AI Case Study

BERG is attempting to identify genetic predisposition to certain conditions using machine learning with promising Phase I study results

BERG has developed a platform to swiftly analyze patient biology and identify biomarkers using machine learning. Their platform was used in Phase I study to discover molecular markers identifying patients more likely to benefit from the medicine, thus applying a precision medicine approach.

Industry

Healthcare

Pharmaceuticals And Biotech

Project Overview

"Patient samples are collected in both diseased and healthy states and are processed by our high throughput mass spectrometer workflow. Here we analyze the biological activity and make up the sample through adaptive omics. This includes the genome, proteome, lipidome and metabolome. We also look at mitochondrial function, oxidative states, and ATP production to examine how the cells are functioning. The process produces trillions of data points from a single sample. The data is then combined with patient clinical information and analyzed by our proprietary artificial intelligence machine learning analytics program. This combination of systems biology and artificial intelligence is the BERG Interrogative Biology® platform."

Reported Results

Research; Results not yet available

Technology

Function

R And D

Product Development

Background

Using AI to discover molecular markers identifying patients more likely to have clinical benefit to the treatment can reduce time taken to conduct research and trial

Benefits

Cost - Targeted Trials/ Precision testing

Data

Genome, proteome, lipidome and metabolome data
Trillions of data points from each sample