Leveraging the impact of diversity in neurodevelopmental disability by integrating machine learning in personalized interventions.

神经发育障碍（NDD）是自闭症、注意力缺陷、智力和学习障碍的总称，影响了13%的人口。它对NDD个人和家庭产生重大的经济和生活质量影响，并对医疗保健系统造成重大的经济负担。到目前为止，治疗仅针对一般症状，这往往导致低疗效和频繁的副作用。新的基因检测方法的出现为基因及其调控对NDD临床表现的重大影响提供了大量证据。尽管如此，即使是相同的基因突变，NDD患者之间也存在很大的差异。这并不是NDD所独有的，因为它在许多其他医疗条件下都很常见。遗传异质性和临床（神经）多样性导致的复杂性对传统的治疗方法提出了挑战。英国和加拿大最近的研究导致了大型数据库的发展，这些数据库记录了NDD患者的详细信息。人工智能（AI）现在为我们提供了快速分析这些数据集中信息的工具。特别是，我们将使用机器学习（ML）来管理复杂的信息，从而加速并更好地确定干预措施的优先级。此外，我们的项目对NDD中基因组信息的理解提出了新的观点。我们的团队将不再专注于探索携带相同基因突变的单个个体或一小群个体的数据，而是将ML应用于大型数据库，以识别与改善临床结果相关的特征（来自基因及其生物学）。此外，我们将使用机器学习来更好地了解不同症状之间的相互依存关系，以开发具有全球积极影响的治疗方法。换句话说，我们将找到既能提高认知能力，又不会对睡眠产生负面影响或产生更多焦虑的解决方案，就像之前的临床试验所看到的那样。最后，我们将为整个科学界提供一个开放访问门户，包括我们的研究成果，它将与目前的开放目标平台集成，该平台是英国学术界和工业界之间的合作伙伴关系，允许研究人员在一个网站上访问有关疾病、基因和药物的相关数据。研究人员将能够提供进一步的信息，这将改进机器学习模型。为了确保实现我们的目标，我们组建了一个NDD临床和遗传学专家小组：Bolduc博士（加拿大）；基因组学、分子和药理学数据计算机科学：Dr. Dunham（英国）；生物信息学：Droit博士；机器学习：Dr. Greiner；社会科学、患者参与和卫生经济学：兹威克博士说。我们的团队还与加拿大和英国的NDD患者和研究组织建立了紧密的联系，这将在整个项目中提供见解。我们得到了来自英国和加拿大的家庭和政府参与、道德和数据管理的合作者的支持。该项目也将是多学科国际培训的独特机会。我们的项目将展示机器学习如何分解NDD中的复杂性和多样性，以开发更成功的干预措施。它将使我们能够开发新的机器学习方法，这些方法将很容易适用于个性化干预落后于诊断的其他疾病。更重要的是，它将把家庭、社会和科学家聚集到一个共享的空间，在那里交换更多更好的信息。最后，我们的项目将包括负责任地实施数据隐私和保密，同时认识到数据共享的必要性，以制定更好的干预措施。

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 advent of novel genetic testing methods has provided plenty of evidence of the major impact that genes and their regulation have on clinical presentation in NDD. Nonetheless, there is a large diversity among individuals with NDD, even with the same genetic mutation. This is not unique to NDD as it is seen widely in many other medical conditions. The complexity derived from the genetic heterogeneity and the clinical (neuro) diversity has proven challenging to traditional approaches for treatment. Recent research in the UK and Canada has led to the development of large databases recording detailed information about individuals with NDD. Artificial intelligence (AI) now provides us with the tools to quickly analyze the information in those datasets. In particular, we will use machine learning (ML) to manage complex information, 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, we will use ML to better understand 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, a partnership between academia and industry in the UK that allows researchers to access linked data on diseases, genes and drugs in a single site. Researchers will be able to provide further information, which will improve the ML model. To ensure that we accomplish our objectives, we have assembled a team of experts in clinical and genetics of NDD: Dr. Bolduc (Canada); in computer science of genomics, molecular and pharmacological data: Dr. Dunham (UK); bioinformatics: Dr. Droit; machine learning: Dr. Greiner; social sciences, patient engagement and health economic: Dr. Zwicker. Our team has also developed strong links with NDD patient and research organizations in Canada and the UK, which will provide insight throughout the project. We are supported by collaborators involved in family and government engagement, ethics and data management in the UK and Canada. The project will also be a unique opportunity for multidisciplinary international training. Our project will show how ML can disassemble the complexity and diversity seen in NDD to develop more successful interventions. It will allow us to develop new ML approaches that will be readily applicable to other disorders where personalized interventions have been lagging behind diagnosis. More importantly, it will bring together families, society and scientists into a shared space where more and better information is exchanged. Finally, our project will embrace responsible implementation of data privacy and confidentiality while recognizing the need for data sharing to develop better interventions.