Plant Protein Localization Using Discriminative and Frequent Partition-Based Subsequences
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
The function of proteins in the living cells varies with respect to their localizations. Extracellular plant proteins are responsible for vital functions such as nutrition acquisition, protection from pathogens, communication with other soil organisms, etc. Hence, characterizing these proteins and distinguishing them from intracellular proteins is of high interest to biologists. Nonetheless, the small number of available extracellular proteins for training makes classifying them difficult and challenging. This work focuses on distinguishing extracellular proteins using partition-based subsequences, i.e., subsequences of amino acids in special partitions within the protein sequences. The use of an associative classifier in this work helps to acquire a set of accurate, small and interpretable localization rules that can be used for further biological analysis. The achievement of 98.83% F-Measure for identifying extracellular proteins shows the appropriateness of the selected features and the classification method.
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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