The intelligent transcriptome platform transforming drug discovery and development.

Unlocking the Transcriptome

Our platform integrates gene expression, extracted features, sample-related data and advanced AI algorithms to understand and predict the biological impact of diseases and therapies.

Analyze your Samples with Confidence

A Platform Designed and Validated by World Experts in Transcriptome Analysis and Machine Learning

Get the most insight from your transcriptomic data​

Comprehensive, cutting-edge insights about your samples

Fast turn-around time

Develop advanced predictors integrating all your data

Platform Overview


RNA-Seq Samples


Wide Range
of Analyses



Build a deep molecular profile of each sample

Automatic Discovery

Feature Selection and Machine Learning

Automatically sift through millions of data points to identify the most relevant features and construct generalizable predictors for your endpoints


Uncover druggable mechanisms

Find new target genes and pathways

Choose successful candidates

Create novel biomarkers
and bio-predictors

Understand biological impact

Predict transcriptome effect

Read More About What This Platform Can Do

Unique Capabilities

An engine with unprecedented abilities to mine data, learn models, and intelligently identify candidate multidimensional biomarkers.

Analyzes Expression-Based Data

Expression clustering, principal component analysis (PCA) and high variance gene identification

Provides Functional Profiling

Connect your expression results to many databases of transcript function and biological pathways for more meaningful interpretation

Detects Novel Transcripts

Find transcripts and mutations that are not present in the reference for more complete expression quantification specific to your samples

Operates at Scale

Our efficient algorithms and advanced horizontal scaling provide rapid turnaround and support dynamic collaborations

Builds and Evaluates Predictors for Arbitrary Endpoints

Learn advanced bio-predictors that integrate many types of molecular and other features for resistance, survival, response, or other endpoints

Tunes Feature Selection to Your Endpoints

Zero in on the most relevant molecular features to find targets and build robust predictors

Constantly Advancing

An engine with unprecedented abilities to mine data, learn models, and intelligently identify candidate multidimensional biomarkers.

Only a minority of patients treated with immunotherapy achieve long-term survival, so there is an unmet need to find predictive biomarkers for patient stratification and selection for this class of treatments. Complementing PD-L1 score as a non-overlapping biomarker, tumor mutational burden (TMB) has emerged as promising prediction of efficacy from checkpoint inhibitor (ICPI) therapy. However, multiple TMB assays have been proposed that often fail to maximize predictive value due to low correlation between TMB and response as well as complexities of sorting out mutational immunogenicity. 

Our new family of expression-based Immuno-Oncology (IO) biomarkers re-frames and extends the concept of mutational burden to exploit gene expression measurements and other -omic data,  improving the analytical and clinical validity of mutational-burden biomarkers. Using a novel neoantigen-focused feature selection approach, RNA-IO is trained to focus on relevant neoantigenic mutations and discard those that do not contribute to tumor immunogenicity. It exploits both Whole-Exome Sequencing (WES) and Whole-Transcriptome Sequencing (WTS) to address shortcomings of TMB by weighing mutations based on expression value.