GSK Bio-Manufacturing Omics Data Challenge – Winners

NineSigma is very proud to have partnered up with GSK for the “Bio-Manufacturing Omics Data Challenge“, and to have delivered innovative solutions to GSK.

In an open science initiative GSK wanted to give their data to world class researchers who have novel methods to get new insights from the data and help GSK solve important problems in bio-manufacturing.

GSK asked NineSigma to design a broad, strategic engagement with the global technology community, identify world-class experts in data analysis, machine learning and AI, and invite them to propose new analytical approaches. We received 39 proposals from companies, universities and research institutes in 19 different countries.

Julia Pence, Digital Transformation Portfolio Manager, GSK, said:

“We are thrilled with the number, breadth, and quality of the responses. It was very difficult to choose the top 3 and we are happy to say that all entrants will be granted access to the GSK data set for their research. We look forward to seeing the excellent work that we expect to come from these proposals.”

The three winning proposals will present their analysis and discuss its applications in pharmaceutical biomanufacturing with the GSK Vaccines Technical Development and Global Data Analytics departments. In addition, GSK may make funding available for possible joint collaboration projects with the winners if the initial analysis shows promising applications for GSK biomanufacturing or research.

Starting this challenge back in July 2021, we had the opportunity to read amazing solutions and ideas from different teams, and now it’s time to announce the 3 winning proposals:

1. A state-of-the-art hybrid Genome scale-machine learning approach to predict and improve product quality attributes

  • James Morrissey & Cleo Kontoravdi & Ben Strain & Thanasis Antonakoudis Chemical Engineering Department, Imperial College London | United Kingdom  

“This approach is highly likely to generate useful insights for GSK as it relies on creating a customized model for our strain, which can be used for future analyses as well. We are very impressed by the detailed workplan and experience of the group”. Julia, GSK


2. Integrative modeling of multi-omics data using deep neural networks

  • Yang Dai, Department of Bio-Medical Engineering, University of Illinois at Chicago | USA
  • Derek Reiman, Toyota Technological Institute at Chicago | USA
  • Peter Larsen, Loyola Genomics Facility, Loyola University, Chicago | USA

“This proposal has an innovative approach to building individual modules and later tying them together to use all the available data. Published papers showing success in similar analyses indicate that the planned work is very feasible”. Julia, GSK


3. Molecular dynamics in longitudinal fermentation batch experiments

  • Kim-Anh Lê Cao, School of Mathematics and Statistics, University of Melbourne | Australia
  • Olivier Chapleur, Environmental Biotechnology Processes Division, INRAE | France

“Very interesting approach for combining different types of models and solid plan for dealing with a sparse data set”. Julia, GSK


In the spirit of open-source science and their commitment to helping people Do More, Feel Better, and Live Longer GSK will give all of the respondents access to the data so they can develop novel analytical methods and publish their results.

We are proud to have collaborated on this amazing innovative challenge together with GSK and to have successfully ended it with great proposals and innovative ideas from the winners.


Download GSK’s Case study to get more insights on the process of running the challenge, read about the details by filling in your information:


 #Openinnovation #Challenge  #Innovation

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