Computational identification of human mitochondrial proteins based on homology to yeast mitochondrially targeted proteins
Bibliographic record
Abstract
MOTIVATION: Patients with defects of the mitochondrial respiratory chain due to mutations in nuclear genes are often undiagnosable due to the lack of information about the role of these genes. We therefore sought to produce a novel dataset of human nuclear-encoded mitochondrial proteins. RESULTS: We have used the web-based computer program Mitoprot to predict which proteins in the Saccharomyces cerevisiae genome are targeted to mitochondria. We then used this protein dataset to identify the homologous human proteins in the Unigene database using TBLASTN from NCBI. Human proteins with an Expectation value <10(-5) and an Identity >30% were accepted as true homologues of the yeast proteins. These human proteins were then reanalyzed with Mitoprot. The final set of proteins comprises a dataset of 361 human mitochondrially targeted proteins with homology to all S.cerevisiae mitochondrially targeted proteins. One hundred twenty eight of these proteins are novel and are of unknown function. SUPPLEMENTARY INFORMATION: Supplementary tables will be available from http://www.sickkids.ca/Robinsonlab/
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".