Pretrained Language Models for Biological Sequence Understanding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pre-trained language models (PLMS) are increasingly becoming innovative tools in life sciences, capable of autonomously learning rich representations from massive amounts of biological sequence data. They capture complex patterns and long-term dependencies in DNA, RNA and protein sequences through self-supervised training, effectively compensating for the limitations of traditional bioinformatics methods. This paper reviews the progress of PLM in the field of biological sequence understanding, covering the model principles and their applications in protein function prediction, gene expression regulation, and structural modeling, etc. It focuses on discussing the case of using the ESM-2 model to predict the impact of protein stability mutations and its comparison with traditional methods. Finally, this paper analyzes the challenges such as data sparsity, model interpretability and computational cost, and looks forward to the development prospects of the deep integration of artificial intelligence and molecular biological science. These advancements indicate that pre-trained models are leading a transformation in the research paradigm of biological sequences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it