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At least 55 records · Page 3Linked to original sources
PubMed2026

Leveraging bioinformatics approaches for drug repositioning in space radiation protection.

The health effects of space radiation, primarily Galactic Cosmic Rays (GCRs), on humans remain largely unknown, with potential cardiovascular consequences posing a significant threat to astronauts on long-duration spaceflight missions. Currently, there are no established pharmacological countermeasures for GCR exposure. Drug repositioning offers a promising strategy to accelerate pharmaceutical research in space medicine. This study leverages existing bioinformatics techniques to identify and prioritize potential drug candidates associated with proteomic perturbations following simulated GCR exposure using previously published murine cardiac proteomic data. A protein-protein interaction (PPI) network was constructed using the top differentially expressed proteins (DEPs) from murine heart tissue following exposure to 5-ion GCRs as seed nodes, focusing on experimentally supported interactions. Network topology, Markov clustering, and functional enrichment analyses were used to characterize biologically relevant proteins and pathways. Drug-protein interactions were predicted using Drugst.One and mapped to PPI clusters of interest to identify candidate drugs. Selected drug-macromolecule interactions were further explored using CB-Dock2 molecular docking and short-duration molecular dynamics simulations as hypothesis-generating structural assessments. Analysis of a key PPI network cluster consisting of several ATP synthase proteins identified 23 unique drug candidates. These analyses demonstrate a systematic approach for leveraging bioinformatics techniques to identify candidate molecular targets and generate pharmacological hypotheses in the context of space radiation countermeasures. Ultimately, this strategy introduces a hypothesis-generating framework for the prioritization of potential drug candidates for future computational characterization and experimental investigation against spaceflight stressors.

Animals↗
PubMed2005

Harnessing the power of an intelligent health environment in cancer control.

In 1971, when Congress declared "war on cancer," the public's perception was driven by an image of a single cure for a single disease. What researchers have learned since that time is that cancer is a formidable enemy made up of more than 100 different disease etiologies. The war on cancer became a war of the 21st century; a war to be fought on multiple fronts against a diffuse enemy and for which prevention was the most judicious path to victory. To fight this new war on cancer, the National Cancer Institute must seek to harness the power of health informatics to create a supportive environment for transforming science, delivering safe and patient-centric health care, and creating an environment of personal empowerment in public health. Three different types of health informatics applications are implicated: (a) applications in bioinformatics, which are intended to revitalize the engine of scientific discovery; (b) applications in medical informatics, which will create a safer and more effective environment for delivery; and (c) applications in consumer informatics, which will enable individuals to advance the charge of their own ongoing health care over the course of their lives. To keep these applications on track, health care administrators must take a sociotechnical approach to implementation. The new systems must be built into the health care environment in such a way that they support human capacities, provide failsafe backups in the face of cognitive and physical limitations, and support continuous quality improvement.

Computational Biology↗
PubMed2004

Toxicogenomics through the eyes of informatics: conference overview and recommendations.

Virginia Bioinformatics Institute, in conjunction with National Institutes of Environmental Health Sciences, hosted a conference, "Toxicogenomics through the Eyes of Informatics," in Bethesda, Maryland, USA, on 12-13 May 2003. Researchers around the world met to discuss how the application of bioinformatics tools, methodologies, and technologies will enhance our understanding of how cells and organisms respond to toxins. Conference topics included statistical methods, quantitative molecular data sets, computational algorithms for data analysis, computational modeling and simulation, challenges and opportunities in computational biology, and information technology infrastructure for data and tool management. This meeting report is a summary of conference presentations, survey results, current toxicogenomics concerns, and future directions of the toxicogenomics community. In conclusion this report discusses toxicogenomics as related to environmental agents, cell-chemical reactions, and gene-environment interactions.

Algorithms↗
PubMed2003

A framework for the biomedical informatics curriculum.

The problem of developing a curriculum for biomedical informatics is highly dependent on how we choose to define and practice the field. Numerous authors have questioned how to position biomedical informatics along the continuum of formal, empirical and engineering disciplines. A concern with current educational programs in biomedical informatics is that students finish without a clear understanding of the relation between theory and practice, or worse, with the impression that the field does not possess any theoretical basis. In this paper, we propose that biomedical informatics curricula explicitly address skills and competencies at three levels: formal, empirical, and applied. We posit that that knowledge of formalization is necessary to build testable empirical models, and that model-driven approaches are necessary for deploying information systems that can be evaluated in a meaningful way. A curricular framework is proposed that identifies a set of methods, techniques and theories that have broad applicability within the domain of biomedicine, and which can span a wide range of application areas: bioinformatics, imaging informatics, clinical informatics and public health informatics. A stronger linkage between theory and practice will result in students who are empowered to create effective and lasting solutions to biomedical problems.

Computational Biology↗
PubMed2004

Database development in toxicogenomics: issues and efforts.

The marriage of toxicology and genomics has created not only opportunities but also novel informatics challenges. As with the larger field of gene expression analysis, toxicogenomics faces the problems of probe annotation and data comparison across different array platforms. Toxicogenomics studies are generally built on standard toxicology studies generating biological end point data, and as such, one goal of toxicogenomics is to detect relationships between changes in gene expression and in those biological parameters. These challenges are best addressed through data collection into a well-designed toxicogenomics database. A successful publicly accessible toxicogenomics database will serve as a repository for data sharing and as a resource for analysis, data mining, and discussion. It will offer a vehicle for harmonizing nomenclature and analytical approaches and serve as a reference for regulatory organizations to evaluate toxicogenomics data submitted as part of registrations. Such a database would capture the experimental context of in vivo studies with great fidelity such that the dynamics of the dose response could be probed statistically with confidence. This review presents the collaborative efforts between the European Molecular Biology Laboratory-European Bioinformatics Institute ArrayExpress, the International Life Sciences Institute Health and Environmental Science Institute, and the National Institute of Environmental Health Sciences National Center for Toxigenomics Chemical Effects in Biological Systems knowledge base. The goal of this collaboration is to establish public infrastructure on an international scale and examine other developments aimed at establishing toxicogenomics databases. In this review we discuss several issues common to such databases: the requirement for identifying minimal descriptors to represent the experiment, the demand for standardizing data storage and exchange formats, the challenge of creating standardized nomenclature and ontologies to describe biological data, the technical problems involved in data upload, the necessity of defining parameters that assess and record data quality, and the development of standardized analytical approaches.

Animals↗
PubMed1997

Health On the Net automated database of health and medical information.

With the number of World Wide Web sites growing every day, the problem is not just to find information, but to locate the right piece of information. Current World Wide Web search engines have not resolved this problem as they most often return a long list of documents. The search result is then unusable because of the large number of answers from different domains and topics. Only complex queries may, in a given situation, produce a limited number of potentially relevant documents. To make searches more efficient and usable by common users, we now need intelligent and specialised search engines on the Net [1,2]. Health On the Net Foundation and the Molecular Imaging and Bioinformatics Laboratory at Geneva University Hospital have developed Multi-Agent Retrieval Vagabond on Information Networks (MARVIN), a robot that searches sites and documents specifically related to a given specialised field. One such robot has already been implemented and used for the medical and the 2D electrophoresis domains. Health On the Net Foundation has implemented the corresponding search engines, MedHunt (http://www.hon.ch/cgi-bin/find) for the medical field and 2DHunt (http://www.hon.ch/cgi-bin/2DHunt/find) for the 2D electrophoresis field.

Computer Communication Networks↗
PubMed2001

Medical informatics and bioinformatics: European efforts to facilitate synergy.

Over the past decade there have been several attempts to rethink the basic strategies and scope of medical informatics. Meanwhile, bioinformatics has only recently experienced a similar debate about its scientific character. Both disciplines envision the development of novel diagnostic, therapeutic, and management tools, and products for patient care. A combination of the expertise of medical informatics in developing clinical applications and the focused principles that have guided bioinformatics could create a synergy between the two areas of application. Such interaction could have a great influence on future health research and the ultimate goal, namely continuity and individualization of health care. This article summarizes current activities related to facilitating synergy between medical informatics and bioinformatics, emphasizing activities in Europe while relating them to efforts in other parts of the world. The report provides examples of the analysis that European investigators are carrying out, aiming to propose new ideas for collaborations between medical informatics and bioinformatics researchers in a variety of areas.

Computational Biology↗
PubMed2026

Identification of NLRP3 and TIPE2 as asthma biomarkers via integrative bioinformatics and Mendelian randomization.

Asthma is a chronic inflammatory airway disease imposing a substantial global health burden. NLRP3 is an immune sensor involved in infection and cellular stress responses. Recent studies suggest that NLRP3 may be involved in the pathogenesis of asthma. We hypothesized that genetic variation in NLRP3 may contribute to asthma susceptibility. However, the causal relationship between NLRP3 and asthma still remains unclear. In this study, bioinformatics analysis using asthma data and R software was performed to identify NLRP3-related genes. We performed weighted gene co-expression network analysis to identify co-expressed genes, resulting in 12 candidate genes. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses were used to identify the functions of these candidate genes, revealing their involvement in cellular metabolism. Mendelian randomization analysis of the 12 candidate genes identified 2 biomarkers: NLRP3 and TNFAIP8L2 (TIPE2). We validated their diagnostic value for asthma using the GSE182503 dataset, with area under the curve values of 0.83 and 0.66 for NLRP3 and TIPE2, respectively. This project discusses how NLRP3 promotes asthma pathogenesis, whereas TIPE2 may alleviate it, and explores the potential interplay between them. NLRP3 and TIPE2 may serve as diagnostic biomarkers for asthma: NLRP3 may promote, whereas TIPE2 may alleviate asthma development. Both genes represent potential diagnostic biomarkers and therapeutic targets that warrant further functional investigation.

Asthma↗
PubMed2004

Recognition of HLA-A2-restricted mammaglobin-A-derived epitopes by CD8+ cytotoxic T lymphocytes from breast cancer patients.

A breast cancer-associated antigen, mammaglobin-A, is specifically expressed in 80% of primary breast tumors. The definition of immune responses against this highly expressed breast cancer-specific antigen should be of great value in the development of new therapeutic strategies for breast cancer. Thus, the purpose of this study was to identify HLA-A2-restricted mammaglobin-A-derived epitopes recognized by CD8+ cytotoxic T lymphocytes (CTL). We identified seven mammaglobin-A-derived candidate epitopes that bind the HLA-A2 molecule (Mam-A2.1-7) by means of a HLA class I-peptide binding computer algorithm from the Bioinformatics & Molecular Analysis Section of the National Institutes of Health. Subsequently, we determined that CD8+ CTLs from breast cancer patients reacted to the Mam-A2.1 (83-92, LIYDSSLCDL), Mam-A2.2 (2-10, KLLMVLMLA), Mam-A2.3 (4-12, LMVLMLAAL), Mam-A2.4 (66-74, FLNQTDETL), and Mam-A2.7 (32-40, TINPQVSKT) epitopes using an IFN-gamma ELISPOT assay. Interestingly, healthy individuals also showed high reactivity to the Mam-A2.2 epitope. Two CD8+ CTL lines generated in vitro against TAP-deficient T2 cells loaded with the candidate epitopes showed significant cytotoxic activity against the Mam-A2.1-4 epitopes. These CD8+CTL lines recognized a HLA-A2+breast cancer cell line expressing the Mam-A2.1 epitope. In addition, DNA vaccination of HLA-A2+/human CD8+ double-transgenic mice with a DNA construct encoding the Mam-A2.1 epitope and the HLA-A2 molecule induced a significant expansion of epitope-specific CD8+ CTLs that recognize the same HLA- A2+/Mam-A2.1+ breast cancer cell line. In conclusion, these results demonstrate the immunotherapeutic potential of mammaglobin-A for the treatment and prevention of breast cancer.

Adenocarcinoma↗
PubMed2006

Gene prioritization through genomic data fusion.

The identification of genes involved in health and disease remains a challenge. We describe a bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena. Unlike previous approaches, ours generates distinct prioritizations for multiple heterogeneous data sources, which are then integrated, or fused, into a global ranking using order statistics. In addition, it offers the flexibility of including additional data sources. Validation of our approach revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. The approach described here offers an alternative integrative method for gene discovery.

Algorithms↗
PubMed2006

AgBase: a functional genomics resource for agriculture.

BACKGROUND: Many agricultural species and their pathogens have sequenced genomes and more are in progress. Agricultural species provide food, fiber, xenotransplant tissues, biopharmaceuticals and biomedical models. Moreover, many agricultural microorganisms are human zoonoses. However, systems biology from functional genomics data is hindered in agricultural species because agricultural genome sequences have relatively poor structural and functional annotation and agricultural research communities are smaller with limited funding compared to many model organism communities. DESCRIPTION: To facilitate systems biology in these traditionally agricultural species we have established "AgBase", a curated, web-accessible, public resource http://www.agbase.msstate.edu for structural and functional annotation of agricultural genomes. The AgBase database includes a suite of computational tools to use GO annotations. We use standardized nomenclature following the Human Genome Organization Gene Nomenclature guidelines and are currently functionally annotating chicken, cow and sheep gene products using the Gene Ontology (GO). The computational tools we have developed accept and batch process data derived from different public databases (with different accession codes), return all existing GO annotations, provide a list of products without GO annotation, identify potential orthologs, model functional genomics data using GO and assist proteomics analysis of ESTs and EST assemblies. Our journal database helps prevent redundant manual GO curation. We encourage and publicly acknowledge GO annotations from researchers and provide a service for researchers interested in GO and analysis of functional genomics data. CONCLUSION: The AgBase database is the first database dedicated to functional genomics and systems biology analysis for agriculturally important species and their pathogens. We use experimental data to improve structural annotation of genomes and to functionally characterize gene products. AgBase is also directly relevant for researchers in fields as diverse as agricultural production, cancer biology, biopharmaceuticals, human health and evolutionary biology. Moreover, the experimental methods and bioinformatics tools we provide are widely applicable to many other species including model organisms.

Agriculture↗
PubMed2004

Poxvirus bioinformatics.

Biochemical and functional analysis of poxvirus genomes, genes, and proteins has entered a new era with the recent sequencing of more than 30 poxvirus genomes. The management and analysis of this volume of sequence data in an efficient and effective manner requires specialized computer software. This chapter describes a number of bioinformatics techniques useful for analyzing poxvirus genomes. Some of the software discussed here have been developed by members of the Poxvirus Bioinformatics Resource Center (PBRC; funded by National Institutes of Health [NIH]) specifically for use with poxvirus genomes. These programs or, more accurately, suites of programs have many functions dedicated to poxvirus genome characterization. Significantly, this software has been designed with ease of use at a single location as the major goal.

Computational Biology↗
PubMed2006

The University of Washington Health Sciences Library BioCommons: an evolving Northwest biomedical research information support infrastructure.

SETTING: The University of Washington Health Sciences Libraries and Information Center BioCommons serves the bioinformatics needs of researchers at the university and in the vibrant for-profit and not-for-profit biomedical research sector in the Washington area and region. PROGRAM COMPONENTS: The BioCommons comprises services addressing internal University of Washington, not-for-profit, for-profit, and regional and global clientele. The BioCommons is maintained and administered by the BioResearcher Liaison Team. The BioCommons architecture provides a highly flexible structure for adapting to rapidly changing resources and needs. EVALUATION MECHANISMS: BioCommons uses Web-based pre- and post-course evaluations and periodic user surveys to assess service effectiveness. Recent surveys indicate substantial usage of BioCommons services and a high level of effectiveness and user satisfaction. NEXT STEPS/FUTURE DIRECTIONS: BioCommons is developing novel collaborative Web resources to distribute bioinformatics tools and is experimenting with Web-based competency training in bioinformation resource use.

Academic Medical Centers↗
PubMedDate not supplied

Safety evaluation of chemical mixtures and combinations of chemical and non-chemical stressors.

Recent developments in hazard identification and risk assessment of chemical mixtures are reviewed. Empirical, descriptive approaches to study and characterize the toxicity of mixtures have dominated during the past two decades, but an increasing number of mechanistic approaches have made their entry into mixture toxicology. A series of empirical studies with simple chemical mixtures in rats is described in some detail because of the important lessons from this work. The development of regulatory guidelines for the toxicological evaluation of chemical mixtures is discussed briefly. Current issues in mixture toxicology include the adverse health effects of ambient air pollution; the application of such modern, sophisticated methodologies as genomics, bioinformatics, and physiologically based pharmacokinetic modeling; and databases for mixture toxicity. Finally, the state of the art of our knowledge on the potential adverse health effects of combined exposures to chemicals and non-chemical stressors (noise, heat/cold, microorganisms, immobilization, restraint, or transportation), research initiatives in these fields, and the development of an indicator for the cumulative health impact of multiple environmental exposures are discussed.

Dose-Response Relationship, Drug↗
PubMed2004

Bioinformatic analysis of the urine proteome of acute allograft rejection.

The urinary proteome in health and disease attracts increasing attention because of the potential diagnostic and pathophysiologic biomarker information carried by specific excreted proteins or their constellations. This cross-sectional study aimed to analyze the urinary proteome in patients with biopsy-proven acute rejection (n = 23) compared with transplant recipients with stable graft function (n = 22) and healthy volunteers (n = 20) and to correlate this with clinical, morphologic, and laboratory data. Urine samples were preadsorbed on four different protein chip surfaces, and the protein composition was analyzed using a surface-enhanced laser desorption/ionization time-of-flight mass spectrometer platform. The data were analyzed using two independent approaches to sample classification. Patients who experienced acute rejection could be distinguished from stable patients with a sensitivity of 90.5 to 91.3% and a specificity of 77.2 to 83.3%, depending on the classifier used. Protein masses that were important in constructing the classification algorithms included those of mass 2003.0, 2802.6, 4756.3, 5872.4, 6990.6, 19,018.8, and 25,665.7 Da. Normal urine was distinguished from transplant urine using a protein marker of mass 78,531.2 Da with both a sensitivity and a specificity of 100%. In conclusion, (1) urine proteome in transplant recipients with stable graft function was significantly different from healthy control subjects, and (2) acute rejections were characterized by a constellation of excreted proteins. Analysis of the urinary proteome may expedite the noninvasive prediction of acute graft rejection, thus importantly assisting in establishing the diagnosis.

Acute Disease↗
PubMed2026

Human Wings Apart-Like Protein as a Serum Diagnostic Biomarker in Cervical Cancer: An Integrative Bioinformatics Analysis with Serum Validation.

Cervical cancer remains a major threat to women's health worldwide, and reliable serum biomarkers for early detection and therapeutic stratification remain limited. Human wings-apart-like (hWAPL) protein has been implicated in cervical carcinogenesis, but its diagnostic and clinical value has not been fully elucidated. To address this gap, this study integrated public multi-omics datasets, including The Cancer Genome Atlas, GEPIA2, the Human Protein Atlas, and single-cell transcriptomic data, to characterize hWAPL expression, clinicopathological associations, immune infiltration, co-expression networks, post-translational modifications, and drug sensitivity predictions. These findings were evaluated in an independent single-center serum cohort comprising 89 patients with histologically confirmed cervical squamous cell carcinoma and 89 healthy female controls. Serum hWAPL and squamous cell carcinoma antigen (SCC) levels were measured, and diagnostic performance was assessed by receiver operating characteristic curve analysis. In silico, hWAPL was broadly upregulated across multiple malignancies, particularly cervical cancer, enriched in malignant epithelial cells and monocytes/macrophages, and associated with shorter progression-free interval, predicted reduced sensitivity to cisplatin, paclitaxel, and 5-fluorouracil, and predicted sensitivity to MCL-1 and Wee1 inhibitors. In the serum cohort, hWAPL levels were significantly higher in patients than controls and discriminated cervical cancer with an area under the curve of 0.961, exceeding SCC alone. Combining hWAPL with SCC further improved diagnostic performance (area under the curve, 0.974; sensitivity, 93.3%; specificity, 95.5%). These findings suggest that serum hWAPL is a potential novel diagnostic biomarker for cervical squamous cell carcinoma whose performance is enhanced by SCC, whereas the observed associations with chemoresistance and immune microenvironment remodeling are hypothesis-generating and require experimental confirmation.

Humans↗
PubMed2003

Postgenomics: Proteomics and Bioinformatics in Cancer Research.

Now that the human genome is completed, the characterization of the proteins encoded by the sequence remains a challenging task. The study of the complete protein complement of the genome, the "proteome," referred to as proteomics, will be essential if new therapeutic drugs and new disease biomarkers for early diagnosis are to be developed. Research efforts are already underway to develop the technology necessary to compare the specific protein profiles of diseased versus nondiseased states. These technologies provide a wealth of information and rapidly generate large quantities of data. Processing the large amounts of data will lead to useful predictive mathematical descriptions of biological systems which will permit rapid identification of novel therapeutic targets and identification of metabolic disorders. Here, we present an overview of the current status and future research approaches in defining the cancer cell's proteome in combination with different bioinformatics and computational biology tools toward a better understanding of health and disease.

Journal Article↗
PubMed2004

Random screening of proteins for HLA-A*0201-binding nine-amino acid peptides is not sufficient for identifying CD8 T cell epitopes recognized in the context of HLA-A*0201.

HLA-A2 is the most frequent HLA molecule in Caucasians with HLA-A*0201 representing the most frequent allele; it was also the first human HLA allele for which peptide binding prediction was developed. The Bioinformatics and Molecular Analysis Section of the National Institutes of Health (BIMAS) and the University of Tübingen (Syfpeithi) provide the most popular prediction algorithms of peptide/MHC interaction on the World Wide Web. To test these predictions, HLA-A*0201-binding nine-amino acid peptides were searched by both algorithms in 19 structural CMV proteins. According to Syfpeithi, the top 2% of predicted peptides should contain the naturally presented epitopes in 80% of predictions (www.syfpeithi.de). Because of the high number of predicted peptides, the analysis was limited to 10 randomly chosen proteins. The top 2% of peptides predicted by both algorithms were synthesized corresponding to 261 peptides in total. PBMC from 10 HLA-A*0201-positive and CMV-seropositive healthy blood donors were tested by ex vivo stimulation with all 261 peptides using crossover peptide pools. IFN-gamma production in T cells measured by CFC was used as readout. However, only one peptide was found to be stimulating in one single donor. As a result of this work, we report a potential new T cell target protein, one previously unknown CD8-T cell-stimulating peptide, and an extensive list of CMV-derived potentially strong HLA-A*0201-binding peptides that are not recognized by T cells of HLA-A*0201-positive CMV-seropositive donors. We conclude that MHC/peptide binding predictions are helpful for locating epitopes in known target proteins but not necessarily for screening epitopes in proteins not known to be T cell targets.

Algorithms↗