Improving human essential protein prediction using only protein sequences via ensemble learning
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
Accurate prediction of essential proteins by using computational methods can effectively reduce the cost of wet-lab experiments. Existing computational methods usually rely on constructed protein-protein interaction (PPI) networks with different kinds of biological data. However, high-quality PPI networks and other biological data are not available for all proteins. Thus, it is very necessary and valuable to develop accurate methods for fast and effective prediction of essential proteins by using only protein sequences. We propose EPGBDT, a machine learning ensemble model, to improve the performance of essential protein prediction by using only protein sequences. EP-GBDT has an ensemble structure that combines multiple Gradient Boosting Decision Tree (GBDT) base classifiers. In addition, to reduce the effects of imbalanced dataset, EP-GBDT uses a sampling technique. The results show that EP-GBDT outperforms state-of-the-art sequence-based methods and network-based centrality measures. The source code and datasets can be downloaded from https://github.com/CSUBioGroup/EP-GBDT.
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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) | 0.000 | 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 it