FREE: Enhanced Feature Representation for Isotopic Envelope Evaluation in Top-Down Mass Spectra Deconvolution
Bibliographic record
Abstract
similarity, potentially lacking a comprehensive assessment. To overcome this constraint and facilitate a comprehensive and refined assessment of the similarity between theoretical and experimental envelopes, there exists an imperative to systematically explore and identify increasingly efficacious features for assessing this correspondence. We present enhanced feature representation for isotopic envelope evaluation (FREE) that derives diverse feature representations, encapsulating fundamental physical attributes of envelopes, including peak intensity and envelope shape. We trained FREE and evaluated its performance on both the ovarian tumor (OT) (human OT cells) data set and zebrafish (ZF) (brain in mature female ZF) data set. Specifically, comparing the state-of-art method, FREE demonstrates higher performance in multiple evaluation metrics across both the OT and ZF data sets, with a particular emphasis on precision, and it demonstrates accurate predictions of a greater number of positive envelopes among the top-ranked envelopes based on their scores. Moreover, within a cross-species data set of ZF, FREE identified a higher number of proteoform-spectrum matches (PrSMs), increasing the count from 50,795 to 52,927 compared to EnvCNN, the amalgamation of FREE with TopFD also exhibits a commendable capacity to discern 117,883 fragment ions, thus surpassing the 97,554 fragment ions identified through the application of EnvCNN in conjunction with TopFD. To further validate the performance of FREE, we have tested 10 a cross-species top-down proteomes containing 36 subdata set from ProteomeXchange. The results reveal that, after deconvolution with TopFD + FREE, TopPIC identifies more PrSMs across these 10 data sets in both the first and second rounds of experiments. These findings underscore the robustness and generalization capabilities of the FREE approach in diverse proteomes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".