Machine Learning and Multi-omics Network Approaches to Predict Protein Functions in Arabidopsis

人工智能（AI）是使用先进的计算机算法对各种大规模数据集进行智能模拟，包括金融，医疗保健，消费者和市场科学，网络安全和运输。最近，人工智能已经成为生物数据分析的重要工具，包括协助医学诊断以及检测遗传学和基因组学中与疾病相关的突变。我们的目标是利用AI来改进和完善基因功能注释，并预测拟南芥（一种模式植物系统）中先前未分类基因的功能。鉴于包括植物-病原体相互作用在内的生物系统极其复杂，基因与表型的关系需要了解不同层次的生物信息，我们将利用基于深度学习和网络的框架，该框架可以整合多个异构数据集，以获得更准确的基因功能推断。我们将使用实验方法验证我们的计算结果。具体来说，我们将专注于一组与“硫”相关的基因，使用遗传学和植物病理学方法将其视为“第四大植物营养素”。我们期望为广泛的用户提供各种可交付成果，包括国内和国际的研究人员，高中教育工作者，系统生物学和生物信息学专家以及一般的植物研究社区。这将为植物研究人员提供生物学见解，基因优先级和可验证的假设。此外，我们将辨别新发现的基因在植物防御的分子机制。该项目还将通过面向少数民族的计划PlantGIFT（植物基因组学教师实习）为当地教育和外展优先事项做出重大贡献。 网络科学和深度学习（机器学习的一种亚型）能够从大型多维数据集进行预测建模，并阐明组学各层之间的复杂关系，以预测蛋白质的功能。我们的目标是应用一种包含网络生物学和深度学习计算方法的混合方法来预测未分类基因的基因功能，并为未充分注释的基因完善基因本体。具体来说，我们将使用来自广泛的生物和非生物胁迫处理（包括植物-病原体相互作用）的转录组研究生成一套多样的共表达网络。这些共表达网络将被整合到转录因子-靶标和蛋白质-蛋白质相互作用网络中。网络拓扑特征将从上述多样性组学网络中提取，并集成到每个节点的预测函数中。此外，我们将使用基于深度神经网络的整合框架来预测拟南芥全基因组基因功能，该框架可以有效地在异构网络上执行网络嵌入。这些计算得出的基因功能预测的精确度将通过基于遗传学和病理学的实验测定进行独立验证。特别是，我们将专注于GO术语“硫”，并研究60个基因和一对调控转录因子在生物和非生物胁迫（包括植物防御）中的生物学功能。我们还将建立PlantGIFT（植物基因组学实习教师），以整合研究，教育和推广少数民族参与基因组学和植物科学。总之，这个项目将产生资源，将有利于植物研究界和当地的教育工作者。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的知识价值和更广泛的影响审查标准的支持。

Artificial intelligence (AI) is the simulation of intelligence using advanced computer algorithms on diverse large-scale datasets including finance, healthcare, consumer and market science, cybersecurity, and transportation. Recently, AI has been emerging as a prominent tool for biological data analyses including assisting with medical diagnostics as well as the detection of disease-related mutations in genetics and genomics. We aim to employ AI to improve and refine gene functional annotations as well as predict functions for previously unclassified genes in Arabidopsis, a model plant system. Given that biological systems including plant-pathogen interactions are exceedingly complex and genes to phenotype relationships require an understanding of diverse layers of biological information, we will utilize a deep learning- and network-based framework that can integrate multiple heterogeneous datasets to obtain more accurate inferences of gene functions. We will validate our computational findings using experimental approaches. Specifically, we will focus on a set of genes that are related to “Sulfur”, which is considered “the 4th major phytonutrient” using genetics and plant pathology approaches. We expect a variety of deliverables to a wide range of users including researchers both nationally and internationally, educators in high schools, systems biology and bioinformatics specialists, and the plant research community in general. This will provide biological insights, gene prioritization, and testable hypotheses to plant researchers. Moreover, we will discern the molecular mechanisms of newly identified genes in plant defense. This project will also significantly contribute towards local education and outreach priorities through a minority-oriented program, PlantGIFT (Plant Genomics Internship For Teachers. Network science and deep learning, a subtype of machine learning enable predictive modeling from large and multi-dimensional datasets and elucidate the complex relationships among various layers of –omics to predict the function(s) of the proteins. We aim to apply a hybrid method encompassing network biology and deep learning computational approach to predict gene functions for the unclassified genes as well as refine the Gene Ontology for the inadequately annotated genes. Specifically, we will generate a suite of diverse co-expression networks using transcriptome studies derived from a wide spectrum of biotic and abiotic stress treatments including plant-pathogen interactions. These co-expression networks will be integrated to transcription factor-targets and protein-protein interaction networks. Network topological features will be extracted from the above-described diverse –omics networks and integrated into the predicted function(s) for each node. Moreover, we will predict Arabidopsis genome-wide gene functions using a deep neural networks-based integrative framework that can efficiently perform network embedding on heterogeneous networks. The precision of these computationally derived gene function predictions will be independently validated through genetics- and pathology-based experimental assays. In particular, we will focus on a GO term “sulfur” and investigate the biological functions of 60 genes and a pair of regulatory transcription factors in biotic and abiotic stresses including plant defense. We will also establish PlantGIFT (Plant Genomics Internship For Teachers) to integrate research, education, and outreach for minority participation in genomics and plant sciences. In summary, this project will generate resources that will benefit the plant research community and local educators.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.