The role of artificial intelligence in precision medicine
Human health is the result of the interplay between many genetic factors, many environmental factors, and the complexity of our biological hierarchy from gene regulation to biochemical pathways to physiological systems. Understanding this complex genetic architecture is key for precision medicine since combinations of etiological factors naturally define small subgroups of subjects with the same risk for disease or treatment outcome. I have written extensively about this throughout my career in peer-reviewed publications and on this blog.
I gave an invited talk on this topic a few weeks ago at the "Leveraging Big Data and Knowledge to Fight Disease" symposium held at the New York Academy of Sciences in New York City. I spoke about our work on using artificial intelligence (AI) and machine learning for identifying combinations of risk factors from big data to advance our national precision medicine agenda. Rebecca Harrington from Popular Science magazine wrote this piece about the symposium and mentioned our work several times. Our EMERGENT algorithm is able to generate machine learning models of disease susceptibility that can take any mathematical form while at the same time learning the best way to do so. This latter feature moves the algorithm from the machine learning space to AI because it mimics how humans solve problems using their expert knowledge about both biological and quantitative sciences. Our latest published work about this algorithm can be found here. Email me for a reprint.
Some of my general thoughts about this topic can be found in a recent open-access editorial in BioData Mining.