Improving plant functional annotation from knowledge graphs using Graph Neural Networks
Bibliographic record
Abstract
Abstract Annotating genes is essential to crop development, and understanding gene functions sheds light on developing crop improvement strategies, such as marker-assisted breeding, genetic modification, or pest resistance. Through an extensive experimental effort and computational annotation projection, tens of thousands of genes have been annotated across plant species, with most of the gene annotations focusing on a well-studied species, Arabidopsis thaliana, but this represents a small fraction of the hundreds of thousands of genes across these different plant species. Phenotypes and their traits result from multiple processes and events involving multiscale information encoded from different omics, such as genomes, proteomes, or transcriptomes. This stresses a need for an efficient computational approach to capture and integrate information from biological networks and transfer this knowledge from well-studied species to unknown species to annotate and discover functional relationships between annotations and genes. Despite recent progress, existing methods only consider one or a few omics levels to perform reasoning on functional annotation-to-gene relations. The main objective of this study is to generate and explore a large-scale plant biological knowledge graph, the DasDB, and to enrich gene functional annotation linked to genes in different species using graph neural networks (GNNs). Integrating various data sources from different omics has resulted in a comprehensive graph database, facilitating researchers’ in-depth understanding of complex biological networks at the highest level. In addition, applying GNNs on a large-scale knowledge graph database has shown promise in the ability of deep learning models to transfer this information from well-studied plant species to less-characterized plant species, outperforming the transfer of information done using only orthology relationships. This study benchmarks a new research direction in producing new functional annotation discovery in plant species with limited functional annotations. This pipeline was applied to a specific research problem: the mechanism involved in pea nodule nitrogen fixation. We identified known gene markers of this process through a systematic analysis of the DasDB, showing the relevance of our approach. Furthermore, new potential targets to better understand and improve this process were identified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".