Deep learning uncovers distinct behavior of rice network to pathogens response

Predicting rice diseases using advanced technologies at different scales: present status and future perspectives

The past few years have witnessed significant progress in emerging disease detection techniques for accurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases. Furthermore, we summarize applications of different detection techniques, including genomic, physiological, and biochemical approaches. In addition, we also present the state-of-the-art in contemporary optical sensing applications of pathogen-plant interaction phenotypes. This review serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice crops through ML and DL models.

Effector protein structures: a tale of evolutionary relationship

Effector proteins are highly diverse, often lacking similarity in their protein sequences, making it challenging to determine their biological function. Using AlphaFold2 (AF2), Seong and Krasileva recently found that effector structures, but not sequences, share commonality. This helps further understanding of effector evolution across fungal species and reveals unique sequence-unrelated, structurally similar, effector families.

Deep learning based protocol to construct an immune-related gene network of host-pathogen interactions in plants

Investigating network behavior from host-pathogen interactions is challenging. Here, we present the deep-learning-based protocol to construct an immune-related gene network and list the genes involved in the defense response of host to specific biotic stress. The protocol includes the steps to pre-process the interaction pairs and expression profile of plants treated with pathogen/control, feed as input for DLNet algorithm to rank genes based on their contribution to data classification. The top-ranked genes are subjected to module and enrichment analysis. For complete details on the use and execution of this protocol, please refer to Kumar et al. (2022).1.