Advances in foundation models for genomics: A detailed exploration of developments
Bibliographic record
Abstract
Foundation models (FMs) are a class of deep learning models originating from natural language processing (NLP), trained on large-scale datasets through self-supervised techniques. After pre-training, these models can be fine-tuned with labeled data to accomplish a variety of downstream tasks. FMs have demonstrated outstanding performance across numerous NLP tasks and have been successfully applied in the fields of biology and medicine, exhibiting remarkable efficacy. However, despite the development of multiple FMs specifically tailored for genomics, referred to as genomic foundation models (GFMs), there remains a lack of systematic analysis of these models. This review provides an overview of the current applications and developments of GFMs, offering a comprehensive analysis of their strengths and weaknesses and categorizing their underlying principles. Given the inherent differences between DNA sequences and natural language, designing FMs suitable for genomics presents significant challenges. This paper aims to provide researchers with a detailed analytical report and valuable insights to guide the further development of high-quality GFMs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".