Quest for Orthologs in the era of Data Deluge and AI: Challenges and Innovations in Orthology Prediction and Data Integration
Bibliographic record
Abstract
The rapid advancement of DNA sequencing technologies and computational algorithms has led to an unprecedented surge in genomic data, driven by several large-scale sequencing projects worldwide. Orthology plays a crucial role in understanding evolutionary patterns of genes and their functions. At the last Quest for Orthologs meeting (Montréal, Canada-2024), we discussed recent advances in orthology inference, with a focus on the impact of artificial intelligence (AI), protein structures, RNA splicing isoforms, and protein domain evolution together with other evolutionary considerations. A long-standing challenge in the field is the functional annotation of paralogs, for which we present novel approaches. The meeting also emphasised strategies for integrating diverse genetic features into the concept of orthology, encouraging frameworks that account for elements like alternative splicing, domain organisation, and regulatory sequences. We discuss various applications of orthology and paralogy to environmental research, agriculture, and comparative genomics. Additionally, we report recent progress in orthology inference methodologies and resources. This work represents a collaborative synthesis of insights and innovations presented at the 8th Quest for Orthologs meeting, highlighting current progress while outlining future directions for orthology research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".