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At least 19 recordsLinked to original sources
PubMed2026

CAUSAL artificial intelligence and data-driven decision intelligence in personalized medicine: a review of healthcare informatics systems.

This review examines the integration of causal artificial intelligence (AI) and data-driven decision intelligence within healthcare informatics systems to advance personalized medicine and clinical decision-making. A narrative review methodology was employed, synthesizing interdisciplinary literature from major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Studies focusing on causal inference, decision intelligence, and healthcare informatics applications in personalized medicine were included. Data were extracted on methodological approaches, healthcare settings, analytical techniques, and clinical applications, followed by thematic synthesis. Findings indicate that causal AI enhances clinical decision support by enabling estimation of treatment effects and simulation of intervention outcomes at the individual patient level. Integration of multimodal health data such as electronic health records, genomic data, and real-time monitoring improves prediction accuracy and supports tailored treatment strategies. Additionally, causal models improve interpretability, fostering clinician trust and facilitating transparent decision-making. Robust healthcare informatics infrastructures, including interoperable systems and data warehouses, were identified as critical enablers of causal analytics. Overall, causal AI represents a transformative advancement in healthcare analytics, supporting more informed, individualized, and evidence-based clinical decisions. Its integration within healthcare informatics systems has significant potential to improve patient outcomes and guide the future of intelligent, personalized healthcare delivery.

Precision Medicine↗
PubMed2005

The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging.

The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. OME is designed to support high-content cell-based screening as well as traditional image analysis applications. The OME Data Model, expressed in Extensible Markup Language (XML) and realized in a traditional database, is both extensible and self-describing, allowing it to meet emerging imaging and analysis needs.

Computational Biology↗
PubMed2026

In silico analysis based on network pharmacology and biomolecular informatics to explore the mechanism of action of Erjing Pills (from Shengji Zonglu) in the treatment of leukotrichia.

This study aimed to explore the core active ingredients and potential molecular mechanisms of Erjing Pills, a prescription in the classic work of Traditional Chinese Medicine, "Shengji Zonglu," in the treatment of leukotrichia by utilizing network pharmacology and biomolecular docking techniques. The chemical components and potential targets of Chinese herbal medicines were analyzed through databases such as the Traditional Chinese Medicine Systems Pharmacology Database. The targets related to leukotrichia were collected using GeneCards. The intersection targets were obtained using RStudio. The protein-protein interaction (PPI) network map and the "drug-component-target-disease" visualization network were generated using Cytoscape and STRING to screen the core components and key targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were carried out using the Database for Annotation, Visualization and Integrated Discovery and RStudio. Finally, molecular docking verification was performed by AutoDock and PyMOL (Schrödinger LLC). The key active ingredients of Erjing Pills in the treatment of leukotrichia are β-sitosterol, quercetin, baicalein, and stigmasterol. The top 5 PPI core target proteins, in order, are AKT serine/threonine kinase 1, interleukin 6, tumor protein p53, cysteine-aspartic acid protease 3, and interleukin 1 beta. The Gene Ontology enrichment analysis suggests that the biological processes mainly include responses to exogenous stimuli, membrane rafts, and DNA-binding transcription factor binding. The Kyoto Encyclopedia of Genes and Genomes pathways involve signal pathways such as lipid and atherosclerosis, hepatitis B, Kaposi sarcoma virus infection, chemical carcinogenesis, and human cytomegalovirus infection. The molecular docking results indicate that most of the main active ingredients in Erjing Pills have relatively stable binding activities with the key targets, such as AKT serine/threonine kinase 1, interleukin 6, tumor protein p53, cysteine-aspartic acid protease 3, and interleukin 1 beta, in the PPI network. The active ingredients of Erjing Pills may interfere with the pathological process of leukotrichia by regulating key targets and signal pathways. This study provides a theoretical basis for the clinical application of Erjing Pills and indicates the direction for subsequent experimental research.

Drugs, Chinese Herbal↗
PubMed2026

Analysis of deep learning techniques in computer-aided diagnosis for meniscus injuries: a systematic literature review.

Meniscus informatics is a growing subject of study in the healthcare industry. One of the major hindrances to the healthcare system's transformation is obtaining knowledge and meaningful information from complicated, high-dimensional and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, dissimilar, poorly documented, and generally unstructured electronic health records, imaging, sensor data and text, even after many current techniques have been used to extract more robust and useful elements from the data for analysis. New efficient standards for building end-to-end learning models from complex data are therefore needed. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for diagnosing meniscus tears and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to shortcomings and the need for better technique development, and we provide new perspectives about this exciting new development in the field.

Humans↗
PubMed2026

Transfer Learning across Material Properties Using Center-Environment Features: From Energetics to Mechanical Properties in Multicomponent Mo Alloys.

Transfer learning (TL) provides a viable approach to mitigate data scarcity in materials informatics. While conventional TL focuses on predicting identical properties across different systems, this work demonstrates a cross-property extension of TL from energy to mechanical properties via end-to-end model weight pre-training and fine-tuning: knowledge learned from predicting substitution energies is transferred to predict distinctly different mechanical properties, substantially improving computational efficiency given the typically higher cost of acquiring target-domain data. To accelerate computational alloy design, machine learning models using center-environment (CE) features were first developed to predict substitution energies of alloying elements in molybdenum (Mo)-based alloys. The Random Forest models achieved the optimal performance and transferability-R2 = 0.97, 〈MAE〉 = 0.11 eV, and 〈RMSE〉 = 0.16 eV-against the density functional theory (DFT) benchmark. The model dependency of feature selection and importance analysis was discussed. The transferability of the energy models was validated on unknown systems with new elements. Subsequently, the energy models were fine-tuned using limited mechanical property data to construct energy-to-property (E2P) TL models capable of predicting elastic properties, including bulk modulus, Young's modulus, shear modulus, and elastic constants, achieving an improved accuracy over the non-transferred ML by ∼10-30%, with its transferability verified by additional DFT calculations. This cross-property E2P transfer learning framework opens a new avenue for accelerating computational materials discovery and may be extended to other multiproperty predictions governed by similar physical principles.

center-environment feature↗
PubMed2025

Proteoform profiling of endogenous single cells from rat hippocampus at scale.

We perform intact proteoform profiling of 10,809 endogenous single cells from the rat hippocampus using single-cell proteoform imaging mass spectrometry (scPiMS). scPiMS directly extracts whole proteins and demonstrates high throughput for MS-based single-cell proteomics compared with existing approaches. We develop an informatics workflow dedicated to this datatype and use it to assign neurons, astrocytes or microglia cell types according to their proteoform signatures.

Animals↗
PubMed2026

A human lysosomal storage disorder toolkit for decoding proteome landscapes in cortical-like and dopaminergic-like induced neurons.

Lysosomes maintain cellular homeostasis by degrading proteins delivered via endocytosis and autophagy and by recycling building blocks for organelle biogenesis. Lysosomal storage disorders (LSDs) comprise a group of diseases affecting diverse lysosomal functions. To facilitate molecular phenotyping across diverse LSD gene classes, we are developing a library of human embryonic stem cells engineered to lack individual LSD genes as a resource for the field. Here, we report our initial stem cell toolkit lacking one of 23 LSD genes, including the majority of genes associated with sphingolipidoses and neuronal ceroid lipofuscinoses, and its use in the generation of a proteomic resource for induced cortical-like and midbrain dopaminergic-like neurons. In-depth abundance and correlation profiling across organelles and suborganelle components revealed potential vulnerabilities that reflect distinct patterns of proteome alterations across both genotypes and neuronal cell types. We characterize alterations in the mitochondrial proteome associated with GBA1 and ASAH1 deficiency and identify synaptic and mitochondrial defects in ASAH1-/- induced neurons that correlate with defects in neuronal firing rates. Moreover, we developed an informatic pipeline for proteome-wide identification of individual protein-protein interactions and protein complexes that may be disrupted as a result of LSD gene deficiency. Finally, we visualized structural alterations of ASAH1-deficient endolysosomes in situ using cryoelectron tomography, revealing swollen organelles that were largely devoid of dense internal membranes characteristic of wild-type cells, but containing numerous intralumenal vesicle compartments. This toolkit and associated proteomic landscapes provide a resource for defining molecular signatures associated with LSD gene dysfunction and organelle vulnerability.

Humans↗
PubMed2026

Malaria-GENOMAP: a web-based tool for exploring genomic variation of malaria parasites.

MOTIVATION: Malaria, caused by Plasmodium parasites, imposes a significant public health burden. While Plasmodium falciparum remains the primary target of elimination strategies due to its high mortality rate, lesser-known species such as P. malariae, P. vivax, and P. knowlesi continue to contribute to substantial human morbidity. Genomic approaches, including whole-genome sequencing, offer powerful tools for understanding the biology, transmission, and emerging drug resistance of these neglected Plasmodium species. However, there is an urgent need for informatic tools to summarize and visualize the high-dimensional and complex genomic data generated. RESULTS: We developed Malaria-GENOMAP, a user-friendly web-based tool, which integrates genomic variant data, such as allele frequencies, with geographical maps and chromosome-wide to gene views for in-depth exploration. The tool includes variation from P. knowlesi (n = 139), P. malariae (n = 158), P. ovale curtisi (n = 36), P. ovale wallikeri (n = 47), P. simium (n = 38), and P. vivax (n = 1359). It enables the investigation of population structure, geographic associations of mutations, and putative drug resistance markers, offering valuable insights for malaria control efforts. AVAILABILITY AND IMPLEMENTATION: Malaria-GENOMAP is available online at https://genomics.lshtm.ac.uk/malaria-genomaps.

Internet↗
PubMed2025

The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data.

The Data Distillery Knowledge Graph (DDKG) is a framework for semantic integration and querying of biomedical data across domains. Built for the NIH Common Fund Data Ecosystem, it supports translational research by linking clinical and experimental datasets in a unified graph model. Clinical standards such as ICD-10, SNOMED, and DrugBank are integrated through UMLS, while genomics and basic science data are structured using ontologies and standards such as HPO, GENCODE, Ensembl, STRING, and ClinVar. The DDKG uses a property graph architecture based on the UBKG infrastructure and supports ontology-based ingestion, identifier normalization, and graph-native querying. The system is modular and can be extended with new datasets or schema modules. We demonstrate its utility for informatics queries across eight use cases, including regulatory variant analysis, tissue-specific expression, biomarker discovery, and cross-species variant prioritization. The DDKG is accessible via a public interface, a programmatic API, and downloadable builds for local use.

Journal Article↗
PubMed2026

AI In Leukemia Diagnostics: Complementing the Pathologist's Role.

Artificial intelligence (AI) is reshaping every stage of leukemia diagnostics, from digital morphology and multiparameter flow cytometry to next-generation sequencing, multi-omics analysis, and emerging computational frontiers such as quantum-inspired feature selection. This review outlines how contemporary AI tools can automate labor-intensive quantitation, flag diagnostically salient patterns, and standardize interpretation, while the pathologist or hematologist retains authority over validation, context-specific integration, and clinical decision-making. We present an illustrative "human-in-the-loop" workflow that embeds AI modules within current laboratory information systems, emphasizing points where expert oversight mitigates algorithmic bias and resolves discordant findings. We further map the validator-integrator role across morphology, flow cytometry, and genomic/multi-omic interpretation and provide practical training competencies and use cases for AI-assisted hematopathology. Beyond technical deployment, the article addresses the educational transformation required for sustainable adoption. Drawing on international competency frameworks, including the Digital Health Competencies in Medical Education Framework and recently proposed AI-specific Entrustable Professional Activities, we map core skills that future hematopathologists must master: data-science literacy, critical appraisal of AI outputs, and ethical governance. We highlight evaluated training models such as the Pathology Informatics Essentials for Residents curriculum, Stanford Artificial Intelligence in Machine and Imaging workshops, and College of American Pathologists bootcamps and propose integration strategies adaptable across resource settings. By pairing rigorous validation with targeted education, AI can elevate rather than eclipse the diagnostic role of the leukemia specialist, enabling more timely, reproducible, and personalized patient care.

Humans↗
PubMed2025

Identification of an antifungal lipopeptide from Bacillus amyloliquefaciens HAU3 inhibiting the growth of Fusarium graminearum using preparative chromatography and 2D-NMR.

UNLABELLED: The presence of fungal contamination and its mycotoxins in animal feed is pervasive, posing a significant threat to the well-being and performance of animals, as well as the safety of animal-derived food products. In this work, we screened a strain of Bacillus amyloliquefaciens (B. amyloliquefaciens) HAU3 that exhibits efficient antifungal activity against the growth of Fusarium graminearum (F. graminearum). The antifungal activity was detected in the supernatant, with 20% sterile supernatant demonstrating an impressive antifungal rate of 98.46% against F. graminearum. The antifungal activity of the strain was evaluated through spectrum analysis and silage trials, revealing its effective antifungal activity against multiple fungal species. Furthermore, the strain is capable of degrading ZEN and its derivatives. The targeted disruption of fungal mycelial membrane was observed using scanning electron microscopy and transmission electron microscopy. Additionally, staining with the reactive oxygen species (ROS)-sensitive fluorogenic dye DCFH-DA and propidium iodide (PI) revealed that the strain induces accumulation of ROS in fungal mycelia. The active compounds underwent further separation, purification, and detection. The prominent active peak was identified through mass spectrometry and magnetic resonance spectroscopy. The molecular structure of the active compounds was predicted to be lipopeptides composed of 8 amino acids known as fengycin. The whole genome sequencing and informatics analysis unveiled a total of 13 gene clusters responsible for the synthesis of secondary metabolites. The antifungal effects of B. amyloliquefaciens HAU3 are exerted through the synthesis of fengycin, which selectively targets and compromises the integrity of fungal mycelia membranes, thereby making it a potential biocontrol agent for mitigating mycotoxin contamination in feed. IMPORTANCE: Mycotoxin contamination in animal feed, predominantly driven by Fusarium graminearum, represents a persistent threat to livestock health and food chain integrity. Here, we report the isolation of a soil-derived Bacillus amyloliquefaciens HAU3, exhibiting potent and broad-spectrum antifungal activity alongside efficient biodegradation of zearalenone and its derivatives. Mechanistic dissection reveals that fengycin, the principal bioactive metabolite, compromises fungal membrane integrity and elicits intracellular oxidative stress, culminating in hyphal collapse. Genomic profiling uncovers a diverse repertoire of biosynthetic gene clusters underpinning secondary metabolite production. These findings establish strain HAU3 as a promising microbial chassis for the development of next-generation biocontrol strategies aimed at mitigating mycotoxin burden in agroecosystems.

Bacillus amyloliquefaciens↗
PubMed2024

Bioinformatics pipeline for the systematic mining genomic and proteomic variation linked to rare diseases: The example of monogenic diabetes.

Monogenic diabetes is characterized as a group of diseases caused by rare variants in single genes. Like for other rare diseases, multiple genes have been linked to monogenic diabetes with different measures of pathogenicity, but the information on the genes and variants is not unified among different resources, making it challenging to process them informatically. We have developed an automated pipeline for collecting and harmonizing data on genetic variants linked to monogenic diabetes. Furthermore, we have translated variant genetic sequences into protein sequences accounting for all protein isoforms and their variants. This allows researchers to consolidate information on variant genes and proteins linked to monogenic diabetes and facilitates their study using proteomics or structural biology. Our open and flexible implementation using Jupyter notebooks enables tailoring and modifying the pipeline and its application to other rare diseases.

Humans↗
PubMed2026

Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.

BACKGROUND: Predicting enterocutaneous fistula (ECF)-associated sepsis and mortality poses significant challenges in digital health care due to the disease's complexity and heterogeneous clinical manifestations. Current approaches that rely on single-modal data or traditional scoring systems often fail to capture the intricate immune-inflammatory dynamics and multisystem involvement in patients with ECF. OBJECTIVE: This study aims to develop an artificial intelligence (AI)-driven multimodal fusion model integrating clinical, imaging, and transcriptomic data for early prediction of ECF-associated sepsis and 28-day mortality, addressing the limitations of conventional single-dimensional models. METHODS: This study leveraged publicly available datasets (Medical Information Mart for Intensive Care III [MIMIC-III], electronic Intensive Care Unit [eICU], and The Cancer Genome Atlas) to construct a multimodal framework. Clinical parameters were processed using Extreme Gradient Boosting, abdominal imaging features were extracted via convolutional neural networks, and transcriptomic profiles were analyzed with variational autoencoders. A Transformer-based fusion network was employed for joint prediction and validated through cross-validation and external testing. Key features were identified using Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretability algorithms, while immune regulatory mechanisms were explored via weighted gene co-expression network analysis. RESULTS: The multimodal model achieved an area under the curve (AUC) of 0.89 for predicting sepsis and 28-day mortality, outperforming unimodal models (clinical-only model, AUC 0.72, and imaging-only model, AUC 0.78). Critical predictors included Sequential Organ Failure Assessment score, lactate levels, intra-abdominal free fluid on imaging, and immunoregulatory genes (programmed death-ligand 1 [PD-L1] and indoleamine 2,3-dioxygenase 1 [IDO1]). Mechanistic analysis revealed distinct immune reprogramming in patients with sepsis, characterized by increased regulatory T cells and M2 macrophages, along with downregulated cluster of differentiation 8+ (CD8+) T cells. CONCLUSIONS: This multimodal AI model offers an innovative digital solution in medical informatics, enabling precise early risk stratification for ECF-associated sepsis. By integrating multisource data and providing interpretable insights into immune-inflammatory pathways, the model enhances health care quality for patients with ECF and paves the way for personalized intervention strategies.

Humans↗
PubMed2026

RESCUE: An end-to-end multi-agent LLM system for proactive rare-disease patient screening in the EHR.

BACKGROUND: Rare diseases affect a significant portion of the global population, yet patients often endure a lengthy diagnostic odyssey, frequently missing the opportunity for timely diagnoses with exome or genome sequencing (ES/GS). Existing informatics tools often rely on pre-identified patients or rigid, institution-specific rule sets, failing to address the broader operational question of clinical utility and feasibility. METHODS: We introduce RESCUE (Rare Disease Detection and Escalation Support via a Learning Health System), an end-to-end, multi-agent LLM-powered workflow designed for proactive rare-disease diagnosis across the entire electronic health record (EHR). RESCUE utilizes a team of specialized agents including Ontology, Modeling, Screening, and Review, to automate the screening process to identify candidates for diagnostic testing based on their clinical features. The Ontology Agent classifies clinical data into a four-tier genetic-evidence taxonomy; the Modeling Agent builds a positive-unlabeled (PU) XGBoost classifier to identify potential cases; the Screening Agent applies these models across the EHR population; and the Review Agent evaluates candidates by sampling clinical notes to ensure medical necessity and operational feasibility for genomic testing. RESULTS: Using electronic medical record data from a pediatric hospital, our retrospective evaluation on a holdout set (n=12,591) demonstrates strong discrimination between patients who received diagnostic genomic testing and those who did not (AUC 0.808). Of nearly 500,000 patients in the institutional base, 175,842 met inclusion criteria for screening; among these, RESCUE-flagged candidates were 7.4-fold more likely to receive subsequent genomic assessments compared to controls. Blinded manual chart reviews confirmed that RESCUE identifies previously missed, medically appropriate patients for ES/GS with 80% precision, while simultaneously accounting for prior testing history. CONCLUSIONS: By decoupling expert roles into modular agents, RESCUE offers a flexible, scalable, and adaptable framework for screening patients for rare-disease diagnostic genomic testing. This approach overcomes the limitations of traditional rule-based methods and provides a reproducible, agentic pathway to reduce diagnostic delays and improve patient care at an institutional scale.

Journal Article↗
PubMed2023

The genome sequence of a solitary sea squirt, Ascidia mentula (Müller, 1776).

We present a genome assembly from an individual Ascidia mentula (the (a solitary sea squirt); Chordata; Ascidiacea; Phlebobranchia; Ascidiidae). The genome sequence is 197.0 megabases in span. Most of the assembly is scaffolded into 9 chromosomal pseudomolecules. The mitochondrial genome has also been assembled and is 19.46 kilobases in length.

(a solitary sea squirt)↗
PubMed2024

The genome sequence of the Hoary Footman, Eilema caniola (Hübner, 1808).

We present a genome assembly from one female Eilema caniola (the Hoary Footman; Arthropoda; Insecta; Lepidoptera; Erebidae). The genome sequence is 781.7 megabases in span. Most of the assembly is scaffolded into 31 chromosomal pseudomolecules, including the W and Z sex chromosomes. The mitochondrial genome has also been assembled and is 15.42 kilobases in length. Gene annotation of this assembly on Ensembl identified 22,953 protein coding genes.

Eilema caniola↗
PubMed2024

The genome sequence of the black-footed limpet, Patella depressa (Pennant, 1777).

We present a genome assembly from an individual Patella depressa (the black-footed limpet; Mollusca; Gastropoda; Patellogastropoda; Patellidae). The genome sequence is 683.7 megabases in span. Most of the assembly is scaffolded into 9 chromosomal pseudomolecules. Gene annotation of this assembly on Ensembl identified 20,502 protein coding genes.

Patella depressa↗
PubMed2024

The genome sequence of the English holly, Ilex aquifolium L. (Aquifoliaceae).

We present a genome assembly from an individual Ilex aquifolium (the English holly; Eudicot; Magnoliopsida; Aquifoliales; Aquifoliaceae). The genome sequence is 800.0 megabases in span. Most of the assembly is scaffolded into 20 chromosomal pseudomolecules. The assembled mitochondrial and plastid genomes have lengths of 538.43 kilobases and 157.52 kilobases in length, respectively.

Aquifoliales↗