62 research outputs found
Multi-omics HeCaToS dataset of repeated dose toxicity for cardiotoxic & hepatotoxic compounds
The data currently described was generated within the EU/FP7 HeCaToS project (Hepatic and Cardiac Toxicity Systems modeling). The project aimed to develop an in silico prediction system to contribute to drug safety assessment for humans. For this purpose, multi-omics data of repeated dose toxicity were obtained for 10 hepatotoxic and 10 cardiotoxic compounds. Most data were gained from in vitro experiments in which 3D microtissues (either hepatic or cardiac) were exposed to a therapeutic (physiologically relevant concentrations calculated through PBPK-modeling) or a toxic dosing profile (IC20 after 7 days). Exposures lasted for 14 days and samples were obtained at 7 time points (therapeutic doses: 2-8-24-72-168-240-336âh; toxic doses 0-2-8-24-72-168-240âh). Transcriptomics (RNA sequencing & microRNA sequencing), proteomics (LC-MS), epigenomics (MeDIP sequencing) and metabolomics (LC-MS & NMR) data were obtained from these samples. Furthermore, functional endpoints (ATP content, Caspase3/7 and O2 consumption) were measured in exposed microtissues. Additionally, multi-omics data from human biopsies from patients are available. This data is now being released to the scientific community through the BioStudies data repository (https://www.ebi.ac.uk/biostudies/)
The BioSample Database (BioSD) at the European Bioinformatics Institute.
ABSTRACT The BioSample Databas
graph2tab, a library to convert experimental workflow graphs into tabular formats
Motivations: Spreadsheet-like tabular formats are ever more popular in the biomedical field as a mean for experimental reporting. The problem of converting the graph of an experimental workflow into a table-based representation occurs in many such formats and is not easy to solve
PASSIM â an open source software system for managing information in biomedical studies
BACKGROUND: One of the crucial aspects of day-to-day laboratory information management is collection, storage and retrieval of information about research subjects and biomedical samples. An efficient link between sample data and experiment results is absolutely imperative for a successful outcome of a biomedical study. Currently available software solutions are largely limited to large-scale, expensive commercial Laboratory Information Management Systems (LIMS). Acquiring such LIMS indeed can bring laboratory information management to a higher level, but often implies sufficient investment of time, effort and funds, which are not always available. There is a clear need for lightweight open source systems for patient and sample information management. RESULTS: We present a web-based tool for submission, management and retrieval of sample and research subject data. The system secures confidentiality by separating anonymized sample information from individuals' records. It is simple and generic, and can be customised for various biomedical studies. Information can be both entered and accessed using the same web interface. User groups and their privileges can be defined. The system is open-source and is supplied with an on-line tutorial and necessary documentation. It has proven to be successful in a large international collaborative project. CONCLUSION: The presented system closes the gap between the need and the availability of lightweight software solutions for managing information in biomedical studies involving human research subjects
Publisher Correction: Image Data Resource: a bioimage data integration and publication platform
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MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis
Artificial Intelligence methods are powerful tools for biological image
analysis and processing. High-quality annotated images are key to training and
developing new methods, but access to such data is often hindered by the lack
of standards for sharing datasets. We brought together community experts in a
workshop to develop guidelines to improve the reuse of bioimages and
annotations for AI applications. These include standards on data formats,
metadata, data presentation and sharing, and incentives to generate new
datasets. We are positive that the MIFA (Metadata, Incentives, Formats, and
Accessibility) recommendations will accelerate the development of AI tools for
bioimage analysis by facilitating access to high quality training data.Comment: 16 pages, 3 figure
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