption, reactive oxygen species-mediated oxidative stress, and sustained metal ion release, alongside strategies targeting biofilm formation and antibiotic resistance. Multifunctional platforms that concurrently integrate cancer theranostic and antibacterial capabilities are further discussed. This review also addresses the principal barriers to clinical translation, encompassing large-scale manufacturing, long-term biosafety, and regulatory approval, and proposes future directions incorporating artificial intelligence-assisted design and materials genomics, underscoring the transformative potential of MOF-based and MOF-derived nanomaterials as next-generation precision nanomedicines. This review establishes a unified mechanistic framework grounded in the intrinsic physicochemical properties of MOF-derived nanomaterials, systematically integrating their applications in cancer theranostics and antibacterial therapy. Critically, it bridges fundamental advances with translational reality by incorporating a rigorous assessment of regulatory pathways, scalable manufacturing constraints, and clinical implementation barriers, and offers a comprehensive, practice-oriented reference for the rational design and responsible translation of MOF-based and MOF-derived nanomaterials.

Theranostic Nanomedicine↗

PubMed2026

Artificial Intelligence for Natural Products Discovery and Development.

Natural products (NPs) remain a cornerstone of modern drug discovery, offering stereochemical complexity and diverse bioactivities that precisely modulate therapeutic targets, refined through billions of years of evolution. However, their research has long been hindered by inefficient, empirical workflows, high resource consumption, structural complexity, and the "multicomponent, multi-target" nature of their mechanisms. The exponential growth of genomic, metabolomic, and spectral data has overwhelmed conventional analytical methods, exposing critical bottlenecks in handling high-dimensional, heterogeneous datasets that exceed human interpretive capacity. Artificial intelligence (AI) is emerging as a transformative paradigm to address these challenges, integrating multi-omics and chemical data to shift NP research from fragmented empiricism toward mechanism-driven, precision-oriented development. By leveraging deep learning architectures- including graph neural networks, Transformers, and diffusion-based generative models-AI enables systematic decoding of NP biosynthesis, automated structure elucidation, rational target identification, knowledge extraction from vast unstructured scientific literature, and de novo molecular design. This review comprehensively surveys recent advances in AI applications across the full NP discovery and development pipeline, encompassing genome mining, structure-based and ligand-based virtual screening, multimodal structural characterization, lead optimization, and biosynthetic pathway engineering. We further examine the emerging roles of protein-centric, molecule- centric, and multimodal foundation models, as well as large language models, in bridging genotype-to-chemotype gaps and unlocking unstructured scientific knowledge. Finally, we discuss critical challenges including data scarcity, representational limitations for complex stereochemistry, physical plausibility in generative models, and the urgent need for experimental validation, while outlining future directions toward autonomous experimentation, closed-loop optimization, and human-AI collaborative discovery.

Artificial intelligence↗

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

AI-integrated digital breeding for crop improvement.

Crop breeding increasingly depends on the effective integration and interpretation of large, heterogeneous datasets spanning genomic, phenotypic, multi-omics, and environmental layers. Conventional breeding approaches are often insufficient to capture the complex relationships among these data or to support timely selection decisions. Digital breeding can help address this limitation by complementing field experimentation, mixed models, and genomic prediction with the integration of biological data and computational prediction throughout the breeding process. In particular, the rapid advancement of artificial intelligence (AI) has improved the analysis of high-dimensional datasets and broadened its application to trait prediction, selection, and breeding design. Here, we review recent developments in AI-enabled digital breeding, encompassing genomic, phenomic, and multi-omics data generation and analysis, predictive modeling, explainable and generative AI, and data-driven breeding decision support. We further discuss emerging AI applications, their current contributions to crop research and breeding, and the major considerations affecting their reliable and practical implementation. Collectively, this review provides a structured understanding of the roles of AI across the digital breeding process and offers guidance for future methodological development and practical application in crop improvement.

artificial intelligence↗

PubMed2026

AI-Driven Multi-Omics Integration of Synthetic Colon Adenocarcinoma for Cluster-Guided PROTAC Candidate Design Targeting KRASG12D.

Colorectal cancer is a leading cause of cancer death, yet its molecular heterogeneity remains poorly translated into individualized treatment. We present a reproducible artificial intelligence (AI) framework that integrates multi-omics benchmarking, sample-level drug prioritization, E3 ubiquitin ligase selection, and shape-anchored Proteolysis Targeting Chimera (PROTAC) design for KRASG12D in colon adenocarcinoma (COAD). A controlled synthetic benchmark comprising 425 tumor and 41 simulated normal profiles, parameterized to match The Cancer Genome Atlas (TCGA) distributions, was used for pipeline verification. Among sixteen methods, the Balanced Latent Integration with Stability Selection (BLISS) model achieved the highest silhouette width (0.86) and competitive agreement (Adjusted Rand Index, ARI, 0.90). The pipeline was validated on real data: a TCGA COAD cohort (186 tumors) with independent Consensus Molecular Subtype (CMS) labels and a CPTAC cohort (104 tumors). Integration modestly recovered CMS (ARI 0.28), and stage, not molecular cluster, drove survival (log-rank p = 0.005 versus 0.81). Sample-level prioritization differed from cluster-level ranking in 82.6% of profiles, below chance (p < 0.0001), without indicating efficacy. Candidate NOVEL00489 showed a good MM-GBSA estimate, matching the reference ASP3082. Compounds are computational candidates requiring experimental validation. This establishes a transparent benchmark for in silico degrader generation in precision oncology.

Humans↗

PubMed2025

Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients.

The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity-including financial and time-are important goals of modern oncology. The Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine founded by Drs. John Mendelsohn and Thomas Tursz provided a vision for innovation, collaboration and global impact in precision oncology. Through pursuit of transcriptomic signatures, artificial intelligence (AI) algorithms, global precision cancer medicine clinical trials and input from an international Molecular Tumor Board (MTB), WIN has led the way in demonstrating patient benefit from precision-therapeutics through N-of-1 molecularly-driven studies. WIN Next-Generation Precision Oncology (WINGPO) trials are being developed in the neoadjuvant, adjuvant or metastatic settings, incorporate real-world data, digital pathology, and advanced algorithms to guide MTB prioritization of therapy combinations for a diverse global population. WIN has pursued combinations that target multiple drivers/hallmarks of cancer in individual patients. WIN continues to be impactful through collaboration with industry, government, sponsors, funders, academic and community centers, patient advocates, and other stakeholders to tackle challenges including drug access, costs, regulatory barriers, and patient support. WIN's collaborative next generation of precision oncology trials will guide treatment selection for patients with advanced cancers through MTB and AI algorithms based on serial liquid and tissue biopsies and exploratory omics including transcriptomics, proteomics, metabolomics and functional precision medicine. Our vision is to accelerate the future of precision oncology care.

Humans↗

PubMed2026

Artificial intelligence-derived myocardial fibrosis on cardiac magnetic resonance for prognosis in cardiomyopathy: A systematic review of a sparse evidence base.

BACKGROUND: Myocardial fibrosis on cardiovascular magnetic resonance (CMR), assessed by late gadolinium enhancement (LGE) and parametric mapping, is an established predictor of adverse events in cardiomyopathy. We assessed whether artificial intelligence (AI) quantification of fibrosis adds independent prognostic value. METHODS: We searched six databases, a clinical-trials register, and a preprint server from inception to 13 June 2026. Eligible studies used AI to generate a fibrosis marker in adults with ischemic or nonischemic cardiomyopathy, with covariate-adjusted outcomes over ≥12 months. Risk of bias was assessed using PROBAST, PROBAST+AI, and QUIPS. Fewer than three comparable studies precluded meta-analysis; certainty was rated using GRADE. RESULTS: Of 448 records (381 after de-duplication), 18 full texts were reviewed and two included, one peer-reviewed and one preprint. In an ischemic-cardiomyopathy registry (Ghanbari et al.; n = 216 analytic, 26 events), AI-derived dense LGE scar predicted arrhythmic events (univariable hazard ratio [HR] 2.35, 95% CI 1.33-4.15), and AI-derived but not manual scar improved discrimination beyond guideline criteria (area under the curve 0.63 to 0.68; p = 0.02). In a nonischemic dilated-cardiomyopathy preprint (Kim et al.; n = 347, 119 events), automated extracellular volume ≥30% predicted cardiovascular death or heart-failure hospitalization (adjusted HR 2.00, 95% CI 1.32-3.03). Both were at high risk of bias, with data-derived thresholds and no external validation. CONCLUSIONS: Across only two studies, AI-derived fibrosis was independently associated with adverse cardiovascular events, but its added value over manual quantification remains unproven. Certainty was very low. The evidence base is sparse and not yet ready for clinical use.

Humans↗

PubMed1992

Neural computing in discovering RNA interactions.

High-order RNA structures are involved in regulating many biological processes; various algorithms have been designed to predict them. Experimental methods to probe such structures and to decipher the results are tedious. Artificial intelligence and the neural network approach can support the process of discovering RNA structures. Secondary structures of RNA molecules are probed by autoradiographing gels, separating end-labeled fragments generated by base-specific RNases. This process is performed in both conditions, denaturing (for sequencing purposes) and native. The resultant autoradiograms are scanned using line-detection techniques to identify the fragments by comparing the lines with those obtained by 'alkaline ladders'. The identified paired bases are treated by either one of two methods to find the foldings which are consistent with the RNases' 'cutting' rules. One exploits the maximum independent set algorithm; the other, the planarization algorithm. They require, respectively, n and n2 processing elements, where n is the number of base pairs. The state of the system usually converges to the near-optimum solution within about 500 iteration steps, where each processing element implements the McCulloch-Pitts binary neuron. Our simulator, based on the proposed algorithm, discovered a new structure in a sequence of 38 bases, which is more stable than that formerly proposed.

Algorithms↗

PubMed2026

Engineering extracellular vesicles for targeted siRNA delivery: Advances, therapeutic applications, and clinical translation.

Small interfering RNA (siRNA) therapeutics have emerged as a transformative approach for sequence-specific gene silencing, offering the potential to treat a broad spectrum of diseases by selectively suppressing disease-associated genes. However, the clinical translation of siRNA remains limited by rapid enzymatic degradation, poor cellular uptake, inadequate endosomal escape, and off-target effects, necessitating the development of efficient delivery systems. Extracellular vesicles (EVs) have gained considerable attention as natural nanocarriers owing to their excellent biocompatibility, low immunogenicity, intrinsic targeting capability, and ability to protect therapeutic cargo while traversing complex biological barriers. This review comprehensively discusses the biological characteristics of EVs, the molecular basis of RNA interference, and the major challenges associated with siRNA delivery [Fig. 1]. Recent advances in EV engineering, including cargo-loading strategies such as electroporation, sonication, extrusion, parent-cell engineering, and microfluidic approaches, together with surface functionalization using peptides, antibodies, aptamers, and hybrid nanoplatforms, are critically evaluated for improving targeting specificity and intracellular delivery. Furthermore, the therapeutic applications of engineered EV-mediated siRNA delivery in cancer, neurological disorders, liver diseases, cardiovascular diseases, inflammatory disorders, and infectious diseases are systematically summarized, highlighting their potential to enhance gene silencing while minimizing systemic toxicity. Current challenges related to large-scale manufacturing, cargo-loading efficiency, standardization, quality control, regulatory approval, and clinical translation are also discussed, together with emerging technologies involving synthetic biology, genome engineering, artificial intelligence, and multifunctional hybrid vesicles. Overall, engineered extracellular vesicles represent a highly versatile and biologically inspired platform for targeted siRNA delivery, providing a promising foundation for the development of next-generation precision RNA therapeutics and accelerating the clinical translation of gene-silencing strategies.

Extracellular vesicle engineering↗

PubMed2026

Biomarker-guided selection of intravesical therapy in high-risk non-muscle invasive bladder cancer: A contemporary review.

High-risk non-muscle invasive bladder cancer poses therapeutic challenges, with significant rates of recurrence and progression with standard intravesical bacillus Calmette-Guérin (BCG) therapy. Current surveillance strategies lack accurate risk stratification models to predict individual treatment response and personalized treatment options. Simultaneously, there are no well-validated alternatives to replace the current gold-standard approach based on clinical and pathologic features. This review examines emerging biomarkers and advanced technologies with the potential to enhance patient selection and personalize intravesical therapy in HR-NMIBC. Artificial intelligence(AI)-driven histopathologic tools, such as the computer histological AI biomarker, have demonstrated the ability to identify non-responders to standard therapy using whole-slide digital pathology images. In parallel, radiomics-enhanced imaging has shown promise in assessing tumor biology and immune microenvironment features predictive of BCG responsiveness. Liquid biopsy, especially urine tumor DNA analysis, is now available in the arsenal to detect minimal residual disease, stratify recurrence risk, and predict treatment response even before clinical or radiographic evidence of recurrence. Tissue-based genomic profiling has also revealed molecular alterations associated with treatment resistance, though additional validation is needed. Together, these next-generation biomarkers may represent a pivotal shift toward precision oncology in bladder cancer and their incorporation into NMIBC future clinical guidelines is both anticipated and necessary.

BCG-unresponsive disease↗

PubMed2026

Immunopeptidomics-guided cancer vaccine design: Advances, challenges, and emerging opportunities.

Selecting clinically relevant tumor antigens remains a major challenge in the development of therapeutic cancer vaccines. Although computational approaches have considerably improved neoantigen prediction, many candidate epitopes identified in silico are not ultimately presented on the tumor cell surface. The emergence of immunopeptidomics has provided direct access to naturally processed HLA-associated peptides and has offered new opportunities for antigen discovery. Increasing evidence has shown that information derived from the immunopeptidome becomes considerably more informative when interpreted alongside genomic, transcriptomic, and proteomic data. This integrative view has broadened the spectrum of targetable antigens and has also revealed important limitations related to peptide abundance, HLA diversity, tumor heterogeneity, and the imperfect relationship between antigen presentation and immunogenicity. These issues have renewed interest in multi-antigen vaccine strategies designed to better reflect the complexity of tumor antigen landscapes. Advances in bioinformatics and artificial intelligence are facilitating the interpretation of increasingly complex datasets and are beginning to support more systematic approaches to antigen prioritization. In this review, we discuss how immunopeptidomics is contributing to next-generation cancer vaccine development, summarize the major translational challenges, and highlight emerging concepts that may improve the clinical applicability of immunopeptidomics-guided immunotherapy.

Cancer immunotherapy↗