Computational Prediction of Protein Subcellular Locations in Eukaryotes: an Experience Report
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
Copyright © 2012 Meinken and Min. This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Computational prediction of protein subcellular locations in eukaryotes facilitates experimental design and proteome analysis. We provide a short review on recent development of computational tools and our experience in evaluating some of these tools. Classical secretomes can be relatively accurately predicted using computational tools to predict existence of a secretory signal peptide and to remove transmembrane proteins and endoplasmic reticulum (ER) proteins. The protocols of differentially combining SignalP, Phobius, WoLFPSORT, and TargetP for identifying a secretory signal peptide in different kingdom of eukaryotes, with TMHMM for removing transmembrane proteins and PS-Scan for removing ER proteins significantly improve the secretome prediction accuracies. Our evaluation showed that current computational tools for predicting other subcellular locations, including mitochondrial or chloroplast localization, still need to be improved.
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 | 0.000 |
| 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