Accelerating Scientific Discovery with Data Analytics Platforms

In this interview, we talk to Victor Wong, Scientific Director of Core Life Analytics, about their StratoMineRMT product and how it helps researchers quickly process their data.

Could you introduce yourself and tell us about your journey to Core Life Analytics?

My name is Victor and I started my scientific career at the University of Toronto, where I obtained my doctorate in physiology. I then focused on metabolic disorders, with an emphasis on drug targets and drug discovery. I then worked as a postdoc in neuroscience at UC Davis and Weill Cornell Medical Center. At the latter institution, I was exposed to high-throughput, high-content imaging, using compound screens for drug discovery.

My naivety at first gave me the false impression that automation would dramatically speed up my projects and posts, but that just wasn’t the case. Data analysis was the biggest challenge; the amount of data coming from my projects was beyond my knowledge to even know where to start. I tried to get some proficiency in programming, but nothing was ever robust or repeatable.

I joined Core Life Analytics simply because they are the solution to the data problem I had. Additionally, our scientific philosophies align incredibly well: to provide robust and transparent data analysis tools that allow scientists to quickly analyze their data and paint a holistic picture of their experiments. In addition, and above all, to make scientists aware of good practices in data science.

What are the main purposes of Core Life Analytics and how does it fit into the broader field of biological and life sciences?

At Core Life Analytics, we are on a mission to democratize data science: we help biologists analyze their complex phenotypic data independently.

High-content or phenotypic screening is a powerful tool for drug discovery. Using advanced microscopes and image analysis software, scientists can translate microscopic images into hundreds or thousands of measurements of a cell’s morphology, such as size, intensity, and shape. These so-called characteristics describe and quantify a cell’s phenotype, allowing researchers to carefully assess a compound’s effect.

Techniques like these are part of a move towards more data-driven approaches: instead of focusing on metrics you know are involved in the processes you’re studying, measure them all and use statistics to determine what is interesting.

Image Credit: Gorodenkoff/

The rise of data analytics and bioinformatics is prevalent in all areas of biological and life sciences, but many still struggle to integrate it into their workflow. What are the main obstacles that limit the use of advanced data analysis software in these sectors?

Most scientists find it difficult to use these datasets. Our founders, David Egan and Wienand Omta, witnessed this firsthand at the UMC Utrecht Cell Screening Core; their clients either had to learn data science and coding skills to analyze their data, or have a data scientist do it for them. In both cases, the data is underutilized. Often only a handful of known metrics are analyzed, leaving behind hundreds or thousands of potentially useful metrics.

Why should biologists strive to use advanced data analytics platforms, and how can this help catalyze innovation in areas like drug discovery?

Giving biologists the tools to perform their analyzes autonomously greatly improves the speed with which discoveries are made. First, biologists no longer have to wait for busy data scientists or learn to code, but can simply run their data through the analytics platform.

Second, it allows the person who knows the experiments best – who designed and ran them – to explore the data and make decisions for future experiments accordingly. This is not only important for the final analysis of an experiment; Being able to quickly run these analyzes in the early experimental stages of a study allows you to assess the quality of your model or test and identify problems early on.

Don’t forget the data scientists; When biologists perform these relatively routine analyzes themselves, they have time for the more exciting things, such as advanced AI and multi-omics.

StratoMine®MT is the core product of Core Life Analytics that aims to help researchers quickly process their data. Could you discuss the background of this product and how users can integrate it into their workflow?

When David Egan and Wienand Omta realized at UMC Utrecht that the need for accessible data analysis tools was widespread, they decided to develop something that could be used by any biologist dealing with this type of data. No matter what hardware or software they use or their data science skills. Simply upload your digital data and StratoMineR guides you through a best practice workflow for phenotypic data.

Starting with the most basic steps, such as finding relevant features, quality checking, normalizing and scaling your data, to more advanced steps, like data reduction, to optionally compare and group phenotypes to determine a compound’s mechanism of action.

How StratoMineR worksMT compare to existing platforms currently available? Are there any components that end users would find particularly interesting?

What differentiates our approach from other tools is that it is intuitive and decision-supporting. StratoMineR’s guided workflow ensures no steps are missed and offers suggestions using AI where possible. This way, any biologist can follow, understand, and explore a best practice analysis workflow for multiparameter data. And start doing it early in the experimental phase of a project.

Core Life Analytics recently participated in ELRIG Drug Discovery, Europe’s largest meeting bringing together life science industry professionals. What are the benefits of attending such events to discuss and demonstrate products in person?

ELRIG Drug Discovery was an excellent meeting with an excellent scientific program. We always appreciate meetings like these, as they are a perfect opportunity to catch up on the latest developments in the field. More importantly, we can talk to scientists from many different backgrounds and learn about their perspectives and challenges.

There are many advancements being made in data science technologies, and all industries are reaping the benefits. How do you think the relationship between data science and the life sciences industry will evolve over the next ten years?

As mentioned earlier, interest in data-driven drug discovery is growing. A good illustration of this is the JUMP-CP consortium, which generated a database of phenotypic data from cells responding to 140,000 different genetic and small molecule disturbances.

The potential of this public resource is obvious, but it raises the question: how can researchers outside the consortium take advantage of a large and complex dataset? This and the ever-increasing complexity of data further underscores the need for accessible tools. We are already seeing AI-based analytics tools becoming more mainstream, machine learning (ML) and deep learning (DL) emerging, and talk of more advanced integrations, such as multi-omics, are underway, turning into a new area of ​​research.

Drug discovery sector

Image Credit: paulista/

What will the next few years look like for Core Life Analytics? Are there any innovations you are striving towards?

Over the next few years, we hope to tackle other bottlenecks in high-content filtering. One of our ambitions is, in addition to digital data, to move images to the cloud. This will solve many people’s storage problems and allow us to use massively parallel cloud computing for image analysis, dramatically reducing analysis time.

Where can our readers keep up to date with company activities?

They can follow us on LinkedIn or visit our website.

Please provide links to any material that may be relevant to our audience.

On November 15, we will host a webinar: get ready for the JUMP-CP!

More information about the JUMP-CP Consortium can be found on their website.

More information about StratoMineR for high-grade data can be found in our brochure.

About Victor Wang

As CSO, Victor Wong’s responsibilities are to establish and communicate the scientific validity and utility of the research products developed by Core Life Analytics. He interacts with the scientific and customer communities regarding our company’s scientific capabilities and discoveries. He also manages with other CxOs the overall management of the products and the team. Victor Wang

Victor Wong earned his Ph.D. at the University of Toronto. He was a fellow of the Canadian Institutes of Health Research and received several grants during his postdoctoral training at the Burke Institute of Weill Cornell Medicine. His scientific motivation is driven by his disability; he is profoundly deaf and since then his scientific journey has taken him across a number of therapeutic areas, with a focus on target and drug discovery to find new treatments for a number of diseases spanning the metabolism, oncology, neurodegeneration and hearing loss.

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