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PubMed2027

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans↗

PubMed2026

Trustworthy Agentic AI in Bioinformatics: From Workflow Automation to Traceable and Validated Biological Inference.

Agentic artificial intelligence is extending bioinformatics beyond conversational assistance by enabling systems to select tools, execute code, revise analytical plans, and interpret biological data. These capabilities may accelerate research, but they also redistribute decisions that determine whether biological conclusions are valid. We conducted a targeted, structured PubMed search in July 2026 and identified 11 peer-reviewed agentic bioinformatics systems for descriptive review based on predefined eligibility criteria for analytical decision-making, tool or code execution, iterative evaluation, or coordinated agent activity. The evidence base covered single-cell transcriptomics, microbial genomics, cancer genomics, and omics applications, together with methodological literature on reproducibility and biological validation. We examined how current systems report delegated authority, provenance, validation, evidence, abstention, and human oversight. Existing platforms implement safeguards such as sandboxed execution, restricted commands, interaction logs, evidence identifiers, automated checks, critic agents, quality scores, and expert assessment. However, published reports rarely provide a connected account linking the original biological question to samples, reference resources, analytical decisions, computational actions, statistical results, supporting evidence, validation outcomes, and final claims. We distinguish inherited bioinformatics errors, errors amplified through autonomous action, and emergent failures arising from memory, retrieval, tool interaction, or agent coordination. We further propose a multidimensional decision-rights profile, consequence-sensitive validation gates, and a claim-to-evidence provenance architecture organized through the Traceable History of Research Evidence, Agent Actions, and Decisions in Bioinformatics (THREAD-Bio) framework. Illustrative cases show that technically successful execution may still support misleading inference. Trustworthy agentic bioinformatics therefore requires claims to remain reconstructible, challengeable, validated, and proportionate to the evidence.

accountable autonomy↗

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↗

PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗

PubMed2026

Genomic Screening for Infants and Reproductive Adults.

Recent progress in genomic sequencing, bioinformatics, cloud computation, and artificial intelligence is advancing a more mature understanding of the architecture of childhood genetic diseases. This knowledge and these technologies are enabling expanded genomic screening of infant and reproductive adult populations. With many new disease-modifying and curative therapies in development and approval processes, there exists unparalleled opportunity to identify, treat, and decrease the population burden of genetic disease and transform medical genetics. Broad implementation of genomic population screening, however, requires investments for overcoming remaining evidence gaps and operational challenges, and for delivery in a sustainable manner that is acceptable to parents, prospective parents, and physicians.

Journal Article↗

PubMed2026

Large language models in bioinformatics: a comprehensive survey.

The emergence of foundation models with trillion-level parameters has redefined the landscape of artificial intelligence. Various fields are developing their own large-scale models, which can solve many problems within the field and improve work efficiency. Biological large-scale models are a cross-disciplinary research field that combines mathematics, computer science, and biology, aiming to simulate and understand the structure, function, and dynamic changes of biological systems through the establishment of complex computational models. This field covers multiple levels such as biological pathways, population dynamics, protein folding, etc., providing us with tools for deep exploration of the mysteries of life and applications in medicine, ecology, and other fields. This article reviews the background and research status of biological large-scale models, and discusses future directions. Large language models (LLMs) and other large-scale foundation models have rapidly advanced in recent years, enabling powerful representation learning and generation across text, sequences, and multimodal data. In bioinformatics and biomedicine, these models are increasingly used to analyze genomic sequences, infer protein properties and structures, support drug discovery, and integrate heterogeneous biomedical evidence. This survey reviews the basic principles of LLMs and summarizes representative applications in (i) gene and genome sequence analysis, (ii) protein structure and function prediction, and (iii) drug design, including virtual screening and personalized medicine. We also discuss emerging multi-model modeling approaches, as well as key challenges such as data quality and privacy, interpretability, generalization to new organisms and tasks, and responsible deployment in health-related settings. Finally, we outline future directions for developing reliable, scalable, and explainable bioinformatics foundation models.

bioinformatics↗

PubMed2025

AI-HOPE: an AI-driven conversational agent for enhanced clinical and genomic data integration in precision medicine research.

MOTIVATION: The growing complexity of clinical cancer research has fueled a surge in demand for automated bioinformatics tools capable of integrating clinical and genomic data to accelerate discovery efforts. RESULTS: We present the Artificial Intelligence Agent for High-Optimization and Precision Medicine (AI-HOPE), an AI-driven system that enables domain experts to conduct integrative data analyses through natural language interactions. Powered by Large Language Models, AI-HOPE interprets user instructions, converts them into executable code, and autonomously analyzes locally stored data. It supports flexible association studies, subset comparisons, clinical prevalence assessments and survival analyses. In addition, AI-HOPE enables global variable scans to identify features significantly associated with a user-defined outcome, making a powerful and intuitive tool for advancing precision medicine research. Importantly, its closed-system design prevents clinical data leakage. To demonstrate its utility, AI-HOPE was applied to The Cancer Genome Atlas data to address two clinical questions. First, it identified significant enrichment of TP53 mutations in late-stage colorectal cancer compared to early-stage cases. Second, it uncovered a strong association between KRAS mutations and poorer progression-free survival in FOLFOX-treated patients. These findings align with established literature and demonstrate AI-HOPE's ability to generate meaningful insights independently, without prior assumptions. By removing programming barriers and simplifying complex analyses, AI-HOPE bridges the gap between data complexity and research needs. With its scalable and adaptable framework, AI-HOPE has the potential to support diverse biomedical research fields, driving innovation and efficiency in translational studies. AVAILABILITY AND IMPLEMENTATION: The AI-HOPE software and demonstration data is available at https://github.com/Velazquez-Villarreal-Lab/AI-HOPE.

Precision Medicine↗

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

Unveiling the Molecular Secrets of Seaweeds: A Comprehensive Review of Bioinformatics Applications in Algal Research.

Recent advances in high-throughput sequencing, bioinformatics, and multi-omics technologies have transformed seaweed research by overcoming long-standing challenges associated with complex genomes, diverse life cycles, and limited genomic resources. This review provides a comprehensive overview of bioinformatics approaches used to investigate seaweed genomics, transcriptomics, proteomics, metabolomics, microbiomes, and functional genomics, with emphasis on the computational tools and databases that support these analyses. Applications of bioinformatics in phylogenetics, drug discovery, microbiome characterization, and the development of biofuels, nutraceuticals, pharmaceuticals, and sustainable agriculture are also discussed. Particular attention is given to emerging strategies involving multi-omics integration, genome editing, artificial intelligence, machine learning, and synthetic biology that are reshaping seaweed research. The review further examines current challenges, including incomplete genomic resources, data standardization, and the need for experimental validation of computational predictions. Collectively, these advances highlight the growing role of bioinformatics in enabling systems-level understanding of seaweed biology and accelerating their translation into sustainable biotechnological and marine bioeconomy applications.

macroalgal genomics↗

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↗

PubMed2025

AutoPVPrimer: A comprehensive AI-Enhanced pipeline for efficient plant virus primer design and assessment.

Plant viruses pose a significant threat to global agriculture and require efficient tools for their timely detection. We present AutoPVPrimer, an innovative pipeline that integrates artificial intelligence (AI) and machine learning to accelerate the development of plant virus primers. The pipeline uses Biopython to automatically retrieve different genomic sequences from the NCBI database to increase the robustness of the subsequent primer design. The design_primers_with_tuning module uses a random forest classifier that optimizes parameters and provides flexibility for different experimental conditions. Quality control measures, including the evaluation of poly-X content and melting temperature, increase primer reliability. Unique to AutoPVPrimer is the visualize_primer_dimer module, which supports the visual evaluation of primer dimers-a feature missing in other tools. Primer specificity is validated via primer BLAST, which contributes to the overall efficiency of the pipeline. AutoPVPrimer has been successfully applied to the tomato mosaic virus, proving its adaptability and efficiency. The modular design allows customization by the user and extends the applicability to different plant viruses and experimental scenarios. The pipeline represents a significant advance in primer design and provides researchers with an effective tool to accelerate molecular biology experiments. Future developments aim to extend compatibility and incorporate user feedback to consolidate AutoPVPrimer as an innovative contribution to the bioinformatics toolbox and a promising resource for the advancement of plant virology research.

DNA Primers↗

PubMed2025

Unlocking microbial potential: advances in omics and bioinformatics for aromatic hydrocarbon degradation.

Aromatic hydrocarbons (AHs) are persistent environmental pollutants with high toxicity. Bacterial degradation of AHs provides a sustainable and cost-effective approach for the remediation of sites contaminated with both mono- and polycyclic aromatic hydrocarbons. Aerobic degradation of AHs typically involves oxygenases-mediated hydroxylation followed by aromatic ring cleavage. In contrast, anaerobic degradation relies on diverse activation mechanisms that ultimately converge on the central intermediate benzoyl-CoA. Over the past decades, research on bacterial degradation of AHs has grown steadily, supported by advances in omics and bioinformatics. In this review, we summarize the current knowledge on the pathways, enzymes, and microbial diversity involved in AH degradation, highlighting how omics and bioinformatic approaches are advancing our understanding of this process. However, to improve our knowledge of microbial AHs catabolism, it is crucial to prioritize the characterization of novel enzymes and pathways, especially those mediating anaerobic and hybrid degradation strategies. Addressing this gap requires the development of specialized resources that incorporate a broader taxonomic diversity and an expanded inventory of anaerobic genes and enzymes supported by experimental evidence. Equally important is the integration of multi-omics technologies, artificial intelligence, and ecological modeling into unified analytical pipelines. These efforts will be key to fully unlocking microbial metabolic potential and guiding more effective bioremediation and monitoring strategies for AHs.

Biodegradation, Environmental↗

PubMed2026

Imaging and genomics in stroke.

Imaging after ischemic and hemorrhagic stroke may allow measurement of key phenotypes of injury and recovery for which targeted therapies are still lacking. Such imaging endophenotypes provide quantifiable and heritable biomarkers that can represent mechanistic aspects of disease processes better than clinical measures. Artificial intelligence is allowing extraction of these imaging biomarkers in large cohorts, which can be paired with genomic and other omics data. This will allow the evaluation of what genetic and other biologic variations impact stroke injury and recovery. Integration of these analyses with bioinformatics tools (such as Mendelian randomization and multi-trait analysis) could further dissect how stroke complications overlap with other biologic processes and how they may be causally linked to risk factors. Further work is required to confirm the translational impact of these methods in elucidating mechanisms and drug targets for stroke. However, global collaborations are accelerating analyses on large multi-ethnic stroke cohorts, with availability of imaging data facilitated by federally-funded repositories such as the Imaging Repository for the Cerebrovascular Disease Knowledge Portal (iCDKP).

Humans↗

PubMed2026

Advancing One Health genomics in Africa: opportunities and challenges for outbreak and antimicrobial resistance control.

SUMMARYAfrica's ongoing struggles with emerging epidemics and antimicrobial resistance (AMR) underscore the urgency of integrating pathogen genomics and surveillance systems into the continent's One Health strategy, particularly given the existing limitations in preparedness and technological resources. This review brings together current evidence on the growth of sequencing infrastructure, the development of regional genomic hubs, and the establishment of governance frameworks, while identifying critical challenges in data integration, bioinformatics capacity, and sustainable financing. Special focus is placed on the lack of African-based genomic data, with our analysis showing that only 1.82% of the global total is available. Case studies illustrate the immense potential and importance of pathogen genomics, giving policymakers a tangible sense of its impact. These examples demonstrate how genomic technologies integrated with artificial intelligence (AI) are transforming outbreak response, AMR surveillance, and stewardship programs by enabling early detection of zoonotic threats, mapping transmission pathways, and guiding vaccine development. However, to fully realize this scientific intel, it is essential to embed One Health pathogen surveillance within strong policy and system frameworks to ensure the translation of technical progress into lasting institutional capacity and sustainable impact. Long-term implementation depends on coordinated investment and advocacy across four interdependent pillars: data architecture, governance and sovereignty, human capital, and technical capacity.

Humans↗

PubMed2026

Immunoinformatics Approach for Optimization of Targeted Vaccine Design: New Paradigm in Clinical Trials and Healthcare Management.

INTRODUCTION: The immunoinformatics approach combines bioinformatics and computational tools, offering a revolutionary method for improving vaccine development by analyzing immune responses at the molecular level. Immunoinformatics enables the creation of customized vaccines designed for specific infections or cancer cells. OBJECTIVE: The primary objective of immunoinformatics is to enhance the vaccine development process by predicting and boosting the body's immune response. It aims to identify potential immunogenic epitopes and biomarkers that are important for creating vaccines with greater specificity and efficacy, especially when dealing with large-scale data. METHODS: Immunoinformatics utilizes a combination of proteomic, genomic, and epigenomic data, as well as machine learning algorithms and artificial intelligence techniques. These tools predict how various immunological components, e.g., T-cell and B-cell epitopes, interact with the immune system. This approach allows researchers to avoid traditional trial-and-error methods, enabling the efficient identification of potential vaccine candidates. Additionally, personalized vaccines can be developed by considering individual genetic and immunological characteristics. RESULTS: The use of immunoinformatics techniques accelerates the screening of vaccine candidates, enhances patient stratification, and optimizes formulations for clinical trials. This approach has been shown to improve vaccine safety, efficacy, and development speed. It also holds promise for managing healthcare on a large scale by producing vaccines tailored to specific populations, thereby improving the overall effectiveness of vaccination programs. CONCLUSION: Immunoinformatics represents a transformative approach to vaccine research, improving clinical trial efficiency and enabling the development of more reliable, flexible, and personalized vaccines. This approach has the potential to significantly enhance global healthcare outcomes by accelerating the vaccine development process and optimizing vaccination strategies.

Immunoinformatics↗

PubMed2026

Molecular biology and integrated strategies for activating cryptic biosynthetic gene clusters toward next-generation antibiotic discovery.

Antimicrobial resistance (AMR) has been identified as one of the 21st century's severest global public health crises. AMR led to an estimated 4.95 million deaths in 2019 and will claim 10 million lives a year by 2050 in the absence of targeted interventions. During the same period, the number of novel antibiotics discovered has decreased drastically as many researchers are rediscovering known antibiotics, non-model microorganisms are poorly understood or difficult to culture and antibiotic research and development investment has declined drastically. However, high-throughput whole genome sequencing and the subsequent application of bioinformatics in bacterial and fungal genomes have shown that a numerous of cryptic or silent biosynthetic gene clusters (BGCs) remain latent at ambient laboratory conditions since their genes are transcriptionally inactive. Cryptic BGCs represent a vast source of unique secondary metabolites, many of which may yield novel antibacterial, antifungal, anti-cancer and other potentially valuable natural products. This review discusses the biological relevance of cryptic BGCs, the major limiting factors that restricts their activation and novel strategies that have been employed to activate them and exploit their potential to produce novel natural products. The review focuses on biological approaches including CRISPR-Cas mediation for the activation of cryptic BGCs, promoter engineering, pathway refactoring, and heterologous expression; biochemical strategies such as Osman, OsMAC, Precursor Feeding, Chemical Elicitation, Epigenetic Regulation and Co-cultivation and technology-based strategies such as Genome mining, Microfluidic Cultivation systems, High-Throughput Screening, Metabolomics, Molecular Networking and Artificial Intelligence and Machine Learning based prediction of BGCs and their metabolites. The use of multi-omics technologies combined with synthetic biology to achieve better discovery, characterization and large-scale production of novel natural products is also discussed herein. Finally, we will talk about the ecological significance and evolutionary advantage of cryptic BGCs' role in interactions between microorganisms, such as competition, communication, symbiosis and environmental adaptability, so as to provide a useful background for accelerating next-generation antibiotics.

CRISPR-Cas activation↗

PubMed2025

Multi-Omics and Integrative Analytics in Natural Products Discovery.

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Biological Products↗

PubMed2026

AI-Driven Precision Medicine in Alzheimer's Disease: Drug Repurposing, Digital Therapeutics and Clinical Decision Support.

Alzheimer's Disease (AD) is a neurodegenerative disease that causes significant clinical, social, and economic burden worldwide. Despite improvements in understanding its multifaceted pathogenesis, current treatments are mostly symptomatic and ineffective across varied patient populations. To overcome these constraints, AI-driven precision medicine allows tailored risk assessment, treatment selection, and disease monitoring. This review covers AI's role in AD precision medicine, focusing on drug repurposing, digital therapies and clinical decision support systems. Machine and deep learning models are used to predict medication response, integrate heterogeneous data sources such as genomics, transcriptomics, neuroimaging and electronic health records, and uncover pharmacogenomic treatment success factors. The paper covers AIenabled precision pharmacology, including tailored dosing algorithms, adaptive therapeutic monitoring, and adverse drug reaction prediction. Bioinformatics-based target identification, network pharmacology, graphbased AI models, virtual screening, and real-world and clinical data validation are emphasized in AI-driven medication repurposing. AI-powered digital treatments like personalized cognitive training platforms, wearable- derived digital biomarkers, virtual and mixed reality interventions, adherence monitoring, and digital twins for therapy optimization have been discussed. AI-based clinical decision support systems are also thoroughly assessed for clinical value, accuracy, and explainability in disease subtyping, trajectory prediction, and risk stratification in preclinical and prodromal AD. Despite these promises, data heterogeneity, algorithmic bias, legal barriers, and privacy concerns exist. Federated learning enables safe multi-center collaboration and hybrid AI-human approaches, and it represents the future. AI's ability to alter AD care opens the door to precision medicine paradigms that use repurposed medications, digital tools and intelligent decision-making to improve patient outcomes.

Alzheimer’s disease↗