BIOTECHNOLOGY
computers with the right information, AI can be used to create algorithms that quickly allow companies to discover new biomarkers of disease. Other companies are using algorithms for risk stratification, looking at a series of biomarkers to predict whether or not a person’s cancer is likely to return and if they would benefit from a given therapy. For chronic illnesses, early detection
AI and ML solutions help to reduce the amount of manual lab work required
information, which is then reviewed by the company’s researchers to understand where, medically and scientifically speaking, the diagnostic platform can help the most and where knowledge gaps exist. With this information in hand, the team uses its in-silico lab – powered by an AI-based big data analytics platform – to develop the firm’s own diagnostic tests. For SARS-CoV-2, an AI-based automated development system can help design and optimise the reagents in the PCR kit, streamlining the test- development process. Tis can ultimately reduce the work of testing labs to just a few hours, instead of weeks. Machine learning-based platforms can subsequently assist with assay validation before the test is brought to market. Every company has different technology. For Seegene, this step involves its team simultaneously running through approximately 16 processes to ensure that the product works in a real-world setting. Tese tests, which took just a couple of weeks to perform, would previously have taken months to validate manually.
THE RICH POTENTIAL OF COMPLEX DATASETS Beyond streamlining and optimising the work of researchers and clinicians, AI/ ML promises to unlock entirely new applications. With complex datasets and customised algorithms, companies are now entering entirely new terrain – with confidence. In SARS-CoV-2, multiplex testing has been an important development. Multiplex tests can be used to check for more than one SARS-CoV-2 variant and/ or influenza. At an individual level, this allows clinicians to quickly establish what is driving a person’s symptoms and what
medical interventions are needed. From a public health perspective, multiplex tests help groups efficiently monitor different infectious diseases and the incidence of specific variants
Although useful, developing a multiplex test has historically been challenging. Designing, optimising and validating a multiplex diagnostic assay manually would normally take upwards of one year. With AI, that process can completed in weeks. Tis creates a lot more flexibility, as companies can rapidly respond to the emergence of new variants with tests that look for those key mutations. For biotech companies depending on AI for the development of diagnostics, proper data management is key. Te management of data is what ultimately determines the success of AI. Te Institute of Seegene Information Science is constantly building its competence for data architecture, data governance and data management.
UNTAPPED BIOMARKER DISCOVERY Creating diagnostic tests ultimately depends on how fast a biotech firm can handle and process a vast amount of data that is related to virus patterns, illnesses and related treatments. Tat is largely done with the help of ML and AI. One of the areas where AI/ ML looks particularly promising is in the discovery of novel biomarkers for disease. Biomarkers are tell-tale signs of a biological process that has been dysregulated, helping guide a more definitive diagnosis. But again, discovering clinically relevant biomarkers can take years. By providing
of relevant biomarkers could prompt lifestyle changes or medical interventions that delay or curb the onset of the disease. With the increased speed, personalisation, and complexity of modern AI-driven diagnostic tests come increased quality of life and health.
ADVANCING IN LEAPS AND BOUNDS Seegene believes that ML and AI will definitely reshape the world we live in. Because we areable to develop diagnostic tests faster with heightened accuracy, we will be able to live an era where molecular diagnostics become part of our lives. Tis technology will improve – not replace – the work of lab personnel and clinicians, streamlining their work and unlocking new potential in poorly understood diseases. When that happens, we will be able to prevent not just the pandemic but all kinds of diseases. Molecular diagnostics will work as a precursor in treating and preventing the diseases from happening.
Youngsahng Suh is head of Diagnostics Data Research Center at Seegene.
www.seegene.com
The Seegene Starlet workstation
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