MOA Prediction Platform to Help With COVID-19 Research

BioSymetrics Mechanism of Action Prediction Platform framework will be put to work both in prioritizing chemical libraries and in predicting mechanism for identified compounds.

Per the three COVID-19 research initiatives announced by the SCN in the post below, we will be working with William Stanford, Amy Wong, Molly Shoichet, Stephen Juvet, Samira Mubareka, Scott Gray-Own, and Mitchel Sabloff as a component of their research.

We will also have the opportunity to screen one of our own pre-clinical leads.

Please follow us on LinkedIn where were will post updates on this project as they become available.

De-noising CMap L1000 Data

As with any assay, L1000 data is noisy. Experimental replicates (the same compound tested on the same cell line under the same conditions) often result in different levels of expression being measured. The process of de-noising the L1000 data makes it easier to see true assay response, and pick a representative concentration for each compound.

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Case Study: BioSymetrics Identifies $5Million in Savings for Healthcare Network

USE CASE: Value Based Care

CLIENT: Major UK based Healthcare network in partnership with Intacare.

OVERVIEW: The annual cost of radiotherapy is escalating year-on-year with little visibility of root cause and control.  Maintaining cost efficient healthcare for patients required an investigation of current code/claim and cost data.

GOAL: Identify and quantify potential cost savings of revising existing reimbursement mechanisms.

“Processing the data manually would have required many months of man hours.”

PROBLEM: Data was incongruous.  Each healthcare provider used different systems, taxonomies, codes and cost basis for mapping radiotherapy procedures when submitting claims.

  • 75,000+ claims
  • 1725+ unique narratives
  • 1000’s of individual codes and duplicate codes
  • Data types: text, numeric

SOLUTION: Intacare used Augusta Pre-Processing workflows to quickly normalize procedure and cost data collected from multiple sources. The team also created an automatable workflow to streamline future analysis and report generation.


RESULTS:

  • Identified inefficiencies across 24,281 claims
  • 39% of total claims
  • Projected cost savings: $5Million

CONCLUSION:

Pre-processing of data using Augusta workflows reduced the time to manage the data from multiple weeks to just hours.

Moreover, the workflows are now available within Augusta as standard packages, easily replicated for future projects or if additional data needs to be interrogated.

Download the Intacare case study in PDF format.

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