Plant Graph Target Discovery
PlantGraph represents a cutting-edge approach to plant research, combining advanced GNNs and LLMs to build an integrated, multiomics knowledge graph. This innovative system offers unprecedented capabilities in predicting plant phenotypes and understanding the impacts of pesticides and herbicides effects in our crops, positioning it as a vital tool for advancing agricultural science.
Introduction:
Harnessing the latest advancements in Graph Neural Networks (GNNs) and Large Language Models (LLMs), our project, PlantGraph, aims to revolutionize plant research. By creating a comprehensive multiomics knowledge graph, we enable sophisticated analysis and prediction of plant phenotypes and their interactions with pesticides and herbicides.
Innovative Framework:
Foundational GNNs: Similar to the success of LLMs in various omics fields, our foundational GNNs can reason across different graphs.
Digital Plant Twin: A deep-learning-based architecture capable of modeling metabolic pathways and predicting plant phenotypes.
Phenotype Predictions: Ability to predict plant attributes such as weight, height, biomass, and disease states and more based on available omics data.
Value Proposition:
Enhanced Prediction Accuracy: Leveraging state-of-the-art LLMs like AlphaFold2, validated for their high prediction accuracy in biological properties.
Comprehensive Knowledge Graph: Creation and enrichment of a multiomics knowledge graph with high-quality, interconnected data, enabling robust analysis of plant metabolic pathways.
Impactful Applications: Facilitating the prediction of pesticide and herbicide effects on plants, providing critical insights for agricultural practices and environmental management.