Identifying new biomarkers and therapeutic targets for disease is challenging. Scientists must interpret an immense amount of data in order to gain the knowledge and insight they need to move their research forward, but there is so much information to wade through.
AI algorithms can rapidly analyze vast amounts of biological data, identify patterns, and help to pinpoint potential biomarkers. This saves researchers significant amounts of time, not just on the analysis but also in reducing the number of compounds they put forward for safety and efficacy testing in the lab.
This article looks at biomarker discovery and recent advances in AI-powered data-driven approaches.
What are biomarkers?
Biomarkers are measurable indicators of a disease or condition, offering accurate insight into diagnosis, prognosis, and therapeutic monitoring.
They can be put into subtypes according to their application – so there are susceptibility or risk, diagnostic, and prognostic biomarkers. And the ideal biomarker is highly specific for the disease or condition in question, and measurable in easily accessible body fluids such as saliva, urine or blood.
In drug development, protein biomarkers can help identify novel drug targets, and reveal disease progression markers. In this way, they streamline clinical trials by helping to identify populations, and monitor response to, and any potential side effects of a drug.
As accurate and measurable indicators of different biological states, biomarkers are powerful tools used to identify high-risk patients, improve diagnostic accuracy, assess sensitivity to targeted therapies, and predict the prognosis. They enable the categorisation of patients and allow treatments to be tailored accordingly.
AI and biomarker discovery
Two AI approaches are utilized in biomarker discovery, depending on the required outcomes:
To define biomarkers based on a known patient phenotype, or observable traits, supervised learning can be implemented. Supervised learning uses a human-created training set to teach AI models the desired output. This training dataset includes inputs and correct outputs, allowing the model to learn over time. The algorithm measures accuracy, and adjusts until any error has been minimized.
In contrast, during data exploration, unsupervised methods can be used to identify patient subtypes without human intervention. Here, AI algorithms are used to analyze and cluster unlabeled datasets, and discover any hidden patterns or data groupings.
These AI approaches have been used successfully for robust interrogation of complex and multimodal data sets to identify novel patterns and potential biomarkers.
One of the main challenges in biomarker discovery is the high number of ‘false discoveries’. This occurs when the initial scientific findings associated with a new biomarker cannot be reproduced by other labs, something known as scientific reproducibility. AI can help overcome this by validating the use of non-invasive biomarkers, and adhering to reporting guidelines.
AI in disease diagnosis and prognosis
AI is playing an increasingly pivotal role in analyzing and interpreting the massive amounts of data generated by DNA sequencing and protein mapping. By identifying genetic mutations or new biological markers, AI-driven genomics and proteomics are expanding the possibilities for human diagnostic and personalized medicine.
AI has been making significant strides in the field of disease diagnosis and prognosis. With the help of advanced algorithms, AI has been able to analyze large amounts of medical data and generate insights that can be used to identify diseases and develop personalized treatment plans.
AI has been proven to efficiently detect features in images that cannot be perceived by the human brain, making them crucial in cancer diagnosis. In a recent study conducted by Stanford University, an AI algorithm diagnosed skin cancer with an accuracy rate of 91%, which was far higher than the group of dermatologists it was being tested against.
AI has the potential to diagnose other health conditions such as heart disease and diabetic retinopathy. Early detection of such life-threatening diseases using AI-driven technology will help us lead longer and healthier lives.
Future prospects for AI in precision diagnostics
Biomarkers play a critical role in realizing the true potential of precision medicine. In drug development, biomarkers can help identify molecular pathways that contribute to disease, the mechanism of action of a drug, and help in the selection of the drug target.
AI can help with biomarker discovery and validation by investigating and cross-referencing the presence of particular biomarkers across a range of different datasets in order to ensure that non-viable biomarkers are avoided.
However, there is still a need for highly skilled and trained data experts to meet the challenge of handling the data generated by AI systems. These data experts play a critical role in ensuring that supervised AI models are correctly trained, so that the data is consistently high quality.
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