微生物组生物标志物发现用于患者诊断,预后,风险评估正在引起广泛的兴趣。选定的微生物特征组提供表征宿主疾病状态如癌症或心脏代谢疾病的特征。然而,当前源自机器学习的预测模型仍然表现为黑箱,很少能很好地推广。他们的解释对医生和生物学家来说是具有挑战性的,这使得它们难以在医患决策过程中进行常规信任和使用。需要提供可解释性和生物学洞察力的新方法。这里,我们引入\“Predomics\”,一种受微生物生态系统相互作用启发的原始机器学习方法,是为宏基因组学数据量身定制的。它发现了准确的预测签名,并提供了前所未有的可解释性。预测模型提供的决策是基于一个简单的,然而通过相加计算出的强大分数,减去,或划分微生物组测量的累积丰度。
在>100个数据集上测试,我们证明了Predomics模型是简单且高度可解释的。即使如此简单,它们至少和最先进的方法一样准确。最好的模特家族,在学习过程中发现,提供了提取生物信息和破译所研究条件的可预测性特征的能力。在概念验证实验中,我们使用术前微生物组数据成功预测了减重手术后的身体肥胖和代谢改善.
Predomics是一种新算法,有助于在微生物组领域提供可靠和可信的诊断决策。Predomics符合社会和法律要求,要求在医学领域采用可解释的人工智能方法。
Microbiome biomarker discovery for patient diagnosis, prognosis, and risk evaluation is attracting broad interest. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes and seldom generalize well. Their interpretation is challenging for physicians and biologists, which makes them difficult to trust and use routinely in the physician-patient decision-making process. Novel methods that provide interpretability and biological insight are needed. Here, we introduce \"predomics\", an original machine learning approach inspired by microbial ecosystem interactions that is tailored for metagenomics data. It discovers accurate predictive signatures and provides unprecedented interpretability. The decision provided by the predictive model is based on a simple, yet powerful score computed by adding, subtracting, or dividing cumulative abundance of microbiome measurements.
Tested on >100 datasets, we demonstrate that predomics models are simple and highly interpretable. Even with such simplicity, they are at least as accurate as state-of-the-art methods. The family of best models, discovered during the learning process, offers the ability to distil biological information and to decipher the predictability signatures of the studied condition. In a proof-of-concept experiment, we successfully predicted body corpulence and metabolic improvement after bariatric surgery using pre-surgery microbiome data.
Predomics is a new algorithm that helps in providing reliable and trustworthy diagnostic decisions in the microbiome field. Predomics is in accord with societal and legal requirements that plead for an explainable artificial intelligence approach in the medical field.