Leveraging artificial intelligence for identifying novel genes in Intellectual disability: genetic interactions in neurodevelopmental disability (NDD)
Neurodevelopmental disability (NDD), which is an umbrella term for autism, attention deficit, and intellectual and learning disability, affects 13% of the population. It has major economic and quality-of-life impacts on NDD individuals and families, and substantial economic burden on the healthcare system. So far, treatment is aimed only at general symptoms, which often leads to low efficacy and frequent side effects. The complexity derived from the genetic heterogeneity and the clinical (neuro) diversity has proven challenging to traditional approaches for treatment.
Recent research has led to the development of large databases recording detailed information about individuals with NDD. Artificial intelligence/machine learning (AI/ML) now provides us with the tools to mine patterns in these datasets. In particular, we will apply ML techniques to unravel underlying complexity, leading to the acceleration and better prioritization of interventions. Also, our project takes a novel view on the understanding of genomic information in NDD. Instead of directing our focus only on exploring data from a single individual or small group of individuals carrying the same gene mutation, our team will apply ML to large databases to identify features (from genes and their biology) correlated with improved clinical outcomes.
In addition, application of ML will enable better understanding of the interdependence between different symptoms to develop treatments that have a globally positive impact. In other words, we would find solutions that improve cognitive skills without impacting sleep negatively or generating more anxiety, as has been seen in previous clinical trials. We will finish by providing the entire scientific community with an open access portal, including our research findings, which will be integrated with the current Open Targets platform that allows researchers to access linked data on diseases, genes and drugs in a single site.
Our project will show how ML can disassemble the complexity and diversity seen in NDD to develop more successful interventions. Application of these new ML approaches can be extended to other disorders where personalized interventions have been lagging behind diagnosis. Together, it will bring families, society and scientists into a shared space for better exchange of information. Finally, our project will embrace responsible implementation of data privacy and confidentiality while recognizing the need for data sharing to develop better interventions. We envisage approximately 36 months for this work to get completed.