Localization site prediction for membrane proteins by integrating rule and SVM classification
Bibliographic record
Abstract
We study the localization prediction of membrane proteins for two families of medically important disease-causing bacteria, called gram-negative and gram-positive bacteria. Each such bacterium has its cell surrounded by several layers of membranes. Identifying where proteins are located in a bacterial cell is of primary research interest for antibiotic and vaccine drug design. This problem has three requirements: First, with any subsequence of amino acid residues being potentially a dimension, it has an extremely high dimensionality, few being irrelevant. Second, the prediction of a target localization site must have a high precision in order to be useful to biologists, i.e., at least 90 percent or even 95 percent, while recall is as high as possible. Achieving such a precision is made harder by the fact that target sequences are often much fewer than background sequences. Third, the rationale of prediction should be understandable to biologists for taking actions. Meeting all these requirements presents a significant challenge in that a high dimensionality requires a complex model that is often hard to understand. The support vector machine (SVM) model has an outstanding performance in a high-dimensional space, therefore, it addresses the first two requirements. However, the SVM model involves many features in a single kernel function, therefore, it does not address the third requirement. We address all three requirements by integrating the SVM model with a rule-based model, where the understandable if-then rules capture "major structures" and the elaborated SVM model captures "subtle structures". Importantly, the integrated model preserves the precision/ recall performance of SVM and, at the same time, exposes major structures in a form understandable to the human user. We focus on searching for high quality rules and partitioning the prediction between rules and SVM so as to achieve these properties. We evaluate our method on several membrane localization problems. The purpose of this paper is not improving the precision/recall of SVM, but is manifesting the rationale of a SVM classifier through partitioning the classification between if-then rules and the SVM classifier and preserving the precision/recall of SVM.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".