The human-data interface for life sciences

A single platform for research data harmonization and analysis. Unparalleled collaboration between your workforce of scientists and multimodal data sources. for life sciences closed loop analysis platform

Data Driven Transformation In Drug Discovery Process

Bring new treatments to market faster. Significantly improve success rates by closing the data question-to-answer loop, from basic discovery to clinical trials to real world evidence.

Basic Research used to identify novel targets, pathways, biomarkers, and signatures.

Preclinical used to evaluate potential success of a treatment, using data from cell lines and model organisms.

Clinical Trials used to stratify heterogeneous patient populations. used to develop diagnostics for stratification, toxicity, and response to treatment.

Regulatory Review used to produce quality controlled reports for reviews.

Real World Evidence used to connect clinical endpoint biomarkers with real world data.

See Solutions For Pharma & Biotech
See Solutions For Translational Oncology

Solve complex data challenges in clinical research

Manage, harmonize, and analyze multimodal biological data from a single platform. Perform statistical analysis and data science by point-and-click and via pluggable R/Python scripts.

Single Cell Analysis

Make analyzable data products that discover biomarkers and explore heterogeneity in single-cell multiomics. Use apps to study genomics, transcriptomics, proteomics and metabolomics across individual cells and populations of cells.

Gene Expression

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.

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. biomarker discovery

Clinical Data Management

Clinical trials frequently involve similar data workflows, however the data is typically siloed into single trial sets. With the analysis platform, you can turn all clinical trial data sources into harmonized, instantly analyzable data products.

Use case examples:

  • Analyze and manage data products for a particular therapeutic as it proceeds or fails through different phases of clinical trials
  • Group different data products into categories for useful data discovery and cataloging
  • Assign and manage user, group, or departmental access to different data products clinical data management

Machine Learning & AI

Use a single platform to access cleaned, harmonized data products for your algorithms.

  • AWS, Azure & Google Cloud
  • R, Python SDK, JupyterHub/JupyterLab Notebook & plugins
  • Integrated AutoML, SageMaker, Azure ML and Google AI services
  • Supports TensorFlow, PyTorch, mxnet, Keras, GLUON, SciML & DeepGraphLibrary machine learning and ai for biomedical research

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.

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. for biomarker discovery clinical data management for machine learning and ai

Explore Resources For Life Sciences for clinical data management and analysis ebook

Data Harmonization for Translational Research

A practical guide to help you resolve clinical trial data challenges using the data mesh approach.

Promote transparency in the data analysis process

Promote Transparency In The Data Analysis Process

Gain visibility into peer-reviewed work. Demonstrate integration of python-based machine learning algorithm into the platform.

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).

Get A Demo

See how works within your clinical workflow.

Redefine How Data Science Advances Clinical Research

Use no-code analysis apps, embedded in your data products, to perform a wide range of analysis — from a simple descriptive analysis to a complex machine learning algorithm.

Statistics currently available:
  • Hypergeometric test for categorical data and sets
  • Student’s T and Mann-Whitney U tests for numeric distributions
  • Univariate and Multivariate Linear Regression for numeric relationships
  • Paired analysis for repeated (longitudinal) measures
  • Matched analysis for control of confounding variables
  • Cox regression for survival analysis
  • K Means and DBSCAN clustering for segmentation
  • PCA, t-SNE and UMAP for projection/embedding
  • Fast event sequence queries
  • Pathway (systems) analysis via Hypergeometric test and GSEA
  • Gene signature analysis via ssGSEA
  • Chi-square test for categorical data
  • Logistic Regression and Random Forest (also many other options) for prediction/classification
  • Pearson/Spearman correlation for numeric distributions
Examples of data types:
  • DNA-Seq (VCF, MAF)
  • RNA-Seq (bulk and single-cell, spatial transcriptomics)
  • Proteomics
  • 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
Examples of data sources:
  • cBioPortal
  • TCGA
  • dbGaP
  • GEO & ArrayExpress
  • Clinical trials
  • UK Biobank

Data Product Examples analysis app - umap and clustering
TCGA Pan-Cancer Atlas And The Immune Landscape Of Cancer

This data product is a combination of data from two sources: TCGA Pan-Cancer Atlas and the Immune Landscape of Cancer. It has 10,967 samples from 33 cancer types. analysis app - umap and clustering
Single Cell RNA-Seq

This data product contains ~6000 cells from 18 patients with head and neck cancer (HNSC). The dataset was published by Puram et al. in Cell in 2017.

Analysis App Examples


Perform UMAP embedding for selected entities, followed by clustering with k-means.

Python Integration

Turn Python models into analysis apps.

Analysis Result Examples analysis - r integration

R Integration analysis - python integration

Python Integration analysis - umap and clustering

UMAP & Clustering analysis - cox survival, analysis - survival analysis

Cox Survival analysis - pathway analysis

Pathway Analysis analysis - cohort comparison

Cohort Comparison

“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

One platform. Multiple uses.

The flexibility and adaptability of’s platform can help you significantly advance the field of biomedical research. for translational oncology

Translational Oncology

Convert static clinical trial data files into harmonized, analyzable data products. offers a secure, scalable, and flexible approach to help researchers, clinicians, and data scientists identify and implement new discoveries through iterative analysis of data.

Learn More for oncology and immuno-oncology

Oncology & Immuno-Oncology

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

Precision Medicine

Reduce the cost of delivering precision medicine. facilitates collaboration between healthcare providers and pharma organizations. The flexibility of platform can harmonize a variety of data sources and formats, from EHR to multi-omics. for immunology

Harmonized Therapeutic Areas

Since immunology connects to many therapeutic areas, such as immuno-oncology, inflammation, and autoimmunity, you can use a single analysis platform to instantly pivot between data products from each of these therapeutic areas. Data Security In Clinical Research Environment

Data Security in Clinical Research Environment 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.

Learn More About Security

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!