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.

Article

Discovery through data transparency: at the heart of scientific collaboration

Case Study

Discovery of PD-1 Pathway Biomarkers of Target Engagement

Article

Mapping Metabolic Changes for Diabetes Prediction with Machine Learning

Video

Experts On… Multi-Omics Data Integration

Video

Experts On… Defining Biocomputational Approaches

Article

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:

Talk to our team

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.

Target Engagement

Demonstrate the drug reaches its intended target with measurable binding effect.

Pharmacodynamics

Evaluate the biological activity of a drug.

Early Disease Detection

Identify genetic and/or environmental factors that predispose to disease and/or cause disease progression.

Disease Progression

Monitor disease status, the occurrence of new disease effects, and/or change in disease severity.

Patient Stratification

Select patients according to disease-based or drug-based biomarker signatures.

Clinical Trial Enrichment

Select patients most likely to respond to a treatment to increase study power while reducing study size.

Safety Profiling

Assess the presence or extent of toxicity related to an intervention or drug exposure.

Companion Diagnostics

Identify predictive biomarkers that identify suitable patients for a treatment.

Tell us the questions you want to answer.

We can help you define a biocomputational plan that delivers rapid time-to-insight to accelerate your study.

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