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Results for “Biomedical data processing”

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PubMed1976

International standard (C.C.I.T.T.) for transmitting biomedical analogue and digital data on the public telephone network.

Analogue transmission of biomedical signals over the public telephone network has advantages from the economic point of view over digitalized transmission. This paper deals with the special problems encountered with the transmission of biomedical signals. Furthermore, the new international transmission standard C.C.I.T.T. recommendation V. 16 is introduced. This standard has recently been adopted by the relevant study group and has been presented to the Plenary Assembly of the C.C.I.T.T. for final approval. This standard is compatible with the existing public telephone networks. The technical specifications of this standard allow the transmission of the three-channel ECG for diagnostic purposes, e.g., remote processing and computer-assisted evaluation, as well as the transmission of the one-channel ECG with acoustic coupling, e.g., in emergency cases and for pace maker monitoring.

Analog-Digital Conversion↗

PubMed2026

Zebrafish as a versatile model in biomedical research, from disease modeling to regenerative medicine: a review.

Zebrafish are an effective animal model widely utilized in biomedical research. They are known for their rapid reproduction and substantial genetic similarity to humans. Their transparent embryos directly enable the visualization of developmental processes and disease progression. This makes zebrafish invaluable for studying a broad range of human diseases, including cancer, cardiovascular disorders, and neurodegenerative conditions. Compared with other vertebrate models, zebrafish offer several advantages, including ease of genome editing, cost-effective maintenance, and suitability for high-throughput drug screening. Recent advancements have expanded the use of zebrafish in disease modeling and regenerative medicine, providing deeper insights into the genetic and cellular mechanisms underlying human pathologies. Zebrafish provide a robust platform for evaluating the safety, efficacy, and regenerative potential of both natural and synthetic biomaterials, including hydroxyapatite, bioactive glass nanoparticles, and bioceramics. This capability facilitates the creation of artificial tissues that closely resemble native structures. Additionally, integrating artificial intelligence technologies has improved automated data analysis and phenotyping in zebrafish studies, enhancing both accuracy and throughput. This review highlights current applications of zebrafish in disease modeling, drug discovery, regenerative medicine, and biomaterial assessment, emphasizing their evolving role as a versatile preclinical platform supported by advanced genetic and computational tools.

Animals↗

PubMedDate not supplied

Optimizing pacing lead design: redesign of the tined lead.

Technical data derived from experimental and clinical experience in the field of lead/catheter implants formed the basis for developing a set of technical design criteria for pacing leads. Four related levels of technical data were identified: Basic lead components, mode of installation, application site, and mode of use. When the technical data were evaluated, a joint goal between the medical and engineering community evolved--namely, to reduce the total transvenous lead-related reoperation rate (both acute and chronic) from 20-25 percent to below 5 percent. As a result, a new family of leads designed specifically to meet the less than or equal to 5 percent reoperation rate is now available; the first leads introduced are a radical redesign of an earlier tined lead. The development process and results, both experimental and clinical (N greater than 1,005), demonstrate the potential of careful technical definition and communication of biomedical problems between engineering and medical personnel.

Biomedical Engineering↗

PubMed1979

The role of laboratory animal studies in estimating carcinogenic risks for man.

The extent to which biological processes predict those in humans is discussed and illustrated by analysis of data presented in the first 16 volumes of the IARC Monograph series. Other examples are given to show that if there is sufficient evidence that a chemical is carcinogenic in appropriate animal test systems it must be treated as if it were carcinogenic in humans. A quantitative correlation between data in animals and in humans is more difficult to establish, although there is tentative evidence that such a relationship exists. Society should attempt to keep 'inevitable' exposures to carcinogens to a minimum; social need should be balanced against social risk. Biomedical research can help to estimate this role.

Animals↗

PubMed2025

IsoBayes: a Bayesian approach for single-isoform proteomics inference.

MOTIVATION: Studying protein isoforms is an essential step in biomedical research; at present, the main approach for analyzing proteins is via bottom-up mass spectrometry proteomics, which return peptide identifications, that are indirectly used to infer the presence of protein isoforms. However, the detection and quantification processes are noisy; in particular, peptides may be erroneously detected, and most peptides, known as shared peptides, are associated to multiple protein isoforms. As a consequence, studying individual protein isoforms is challenging, and inferred protein results are often abstracted to the gene-level or to groups of protein isoforms. RESULTS: Here, we introduce IsoBayes, a novel statistical method to perform inference at the isoform level. Our method enhances the information available, by integrating mass spectrometry proteomics and transcriptomics data in a Bayesian probabilistic framework. To account for the uncertainty in the measurement process, we propose a two-layer latent variable approach: first, we sample if a peptide has been correctly detected (or, alternatively filter peptides); second, we allocate the abundance of such selected peptides across the protein(s) they are compatible with. This enables us, starting from peptide-level data, to recover protein-level data; in particular, we: (i) infer the presence/absence of each protein isoform (via a posterior probability), (ii) estimate its abundance (and credible interval), and (iii) target isoforms where transcript and protein relative abundances significantly differ. We benchmarked our approach in simulations, and in two multi-protease real datasets: our method displays good sensitivity and specificity when detecting protein isoforms, its estimated abundances highly correlate with the ground truth, and can detect changes between protein and transcript relative abundances. AVAILABILITY AND IMPLEMENTATION: IsoBayes is freely distributed as a Bioconductor R package, and is accompanied by an example usage vignette.

Proteomics↗

PubMed2026

A benchmarking study of feature screening approaches across type 1 diabetes omics studies classification settings.

In recent years, high dimensional omics analyses have become more commonplace for investigating complex biological systems. Typically, these studies attempt to identify key biomolecules associated with a particular biological process. Often, machine learning (ML) is used to identify these biomolecules, typically by learning which biomolecules are highly predictive of a treatment, biological outcome, or phenotype. A major challenge of applying ML to high throughput omics is overcoming noise when sample size is limited and unbalanced with respect to tens of thousands of biomolecules measured. Thus, feature selection (the process of reducing the number of predictors) is both a critical and common step in the ML analysis pipeline. While much attention has been given to embedding and wrapping techniques for feature selection in the omics space, filter-based methods for model-free feature selection have appealing theoretical properties. This manuscript evaluates sure screening, a class of filter-based feature selection methods which provide analytical guarantees for true feature set retention. Here, we cover existing feature screening methods based on the sure screening principal, available software, methods to improve feature screening, and contextualize feature screening in the larger discussion of feature selection for omics data analysis. Additionally, a suite of model-free sure screening approaches is applied and compared for several omics biomedical applications in a ML classification context. We identified BcorSIS as the most effective and computationally efficient screening method across various omics datasets, consistently outperforming others like CSIS and DCSIS in runtime.

Humans↗

PubMed1975

A biomedical information source: the National Clearinghouse for Alcohol Information.

The National Clearinghouse for Alcohol Information (NCALI) is an information resource developed by the National Institute on Alcohol Abuse and Alcoholism of the U.S. Department of Health, Education, and Welfare. It provides numerous alcohol-related information services to professionals in a wide spectrum of biomedical and other disciplines, services that are designed to aid information users in the discrimination and selection of useful literature from the volumes of available information. One such service will provide the biomedical professional, working in an alcohol-related field, with announcements of recent information in categories that he selects from 110 possible fields of interest. Another information service is the quality evaluation of technical documents. The quality evaluation system, which is under continuing development and refinement, serves the literature user by providing a literature quality prescreening process designed to aid users in their literature review and monitoring activities. Additional information services provided by the clearinghouse include Grouped Interest Guides, Subject Area Bibliographies, a quarterly magazine and a periodic general interest information service, and a wide range of special publications. Reference services provide a suitable depth of response to information requests through services that range from assemblages of standard information materials, such as pamphlets and similar publications, to automated data base searches for more technically oriented information requests.

Alcoholism↗

PubMed2017

Drug target ontology to classify and integrate drug discovery data.

BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. RESULTS: As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Biological Ontologies↗

PubMed2025

Evaluating 12 automated, whole-genome sequencing analysis pipelines for Mycobacterium tuberculosis complex: a comparative study.

BACKGROUND: Reliance on complex, custom-built bioinformatics pipelines is a barrier to the implementation of whole-genome sequencing (WGS) of Mycobacterium tuberculosis in high-burden settings in some low-income and middle-income countries (LMICs). Automated analysis pipelines could address this inequity in access to WGS-based diagnostics and surveillance. This study aimed to systematically evaluate the performance and usability of publicly available WGS pipelines for M tuberculosis. METHODS: We identified automated M tuberculosis WGS analysis pipelines through searches of PubMed and GitHub from database inception up to Aug 31, 2024. Accuracy, cost, accessibility, and scalability were assessed for each pipeline. We evaluated the accuracy of genotypic drug susceptibility testing (gDST) using publicly available sequences with phenotypic susceptibility data for 12 antituberculosis drugs. We estimated pooled sensitivity and specificity for each pipeline, across all drugs, by conducting a bivariate meta-analysis, with random effects representing between-drug variability. Lineage classifications were compared, and a previously epidemiologically well-characterised dataset was used to compare measures of genomic relatedness. FINDINGS: Among 28 candidate pipelines, 16 were excluded as they were unmaintained and inexecutable. 12 pipelines (11 compatible with Illumina and four compatible with Nanopore), all free to use, were included for evaluation. Six pipelines processed and stored data remotely, but for five of these six, scalability was limited by the need to upload sequences through web portals. For local processing pipelines, scalability was dependent on substantial local computational resources, data storage capacity, and command-line interfaces that limited user-friendliness. Only one of six remote-processing pipelines removed human DNA sequences before server upload. gDST was similarly accurate across ten of 11 Illumina-compatible pipelines and three of four Nanopore-compatible pipelines. All pipelines classified the main lineages consistently, although there were differences at sublineage resolution. Outputs from three of four pipelines reporting genomic relatedness were compatible with commonly cited single nucleotide polymorphism difference thresholds. INTERPRETATION: Numerous automated analysis pipelines capable of enhancing equity in M tuberculosis WGS are available. Given the overall similarities between the pipelines evaluated in this study in terms of gDST performance, lineage classification, and genomic relatedness inference, non-functional attributes such as availability, accessibility, scalability, and privacy could represent the point of difference for prospective users in LMICs with a high burden of tuberculosis. FUNDING: The Rhodes Trust, Wellcome, Ellison Institute of Technology, and the UK National Institute for Health and Care Research Oxford Biomedical Research Centre.

Mycobacterium tuberculosis↗

PubMed2025

ClarID: A Human-Readable and Compact Identifier Specification for Biomedical Metadata Integration.

BACKGROUND: In biomedical research, subjects and biospecimens are commonly tracked using simple IDs or UUIDs, which guarantee uniqueness but convey no embedded semantic information. Contextual metadata (such as tissue type, diagnosis, or assay) is often stored separately, making integration, cohort selection, and downstream analysis cumbersome. While structured barcoding systems exist in large consortia (e.g., TCGA, GTEx) or domain-specific contexts (e.g., SPREC, GOLD), no unified, extensible framework currently spans both subjects and biosamples in a human- and machine-readable way. METHODS: We developed ClarID, a domain-agnostic specification that supports two identifier formats: (i) a human-readable form (e.g., 'CNAG_Test-HomSap-00001-LIV-TUM-RNA-C22.0-TRT-P1W' that encodes key metadata such as project, species, subject_id, tissue, assay, disease, timepoint and duration (from that event); and (ii) a compact version named 'stub' (e.g., 'CT01001LTR0N401T1W') optimized for filenames, pipelines, and labeling.ClarID is implemented through an open-source command-line tool, ClarID-Tools, which processes tabular metadata files (CSV/TSV) and uses a YAML-based codebook to generate, decode, and validate identifiers, as well as to create and read QR codes. The tool supports bulk and single-sample processing and allows easy integration with institutional workflows. RESULTS: To demonstrate ClarID's utility, we applied it to datasets from the Genomic Data Commons (GDC), generating interpretable identifiers for more than 113,000 clinical records (subjects) and 4,255 biospecimen records. All materials, including pre-processing scripts, input and encoded data, are publicly available and fully reproducible via the accompanying GitHub repository and Google Colab. CONCLUSIONS: ClarID fills a critical gap between opaque accession numbers and rich metadata schemas by embedding key context directly into structured identifiers. It enhances traceability, facilitates downstream analysis, and remains adaptable to project-specific needs through a configurable codebook. The accompanying ClarID-Tools software is freely available, together with full documentation and reproducible pipelines, at https://github.com/CNAG-Biomedical-Informatics/clarid-tools.

Biosample identifiers↗

PubMed2026

Artificial Intelligence for Colorectal Surgeons-Part II: Research Applications, Challenges in Adoption, and Practical Resources.

BACKGROUND: This is part II of a 2-part series examining artificial intelligence in colorectal surgery. Part I established foundational concepts and clinical applications. Implementation, however, requires understanding research methodologies, available resources, and the specific challenges currently limiting widespread adoption. These topics are the focus of part II. OBJECTIVE: To examine artificial intelligence's transformation of surgical research, provide practical implementation resources, address adoption challenges, and explore future directions in colorectal surgery. METHODS: Comprehensive literature review focusing on artificial intelligence research methodology, implementation barriers, educational resources, and emerging technologies relevant to colorectal surgeons. RESULTS: Artificial intelligence streamlines clinical trial design through predictive modeling and natural language processing, reducing enrollment challenges that contribute to failed or inadequate trial accrual. Machine learning enables heterogeneity analysis within clinical trials, identifying treatment-responsive subgroups. Foundation models unlock analysis of unstructured electronic health record data at scale. Professional societies and universities offer specialized artificial intelligence education programs, with open-access data sets facilitating research participation. However, implementation faces multifaceted challenges: technical infrastructure demands, with real-time processing requiring dedicated graphics processing unit clusters; regulatory frameworks struggling with continuously evolving algorithms; undefined liability distribution for artificial intelligence-assisted decisions; algorithmic bias risking health care disparities; and the "black box" problem limiting clinical trust. Economic barriers include substantial initial costs without clear reimbursement pathways. Future directions include multimodal artificial intelligence integrating imaging, genomics, and histopathology; cognitive robotic systems with real-time decision support; digital twin technology for patient-specific surgical simulation; and global surgical artificial intelligence networks enabling distributed learning across institutions. CONCLUSIONS: Although artificial intelligence offers transformative potential for colorectal surgery research and practice, successful implementation requires addressing technical, regulatory, ethical, and economic challenges. The surgeon's evolving role demands both traditional expertise and computational fluency. Future advances in multimodal integration, autonomous systems, and global collaboration will fundamentally reshape surgical practice but will require thoughtful implementation prioritizing patient benefit and clinical value.

Humans↗

PubMed2026

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans↗

PubMed2026

Adeno-Associated Virus Gene Therapy Translation: Lessons from Early Regulatory Meetings.

The Platform Vector-Gene Therapy (PaVe-GT) program is a National Institutes of Health (NIH) initiative that aims to develop adeno-associated virus (AAV) gene therapies for four monogenic rare diseases, two organic acidemias and two congenital myasthenic syndromes. PaVe-GT's platform-based approach identifies and diminishes redundancies and applies efficiencies in preclinical, clinical, and regulatory activities. The program's hypothesis is that implementing these efficiencies can accelerate clinical trial initiation. Based on its platform-centric experience and public-serving mission, the PaVe-GT program actively shares its scientific and regulatory learnings with the public to benefit the development of similar gene therapy products for rare diseases. PaVe-GT's first investigational AAV gene therapy candidate is AAV serotype 9 human propionyl-CoA carboxylase alpha subunit (AAV9-hPCCA) for propionic acidemia caused by PCCA deficiency, which received initial feedback from the Food and Drug Administration (FDA) in an INitial Targeted Engagement for Regulatory Advice on CBER/Center for Drug Evaluation and Research (CDER) ProducTs (INTERACT) meeting. Upon further product development that took into consideration the FDA's initial advice, the program obtained the Agency's feedback in pre-investigational new drug (IND) (Type B) and Type C meetings. Here, we share our experience from these meetings, including strategy, preparation, pre- and post-meeting feedback from the FDA, and lessons learned during the AAV9-hPCCA regulatory process, which the program plans to apply across the PaVe-GT platform. Topics discussed in the regulatory meetings included animal model and efficacy studies, toxicology study plans, manufacturing of the investigational AAV product, and clinical trial design. The main lessons learned from the pre-IND and Type C meetings for AAV9-hPCCA are: (1) Pharmacology/Toxicology studies in a single rodent species are sufficient for filing an initial IND; (2) FDA feedback guides product quality improvements and early development of a quantitative potency assay; (3) use of biomarkers as potential surrogate endpoints in a future efficacy trial benefits from collection of data in the natural history study and the first-in-human Phase 1/2 study; and (4) evidence from the Phase 1/2 clinical trial could be leveraged to support a license application. Lightly redacted regulatory documents and comprehensive templates developed by the PaVe-GT team are available on the PaVe-GT website.

Dependovirus↗