Productize and harmonize your clinical trials data to increase your chances of clinical success.
Rapidly analyze existing and emerging data types
Advanced self-service analysis to optimize your clinical trials. Improve your chances of clinical success by assessing the safety and efficacy of your drug candidates as often and as quickly as possible.
Instant access to analysis
Promptly translate your findings across all clinical trial phases, from phase I to phase IV, using a streamlined approach to statistical analysis. Tag.bio’s data products offer self-guided, no-code analysis apps that enable you to instantly ask and answer your research questions. Below are some examples of how our apps can be used to evaluate your study details, such as biomarkers, endpoints, compounds, and study arms, to increase the chances of your drug candidates’ success:
Perform unsupervised clustering analysis on multimodal clinical data to identify subtypes in heterogeneous disease populations
Create cohorts — using the point-and-click function — to compare the clinical outcomes of your drug candidate against the standard of care
Generate a cox survival analysis report, via R or Python scripts, on your defined subgroups via biomarker status and treatment arm
Access the outputs of your ML/AI models to identify study sites, healthcare professionals, and participants for your studies
See analysis app demo
Single Cell Gene Expression
In this demo, we’re going to show you how you can use a no-code analysis app to look for genes that are differentially expressed in cancer cells.
Productizing your data
Your data analysis process can be dramatically streamlined when you productize your data. By turning your clinical trials data into analyzable data products, you gain fast access into crucial information that will accelerate your clinical trial planning, such as biomarker identification, protocol design, and site selection. Furthermore, the data products will be readily available throughout your drug development lifecycle for quick references.
Curious what productizing your clinical trials data looks like? Below are two examples.
Pfizer’s Trial Phase III - JAVELIN 101
This data product contains data on avelumab with axitinib versus sunitinib in advanced renal cell cancer. Analysis app examples:
This data product contains data from ClinicalTrials.gov to assist in discovering clinical trials. Analysis app examples:
Types of data that can be turned into data products
Tag.bio’s data product framework will integrate any combination of datasets into a simplified, flexible model – perfect for use within apps or as a dataframe to export to other tools.
Some examples of potential data sources, types, and formats:
Data type examples
- DNA-Seq (VCF, MAF)
- RNA-Seq (bulk and single-cell, spatial transcriptomics)
- Flow cytometry
- Compound screening
- Immune repertoire
- Clinical trials (outcomes and biomarkers)
- Longitudinal studies
- Annotation, ontology, pathways
- Machine behavior and maintenance
- Drug response & pKa studies
- Biomanufacturing yields
- Knockdown studies (RNAi, CRISPR)
- Meta genomics
- Gene expression
- DNA methylation
- Somatic mutations
- Germline variants
- High content screening
- Patient-reported outcomes
- Patient apps
Data source examples
- Siloed data
- Data warehouses
- Data lakes
- Data products
- GEO & ArrayExpress
- Clinical trials
- UK Biobank
Data format examples
- CDISC (SDTM, ADaM, etc.)
- Any form of tabular data (CSV/TSV)
Talk To Our Experts
If you don’t see the data types or sources you’re looking for in the lists, chances are we can accommodate them.
Talk to our experts to discover how to turn your clinical trials data into data products!
Harmonize your clinical trials data using data mesh
Build a data mesh of reusable data products to promote data harmonization and bring fragmented data together across clinical trials and therapeutic areas.
Seamlessly integrate both historical and emerging data, such as biomarkers and clinical trial phase III, to inform and enhance your clinical trial planning
Compare your proprietary data against public datasets so that you can identify scientific trends and correlations across multiple studies
Quickly perform exploratory and confirmatory analysis to reduce trial failures and improve clinical development and operations
Data mesh in a box
Tag.bio offers an out-of-the-box solution to help you accelerate your data mesh implementation. Talk to our experts to learn more about our offerings!
Promote cross-organizational collaboration
Access your data mesh of clinical trials data products and collaborate efficiently using Tag.bio’s two-sided analysis environment.
Tag.bio offers a customizable Analysis Platform to support your unique business and research needs. Leverage the enterprise features to drive collaboration and innovation.
- Ask and answer your research questions with confidence – by using self-guided, no-code analysis apps within each data products to perform advanced analytics, such as clustering analysis and generating R reports
- Promptly identify, store, and share your useful data artifacts (UDATs), such as biomarkers and clinical outcomes, with your fellow team members
For data scientists and bioinformaticians
- Demonstrate the value of your data science in clinical trials – by making your work accessible to researchers and other domain experts across the organization
- Save hours from performing one-off analyses – by repurposing your work as reusable analysis apps across multiple data products
- Promote a consistent view of reliable, harmonized clinical trials data – by providing a platform that presents a single source of truth
- Boost efficiency and drive innovation – by streamlining the data analysis process and making data science accessible across your organization
For strategic IT
- Simplify your data governance process as your clinical trials data grows – by streamlining your data management and access
- Stay compliant with regulatory and legal requirements without compromising data use
To help you build high quality data products, Tag.bio offers a Developer Studio that uses a familiar, Jupyter notebook-based setting. Leverage the pre-built templates to streamline your data product creation.
For data scientists
- Access reliable and harmonized clinical trials data from data products for your R & Python algorithms and ML/AI models
- Collaborate more efficiently when your fellow data scientists and data engineers use the same tool to access data and build data products
For data engineers
- Deliver quality and harmonized clinical trials data to data scientists and promote data reusability – by standardizing how nomenclatures get mapped into the data products
- Advance data literacy when you use terminologies that researchers understand – by partnering with domain experts to standardize the terminologies being mapped into the data products
For quality controlled analysts
- Rapidly reproduce any reports and analyses to support regulatory submissions and publications – with instant access to all the versioned data, software packages, and analysis methods
- Streamline the auditing procedures when you gain transparency into the data mapping process and source code
- Set a lasting foundation that promotes consistent quality controlled outputs as your research trial data grows and new data types emerge
- Significantly free up your resources time so that they can focus on what they do best – by streamlining and automating manual repetitive work
More ways to use Tag.bio
Tag.bio helps you overcome data challenges across your drug development process.
To identify novel targets, pathways, biomarkers, and signatures.
To evaluate potential success of a treatment, using data from cell lines and model organisms.
To produce quality controlled reports for reviews.
Real World evidence
To connect clinical endpoint biomarkers with real world data.
Data Security in Clinical Research Environment
Tag.bio is hosted entirely within your secure network and/or your secure cloud. Source data storage and access is tightly controlled within your network, and the platform is compliant with standards such as GxP, GDPR and HIPAA.