At the Intersection of Proteomics and Big Data Science
Bibliographic record
Abstract
As in other areas of big data science, a major bottleneck in proteomics is data analysis and data management. The primary technology used in proteomics is LC-MS/MS, which is used to resolve and collect fragment spectra of many thousands of peptides in a protease-digested proteome. To get a sense for the sheer volume of data generated by such experiments, imagine the data generated by a routine clinical LC-MS/MS method quantifying a single analyte and scale up by a factor of 100000. From LC-MS/MS proteomics experiments, proteins must first be identified from peptide fragment spectra, followed by relative quantification of all peptides, all the while trying to adhere to common quality metrics. There are dozens of search engines available for these steps, including MaxQuant, Mascot, SEQUEST, Byonic, and X!Tandem. Once a proteome profile has been generated, further data mining, including machine learning, is required to derive biological insight from the systems level view of the proteome. These steps can vary greatly and depend on the type of experiment performed (e.g., differential protein expression, protein interaction partners, posttranslational modifications) and the organism/tissue/cell type/etc. under study. Finally, the data must be archived, annotated, and shared publicly in adherence with community standards—now a requirement for publication in many top-tier journals.
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.
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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.018 | 0.037 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".