ProteoForge: An Imputation-Aware Framework for Differential Proteoform Discovery in Bottom-Up Proteomics
Bibliographic record
Abstract
The human genome contains approximately 20,000 protein-coding genes. However, millions of diverse protein variants, called proteoforms, exist. Despite originating from the same gene, proteoforms often have distinct biological roles. In bottom-up proteomics, the aggregation of peptide measurements into protein-level quantities often obscures this information. Existing methods for differential proteoform discovery are limited by their handling of missing data, which can introduce a significant bias. To address this, we developed ProteoForge, which builds on an imputation-aware statistical model to identify and group covarying peptides into quantitatively differential proteoforms (dPFs). Benchmarking against existing methods demonstrated that ProteoForge provides high accuracy and stability in data sets with high rates of missing values, complex experimental designs, or varying signal strengths. Application of ProteoForge to proteomics data from lung cancer cells under hypoxia revealed extensive proteoform-level regulation hidden by a standard protein-level analysis.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".