metadata machine learning

Machine Learning: It’s All About the Data

Building a Data Foundation for Machine Learning Applications

Tackling the Complexity of Biology with Machine Learning

Biological research is full of increasingly complex problems that often are difficult for humans to dissect on their own. Organizations are now turning to tools such as machine learning to enable research teams to address these problems. 

In 2013, it was estimated by McKinsey that the annual value of machine learning and big data in pharma and medicine could amount to $100 billion [1, 2]. In drug discovery and development, where every day counts, organizations rely on better decision making, increased efficiency, and optimized processes to save money and get their life-saving therapies to market as soon as possible. 

The Need for Structured Biological Data to Enable Machine Learning

To train a machine learning algorithm, humans must provide structured and highly contextualized biological data sets. This sets a high barrier to experimental design, with far more repeats and controls needed than what is usually acceptable to a laboratory scientist conducting the experiment by hand. Since working in this way has high barriers to entry, we are yet to see the wider adoption of these techniques in the average laboratory environment. 

The physical challenges associated with generating these rich “small big data sets” are often compounded by data management challenges, which de-incentivize the collection of data fit for data-intensive applications.

Synthace Life Sciences R&D Cloud: an Operating System for Wet Lab Experimentation

Developed with these challenges in mind, the Synthace Life Sciences R&D Cloud acts as an operating system for the wet lab, enabling scientists to build a data generation foundation on the statistical experimental design and automated execution of experiments. It is a cloud-based, device-agnostic software platform that automatically translates experimental designs into physical instructions for executions on selected liquid handlers, then collects and structures the data from that experiment. 

This approach to automation offers key benefits for the wet lab data generation, including:

  • Generation of the whole data package - Synthace’s high level of control, from experimental design to physical execution, puts it in a unique position to collect not only the end data result but also the full experimental metadata that contextualizes that result
  • Unified user interface across experiments - Synthace gives scientists one user interface to design and execute the experiment, then collect and visualize data, removing much of the legwork that comes with having to manage an experiment across multiple software solutions
  • Dynamic experimental design for rapid iterations - Synthaces’s interface allows users to build flexible, reusable protocols that can rapidly generate automation methods for multiple iterations of experiments
  • Standardized, organization-wide solution for automation - Synthace’s device-agnostic nature gives organizations a scalable, standardized solution for executing experiments across labs, sites, and instruments; the intuitive interface allows multiple users to simultaneously build, simulate, and schedule automated experiments to increase efficiency

Where Do We Go from Here? 

Although a great amount of progress has been made with the adoption of digital tools such as Synthace's Life Sciences R&D Cloud, there is still much to do before machine learning can be utilized to its full potential in addressing complex biological questions. This all starts with rethinking how we generate and work with data with the goal of building a foundation of well contextualized data in biological research and development. 

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Learn more about building a data foundation for machine learning applications with Synthace: watch our 30 min online demo.

Learn more about the challenges and new ways of working with data in the 21st century: read our latest whitepaper and listen to our expert panel discussion (with Sam Cooper, Sabina Leonelli, Markus Gershater, and Peter Crane).

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