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PubMed2025

Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives.

Healthcare systems worldwide face growing challenges, including rising costs, workforce shortages, and disparities in access and quality, particularly in low- and middle-income countries. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these issues by enhancing diagnostics, treatment planning, patient monitoring, and healthcare efficiency. AI's role in modern medicine spans disease detection, personalized care, drug discovery, predictive analytics, telemedicine, and wearable health technologies. Leveraging machine learning and deep learning, AI can analyze complex data sets, including electronic health records, medical imaging, and genomic profiles, to identify patterns, predict disease progression, and recommend optimized treatment strategies. AI also has the potential to promote equity by enabling cost-effective, resource-efficient solutions in low-resource and remote settings, such as mobile diagnostics, wearable biosensors, and lightweight algorithms. Successful deployment requires addressing critical challenges, including data privacy, algorithmic bias, model interpretability, regulatory oversight, and maintaining human clinical oversight. Emphasizing scalable, ethical, and evidence-driven implementation, key strategies include clinician training in AI literacy, adoption of resource efficient tools, global collaboration, and robust regulatory frameworks to ensure transparency, safety, and accountability. By complementing rather than replacing healthcare professionals, AI can reduce errors, optimize resources, improve patient outcomes, and expand access to quality care. This review emphasizes the responsible integration of AI as a powerful catalyst for innovation, sustainability, and equity in healthcare delivery worldwide.

Humans↗
PubMed2025

Out-of-the-box bioinformatics capabilities of large language models (LLMs).

Large Language Models (LLMs), AI agents and co-scientists promise to accelerate scientific discovery across fields ranging from chemistry to biology. Bioinformatics- the analysis of DNA, RNA and protein sequences plays a crucial role in biological research and is especially amenable to AI-driven automation given its computational nature. Here, we assess the bioinformatics capabilities of three popular general-purpose LLMs on a set of tasks covering basic analytical questions that include code writing and multi-step reasoning in the domain. Utilizing questions from Rosalind, a bioinformatics educational platform, we compare the performance of the LLMs vs. humans on 104 questions undertaken by 110 to 68,760 individuals globally. GPT-3.5 provided correct answers for 59/104 (58%) questions, while Llama-3-70B and GPT-4o answered 49/104 (47%) correctly. GPT-3.5 was the best performing in most categories, followed by Llama-3-70B and then GPT-4o. 71% of the questions were correctly answered by at least one LLM. The best performing categories included DNA analysis, while the worst performing were sequence alignment/comparative genomics and genome assembly. Overall, LLMs performance mirrored that of humans with lower performance in tasks in which humans had low performance and vice versa. However, LLMs also failed in some instances where most humans were correct and, in a few cases, LLMs excelled where most humans failed. To the best of our knowledge, this presents the first assessment of general purpose LLMs on basic bioinformatics tasks in distinct areas relative to the performance of hundreds to thousands of humans. LLMs provide correct answers to several questions that require use of biological knowledge, reasoning, statistical analysis and computer code.

Journal Article↗
PubMed2025

BioMedGraphica: An All-in-One Platform for Joint Textual Biomedical Prior Knowledge and Numeric Graph Generation.

Multi-omic data analysis is essential for scientific discovery in precision medicine. However, translating statistical results of omic data analysis into novel scientific hypothesis remains a significant challenge. Human experts must manually review analysis results and generate new hypothesis based on extensive and inter-connected biomedical prior knowledge, which is subjective and not scalable. While large language models (LLMs) can accelerate the discovery, their reasoning improves when grounded in structured, auditable and comprehensive biomedical prior knowledge. Biomedical knowledge, however, is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of AI systems to fully leverage biomedical data for scientific discovery. To address these challenges, we developed BioMedGraphica , an all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified knowledge graph containing 2,306,921 entities and 27,232,091 relations. In addition, to the best of our knowledge, this is the first work to propose a novel Textual-Numeric Graph (TNG) data-structure for multi-omics data analysis. In TNG, textual information captures prior biological knowledge (e.g., transcription start sites, functions, mechanisms), while numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data-structure for the development of graph foundation models, with the potential to improve prediction performance and interpretability, while also augmenting LLMs by supplying graph-structured mechanistic context to strengthen reasoning. The details for BioMedGraphica code can be accessed by github link: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph data can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

biomedical knowledge graph↗
PubMed2006

Aromatase inhibitors and breast cancer.

Oestrogens are involved in risk to, and progression of, breast cancer. Drugs that inhibit the production of oestrogens (aromatase inhibitors, AIs), are therefore extremely attractive strategies both to prevent the disease and to treat established tumours. AIs now have a central role in the treatment of established breast cancer and are being considered for prevention. Third generation agents have been derived from rational drug design. They are able to block oestrogen production and reduce oestrogen levels to a degree that has not been observed previously and without affecting levels of other hormones. Such selective reduction of endogenous oestrogen provides targeted therapy for hormone-dependent breast cancer. This has led to improved clinical benefits in patients with these tumours. Anastrozole, letrozole and exemestane all have impressive antitumour effects in postmenopausal women with breast cancer and they are at least as beneficial as or better than other established endocrine agents when used to treat hormone-sensitive cancer in the advanced setting or as an adjuvant to surgery in earlier stages of the disease; ongoing trials are exploring the use of AIs in the preventative setting. Third generation inhibitors are well tolerated, having no greater side effects than might be expected from oestrogen suppression. Important differences in endocrinological and molecular effects exist between AIs and SERMs. These have implications for the preferred drug sequence and setting in which AIs are used. Since the major obstacle to more widespread use is primary/acquired resistance, discovery of the mechanisms by which resistance occurs offers hope for the future. More detailed study of AIs will yield important information about the involvement of oestrogen on the development and progression of breast cancer. Consequently AIs offer major clinical benefits to patients with breast cancer and the promise of relatively nontoxic intervention in women at high risk of the disease.

Adipose Tissue↗
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↗
PubMed2005

Neuropathology in Alzheimer's disease: target of pharmacotherapy.

The pathogenesis of AD is complex and not yet fully understood. A number of factors, including amyloid plaques, NFTs, and inflammatory processes, are likely to contribute to development of the disease. Acetylcholine and glutamate are intimately involved in learning and memory. Hypotheses implicating defects within both neurotransmitter systems in AI) are recognized. This knowledge coupled with ongoing discoveries about the multiple pathophysiologic pathways involved in development and progression of AD has given rise to several plausible therapeutic targets. Therapies addressing some of these targets (ie, acetylcholine, glutamate) have already shown clinical efficacy in treating AD while other targets continue to be investigated.

Acetylcholine↗
PubMed2026

The AI Revolution: Shaping the Present and Future of Pharmaceutical Research and Development.

The transformative role of artificial intelligence (AI) in the pharmaceutical industry is examined, with a focus on its significant contributions to drug discovery, development, and clinical trial processes. It highlights the inefficiencies and high costs associated with traditional drug development and explores how AI and machine learning (ML) can enhance these processes by analyzing extensive biological datasets. The historical context of AI in pharmaceutical development is examined, noting how advances in computational power and data accessibility have facilitated innovative methodologies, such as predictive analytics and natural language processing. Contemporary trends reveal the integration of AI technologies in drug design, repurposing, and patient response forecasting. This study also addresses the challenges of participant recruitment for clinical trials and proposes AI-driven solutions to optimize patient selection and data management. Furthermore, it discusses AI's role in tailored medicine, emphasizing its potential for advancing precision therapy through targeted drug development and personalized treatment strategies. The importance of digital tools, genomic data analysis, and AI-driven imaging technologies for customizing therapeutic approaches is underscored, along with the regulatory and ethical challenges posed by AI deployment in healthcare. This study illustrates the complexities of AI applications in the pharmaceutical sector, offering insights into both successful and unsuccessful initiatives. The findings suggest that the digitalization of the pharmaceutical industry and enhanced AI integration hold promise for developing safer and more effective therapeutic strategies, while also identifying obstacles to their widespread adoption and optimal functionality.

Artificial intelligence↗
PubMedDate not supplied

Individual cryopreservation with dimethyl sulfoxide and polyvinylpyrrolidone of ejaculates and pooled semen of three avian species.

Artificial insemination (AI) has been used for avian reproduction due to the discovery of cryoprotectants extending its usefulness both in production of domestic fowl and conservation of wild species. The goal of this study was to assess the effect on domestic and wild fowl pooled semen and individual ejaculate cryopreservation with dimethyl sulfoxide (DMSO) and polyvinylpyrrolidone (PVP). Twenty ejaculates and twenty samples of pooled semen of roosters, pheasants and hawks were frozen in media containing DMSO or PVP. DMSO and PVP cryopreservation are equally effective both for ejaculates and pooled semen. Even PVP is a good alternative since no significant difference was found when compared to DMSO. The fertilizing capacity of fresh and cryopreserved pooled semen was analyzed through AI of hens and female pheasants. Similar fertility rates using DMSO, PVP or frozen-thawed samples demonstrated that reproduction is possible through the use of cryopreserved semen. In the case of female pheasants, the same values were obtained with both cryopreserved and fresh semen.

Animals↗
PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗
PubMed2025

Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models.

Reasoning about hypotheses and updating knowledge through empirical observations are central to scientific discovery. In this work, we applied logic-based machine learning methods to drive biological discovery by guiding experimentation. Genome-scale metabolic network models (GEMs) - comprehensive representations of metabolic genes and reactions - are widely used to evaluate genetic engineering of biological systems. However, GEMs often fail to accurately predict the behaviour of genetically engineered cells, primarily due to incomplete annotations of gene interactions. The task of learning the intricate genetic interactions within GEMs presents computational and empirical challenges. To efficiently predict using GEM, we describe a novel approach called Boolean Matrix Logic Programming (BMLP) by leveraging Boolean matrices to evaluate large logic programs. We developed a new system, [Formula: see text], which guides cost-effective experimentation and uses interpretable logic programs to encode a state-of-the-art GEM of a model bacterial organism. Notably, [Formula: see text] successfully learned the interaction between a gene pair with fewer training examples than random experimentation, overcoming the increase in experimental design space. [Formula: see text] enables rapid optimisation of metabolic models to reliably engineer biological systems for producing useful compounds. It offers a realistic approach to creating a self-driving lab for biological discovery, which would then facilitate microbial engineering for practical applications.

Active learning↗
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

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↗
PubMed2001

DNA microarray-based identification of genes controlled by autoinducer 2-stimulated quorum sensing in Escherichia coli.

Bacterial cell-to-cell communication facilitates coordinated expression of specific genes in a growth rate-II and cell density-dependent manner, a process known as quorum sensing. While the discovery of a diffusible Escherichia coli signaling pheromone, termed autoinducer 2 (AI-2), has been made along with several quorum sensing genes, the overall number and coordination of genes controlled by quorum sensing through the AI-2 signal has not been studied systematically. We investigated global changes in mRNA abundance elicited by the AI-2 signaling molecule through the use of a luxS mutant that was unable to synthesize AI-2. Remarkably, 242 genes, comprising ca. 5.6% of the E. coli genome, exhibited significant transcriptional changes (either induction or repression) in response to a 300-fold AI-2 signaling differential, with many of the identified genes displaying high induction levels (more than fivefold). Significant induction of ygeV, a putative sigma(54)-dependent transcriptional activator, and yhbH, a sigma(54) modulating protein, suggests sigma(54) may be involved in E. coli quorum sensing.

Amino Acid Sequence↗
PubMed2026

Machine Learning in Hyperlipidaemia Research: Screening and Experimental Insights into Lipid Metabolism Modulators.

Hyperlipidemia, characterized by elevated blood lipid levels, represents a major global health concern due to its strong association with cardiovascular disease, diabetes, and metabolic syndrome. While current therapies - such as statins, fibrates, bile acid sequestrants, and PCSK9 inhibitors - are effective in controlling hyperlipidemia, they are often associated with adverse effects, potential drug resistance, and suboptimal efficacy in certain patient populations. All of the above underscore the urgent need for safer and more effective therapeutic alternatives. Among the major molecular targets involved in the regulation of lipid metabolism are HMG-CoA reductase, PCSK9, peroxisome proliferator-activated receptors (PPARs), cholesteryl ester transfer protein (CETP), and nuclear receptors, including the liver X receptor (LXR) and farnesoid X receptor (FXR), which are also targets for future antihyperlipidemic drug development. Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly transformed and accelerated drug discovery by enabling the processing of vast amounts of genomic, proteomic, and chemical data. Furthermore, ML tools such as quantitative structure-activity relationship (QSAR) modelling, deep learning, random forest, and support vector machines (SVM) have proven predictive and effective in identifying novel lipid metabolism modulators, thereby enhancing the efficacy and accuracy of virtual screening. Meanwhile, molecular docking has become an integral part of structure-based drug design (SBDD), and software such as AutoDock, Glide, and GOLD have proven effective in generating accurate ligand-target docking models. Molecular docking, together with ML-based approaches, enables the identification of potent and selective drug candidates. Overall, the combination of ML and molecular docking offers an efficient and accurate platform for antihyperlipidemic drug discovery, helping to overcome the limitations of currently available therapeutic strategies.

HMG-CoA reductase↗
PubMed2026

Reimagining research papers as interactive and reliable AI agents.

Here we introduce Paper2Agent, an automated framework that converts research papers into artificial intelligence (AI) agents. Paper2Agent transforms research output from passive artefacts into active systems that accelerate use and discovery. Conventional research papers require readers to understand and adapt the paper's code, data and methods to their work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by converting a paper into an AI agent that functions as a virtual corresponding author, exposing its manuscript, supplementary materials, datasets, code and workflows as active, agent-native knowledge rather than static text. It analyses the paper and codebase using multiple agents to construct a model context protocol (MCP) server, then generates and runs tests to refine and increase robustness of the MCP. These paper MCPs can be connected to a chat agent (such as Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the paper. We demonstrate Paper2Agent's effectiveness through case studies. Paper2Agent created an agent that leveraged AlphaGenome1 to interpret genomic variants and agents based on Scanpy2 and TISSUE (transcript imputation with spatial single-cell uncertainty estimation)3 to conduct single-cell and spatial transcriptomics analyses. We validate that these agents reproduce the results of the original papers and carry out novel user queries. Paper2Agent created multiple agents that collaborate to prioritize a causal gene for psoriasis. By turning static papers into interactive AI agents, Paper2Agent introduces a paradigm for knowledge dissemination and a collaborative ecosystem of AI co-scientists.

Journal Article↗
PubMed2026

Accelerating natural product discovery, characterization and engineering by biofoundries.

Covering: From early developments to the presentNatural product (NP) discovery is increasingly constrained by low-throughput screening, repeated rediscovery, and challenges in scaling genome mining-guided validation workflows. This highlight examines how automated biofoundries are accelerating NP discovery, characterization, and engineering through integrated design-build-test-learn (DBTL) cycles. We discuss recent advances in phenotype-first and genome-first discovery strategies enabled by robotics, high-throughput pathway reconstitution, and automated screening platforms. We further highlight emerging technologies, including cell-free biosynthesis, automated culturomics, programmable chassis engineering, and AI-assisted workflow orchestration, that may enable increasingly autonomous biofoundries for scalable exploration of NP chemical space and therapeutic discovery.

Journal Article↗
PubMed2003

Factors affecting commercial application of embryo technologies in New Zealand: a modelling approach.

New reproductive technologies include sexed sperm and embryo-based technologies. The technology of sperm sexing, for various reasons, is not available in New Zealand and its use has not been modelled. Embryo technologies are however already in use on a limited scale and various scenarios for their use in both the dairy and beef industries in New Zealand have been modelled. This review briefly discusses the various technologies available and some of their potential strengths and weaknesses. In the dairy industry, modelling has been used to simulate the production of breeding bulls for large breeding companies and the production of replacement heifers in dairy herds. For the beef industry, similar modelling has been carried out to determine the opportunities for more efficient beef production. All the models confirmed that at current levels of performance, embryo-based reproductive technologies are usually not profitable in New Zealand except in niche market situations where the returns from the resulting offspring are significantly greater than can be obtained from natural mating or artificial insemination (AI) reproduction systems. This is confirmed by the low uptake of these technologies in this country to date. Even if performance lifts to levels similar to AI, profitability is likely to occur only if the costs of pregnancies to embryo-based reproductive technologies can occur at prices less than two to four times greater than AI or natural mating. This break-even requirement depends on the returns that can be achieved and the advantages that can be captured by the technology over and above those available from AI or natural mating. Two new uses for reproductive technologies in dairy cattle could be the proliferation of novel or rare genotypes from gene discovery programs and improving the female reproductive rate for optimal marker assisted selection. In both these uses the technology is not at present competing with AI or natural mating. The challenge exists therefore for the biological scientists to satisfy these requirements, coupled with the ethical and human factors involved in the introduction of any new technology. Potential end users of the technologies have been surveyed. They are quite positive about the technologies provided they can use them profitably and are keen to obtain more information about them.

Animals↗
PubMed2003

Laboratory semen assessment and prediction of fertility: still utopia?

Finding a laboratory test reliable enough to predict the potential fertility of a given semen sample or a given sire for artificial insemination (AI) is still considered utopian, as indicated by the modest correlations seen between results obtained in vitro and field fertility. Male fertility is complex, and depends upon a heterogeneous population of spermatozoa interacting at various levels of the female genital tract, the vestments of the oocyte, and the oocyte itself. For this reason, laboratory assessment of semen must include the testing of most sperm attributes relevant for fertilization and embryo development, not only in individual spermatozoa but within a large sperm population as well. Strategies for the discovery of in vitro predictors of semen fertility require evaluations of low sperm doses for AI, so that differences in innate in vivo fertility can be accurately detected.

Animals↗