Dataset Information

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems.

ABSTRACT: Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

SUBMITTER: Liu L

PROVIDER: S-EPMC10774286 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems.

Liu Licheng L Zhou Wang W Guan Kaiyu K Peng Bin B Xu Shaoming S Tang Jinyun J Zhu Qing Q Till Jessica J Jia Xiaowei X Jiang Chongya C Wang Sheng S Qin Ziqi Z Kong Hui H Grant Robert R Mezbahuddin Symon S Kumar Vipin V Jin Zhenong Z

Nature communications 20240108 1

Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) frame ...[more]

PMID: 38191521

Similar Datasets

Project description:Summary In drug development, a major reason for attrition is the lack of understanding of cellular mechanisms governing drug toxicity. The black-box nature of conventional classification models has limited their utility in identifying toxicity pathways. Here we developed DTox (deep learning for toxicology), an interpretation framework for knowledge-guided neural networks, which can predict compound response to toxicity assays and infer toxicity pathways of individual compounds. We demonstrate that DTox can achieve the same level of predictive performance as conventional models with a significant improvement in interpretability. Using DTox, we were able to rediscover mechanisms of transcription activation by three nuclear receptors, recapitulate cellular activities induced by aromatase inhibitors and pregnane X receptor (PXR) agonists, and differentiate distinctive mechanisms leading to HepG2 cytotoxicity. Virtual screening by DTox revealed that compounds with predicted cytotoxicity are at higher risk for clinical hepatic phenotypes. In summary, DTox provides a framework for deciphering cellular mechanisms of toxicity in silico. Graphical abstract Highlights • DTox is a deep learning model for toxicity prediction with broad applicability• It provides an interpretation framework to infer toxicity pathways of compounds• We improve the interpretability of toxicity prediction without sacrificing accuracy• DTox’s interpretation framework deciphers cellular mechanisms of toxicity in silico The bigger picture In drug development, a major reason for attrition is the lack of understanding of cellular mechanisms governing drug toxicity. It is challenging to explain the toxicity outcomes of newly developed compounds with limited prior knowledge. To address the challenge, we present DTox (Deep learning for Toxicology), a deep learning model incorporated with extensive knowledge from pathway ontology. DTox is a highly efficient learning model with good predictive performance. It is applicable to all compounds because it requires only chemical structure as model input. More importantly, the knowledge-guided structure of DTox enables us to identify network paths connecting query compounds to toxicity outcomes via target proteins, functional pathways, and general biological processes. Such paths can be viewed as mechanistic interpretation of toxicity and facilitate experimental investigation. We employ existing experimental datasets to validate the mechanistic interpretation by DTox and demonstrate its biological significance. Toxicity assessment is a critical step in drug development. To overcome the black-box nature of conventional classification models, Hao et al. propose an interpretable model named DTox (deep learning for toxicology) for predicting compound response to toxicity assays and inferring toxicity pathways of individual compounds. Validation studies using experimental datasets demonstrate the effectiveness of DTox in rediscovering known mechanisms, differentiating distinctive mechanisms, and recapitulating cellular activities leading to toxicity. DTox will benefit mechanistic studies in toxicology by generating testable hypotheses for further investigation.

Dataset Information

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems.

Publications

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets