High throughput bioanalytical workflows generate massive amounts of multi-omics 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:
To identify key biomarkers from tens of thousands of features, distilling them into specific biological pathways to map their associations.
Applying statistical modeling and “white box” machine learning models curated with biologically relevant markers for prediction.
Our team uses a carefully selected suite of analytic tools, from regression analyses to custom-built ML models of ‘explainable AI’, to integrate and interpret the large-scale proteomics, metabolomics, and lipidomics datasets we generate – extracting dynamic insights to inform your development.
Our scalable, cloud-based platform allows for efficient handling of voluminous datasets, and is paired with a robust testing structure to ensure reproducible results for:
Sapient’s data science team also provides guided analyses to curate unique datasets within DynamiQ, our multi-omics database built from measures collected in >62,000 samples, that support your research questions. We can help define cohort criteria, build the biocomputational design, and execute analyses to:
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:
Identify differentially expressed, disease-modifying, and tractable therapeutic targets that can be acted upon by your drug.
Confirm a target’s role in disease process and/or the effects of pharmacological modulation of the target.
Demonstrate the drug reaches its intended target with measurable binding effect.
Evaluate the biological activity of a drug.
Identify genetic and/or environmental factors that predispose to disease and/or cause disease progression.
Monitor disease status, the occurrence of new disease effects, and/or change in disease severity.
Select patients according to disease-based or drug-based biomarker signatures.
Select patients most likely to respond to a treatment to increase study power while reducing study size.
Assess the presence or extent of toxicity related to an intervention or drug exposure.
Identify predictive biomarkers that identify suitable patients for a treatment.
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