Life Sciences

A collaborative analysis platform for researchers and bioinformaticians to investigate multimodal biological data and accelerate discoveries.

Data Applications


Discover and save signatures from many aspects of omics (expression, mutation, methylation, and others). Use these signals to reveal patterns across public and proprietary data.

Oncology & Immuno-Oncology

Use public oncology datasets to assess omics across cancers and immune subtype. Compare single-cell to bulk tissue results to mine data at tissue and cellular levels of resolution.

Biomarker Discovery

Use cohort comparison tools to reveal differential markers in tumor vs. normal, responder vs. non responder. Make differential gene expression lists with a single click.


Integrate data from multiplex diagnostics into a single report. Review and analyze data from your testing population for correlations or trends.

Translational Medicine

Build quantitative evidence that accurately identifies promising early research and validate discoveries so that you can confidently move into clinical care.

Precision Medicine

Zoom in and out to get a 360 degree view of an individual’s health information, from genomics details to complex health conditions, to prescribe personalized treatments.


Iterative analysis allows faster discoveries

In the course of one evening in 2019, Dr. Radovich found three significant results from his own dataset in Thymoma (TCGA).

Milan Radovich, PhD,

Assoc. Prof. IU School of Medicine,

IU Health Vice Presidenat for Oncology Genomics,

Co-Director IUH Precision Genomics

ORIEN Network Scientific Committee Co-Chair

Milan Radovich, PhD,

Assoc. Prof. IU School of Medicine,

IU Health Vice President for Oncology Genomics,

Co-Director IUH Precision Genomics

ORIEN Network Scientific Committee Co-Chair

Analysis Apps

Below are examples of analysis apps used in life sciences. New apps can be built and deployed in under an hour to provide analysis capabilities unique to your data.

Gene Expression Signatures

In this demo, we’re going to show you how to view the level of expression of a gene signature across cancer types using a no-code analysis app.

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.

More analysis apps examples

Cox Survival

Perform cox survival analysis on a patient cohort that you define via disease type and mutation status.

N-Month Survival

Identify features correlated with limited survival times. These features can be the basis of studies for what defines poor prognosis in a particular cancer.

Gene Signature Heatmap Using R

Create a heatmap of selected gene expression using an integrated R script. This analysis app is an example of how you can incorporate scripts and algorithms into a node.

Elastic Net Cross Validation

Elastic net cross validation via a Python implementation of an ML algorithm. Reparameterize this predictive model and assess the importance of patients in your test set or input features.

Want to see more examples of analysis apps in life sciences? Get in touch!

We have hundreds of data nodes and analysis apps at your disposal. Let us know what types of questions you have and what areas you want to focus on and we will give you a personalized demo.

“There is no piece of software that does what you do. If there was we would be using it.”

– Data Science Department Director
@Top 5 Global Pharmaceuticals Company

Specialized Analytics for Everyone

Staff at all levels can confidently make data driven decisions using guided, investigation-driven analysis apps tailored to their area of expertise.

Information and Innovation Officers

Consolidate tools, maximize ROI and keep your organization on the cutting edge by providing your scientists a powerful tool that allows them to be 1000x times more efficient.

Researchers and Domain Experts

Speed up drug discovery with the ability to run reproducible and configurable analysis on the fly. Contextualize your data better with gene, variant and pathway annotation and compare your findings to public datasets. Utilize public data to validate your results.

Data Scientists and Bioinformaticians

Keep up with research questions by building point-and-click analysis apps that empower your researchers to run analyses on their own and enable them to reproduce and share their analyses with the team.

Let’s get the conversation started

From a 30-minute demo to an inquiry about our 4-week pilot project, we are here to answer all of your questions!