Biocomputational services to make findings actionable.

Clinically- and biologically-driven analysis to identify robust biomarkers and validate them with population-level statistical power.

Multi-omics data analysis to identify and validate the most biologically relevant biomarkers.

High throughput biomarker discovery technologies generate massive amounts of data, but
rapid discovery requires more than just analytical speed. That is why we’ve built an expert
data science team to interpret these large-scale datasets for actionable insight.

Sapient provides you with advanced biocomputational support for:

Multi-Dimensional, Multi-
Omics Data Analysis

Applying statistical modeling and ML tools to identify key biomarkers.

Population-Level Biomarker
Cross-Validation

Using Sapient’s longitudinal database from 100,000+ human biosamples.

Explore examples of our data science in action.

scientific data transparency why important

Discovery through data transparency: at the heart of scientific collaboration

pd-1 biomarker target engagement of PD-1 pathway

Discovery of PD-1 Pathway Biomarkers of Target Engagement

metabolic biomarker profiling

Mapping Metabolic Changes for Diabetes Prediction with Machine Learning

multi-omics data integration and big data analytics

Experts On… Multi-Omics Data Integration

biocomputational approaches for biomarker discovery

Experts On… Defining Biocomputational Approaches

metabolomics data interpretation

Data ≠ Insight: Improving Metabolomics Data Interpretation

Biocomputational framework for efficient multi-omics analyses.

Our team uses advanced statistical and machine learning models to integrate the large-scale biomarker datasets we generate with human health metadata as well as genomics information, preclinical data, and clinical outcomes data.

This allow us to identify key biomarkers of interest and map both their genotype and phenotype associations related to:

multi-omics data analysis services

Human Biology Database to amplify discovery insight.

Sapient’s Human Biology Database is comprised of data from >100,000 biosamples already run on our mass spectrometry platform. The samples represent diverse individuals and disease areas, and are paired with longitudinal data including adjudicated clinical outcomes spanning 10-30 years.

The database allows us to:

What questions can you answer
through multi-omics data analysis?

We build our biocomputational approach around your context of use, working collaboratively with your team to define experiments that give you the data you need for the phase you’re in. This could include analysis of biomarkers that inform:

Target Identification

Identify therapeutic targets that may be involved in the pathogenesis of a specific disease.

Target Validation

Confirm a target’s role in disease process and/or the effects of pharmacological modulation of the target.