txomeAITM
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
Input
RNA-Seq Samples
Executes
Wide Range
of Analyses
Compiles
Comprehensive
Features
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
Deliverables
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
Txome.ai
Unique Capabilities
An engine with unprecedented abilities to mine data, learn models, and intelligently identify candidate multidimensional biomarkers.
Analyzes Expression-Based Data
Provides Functional Profiling
Detects Novel Transcripts
Operates at Scale
Builds and Evaluates Predictors for Arbitrary Endpoints
Tunes Feature Selection to Your Endpoints
Constantly Advancing
An engine with unprecedented abilities to mine data, learn models, and intelligently identify candidate multidimensional biomarkers.

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.