Solutions > Translational Research and Computational Biomarker Development

Translational Research and Computational Biomarker Development

Data available for biomarker development can come from many sources (for example: DNA, RNA, proteins, imaging) making identifying relevant features and building predictive models complex. Keeping up with continually changing scientific literature adds to the challenge. In addition, performing lab work to identify and validate biomarkers can be time-consuming and costly. bPrescient can help, with machine learning approaches to multi-modal composite biomarker design and AI-driven biomarker feature extraction and engineering.

Solution Overview

Our solution for Translational Research and Computational Biomarker Development includes:

Per-modality feature engineering and multi-modal integration to enable composite biomarker identification.

Integration of clinical and pre-clinical biomarker data to enable translation and responding population analytics.

Design and development of systems to track and manage public data from research papers and public databases, including tools for updating and querying data.

Implementation of analytics platforms using tools like AWS Sagemaker for building and testing predictive models that leverage machine learning and AI.

Integration with lab and clinical data systems, both in-house and from partners, to reliably create data packages that contain molecular/imaging data with blinded or unblinded clinical/sample data.

Solution Benefits

Partnering with bPrescient benefits your R&D program in a variety of ways:

Improve biomarker robustness and decrease research/development cycle time by leveraging the full body of biomedical literature across discovery.

Integrative analytics for biomarker identification can use all available in-house and public data across many modalities.

Identify the components of a composite biomarker that yield the best clinical and translational results.

Improve the success rates of clinical trials by deconvolving patient heterogeneity.

Data is accurately linked to patient and sample metadata so analyses can identify robust markers that connect assay signals to patient outcomes and adverse events.

Relevant Expertise

This service draws on our expertise in the following areas:

Strategy & Roadmap Development

Scientific Informatics: Software Design and Implementation

AI & Machine Learning: Generating Novel Insights

Computational Sciences for Biology & Chemistry Research

Data Engineering, MLOps, and CloudOps

Let’s talk

Tell us about your toughest R&D challenge and let’s discuss how bPrescient can solve it.

Translational Research and Computational Biomarker Development

Solution Overview

Solution Benefits

Relevant Expertise

Example Tools and Data Sources

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Let’s talk