Bibliographic record
Abstract
Python is a versatile scripting language that is widely used in industry and academia. In bioinformatics, there are multiple packages supporting data analysis with Python that range from biological sequence analysis with Biopython to structural modeling and visualization with packages like PyMOL and PyRosetta, to numerical computation and advanced plotting with NumPy/SciPy. In the proteomics community, Python began to be widely used around 2012 when several mature Python packages were published including pymzML, Pyteomics and pyOpenMS. This has led to an ever-increasing interest in the Python programming language in the proteomics and mass spectrometry community. The number of publications referencing or using Python has risen eight fold since 2012 (compared with the same time period before 2012), with multiple open-source Python packages now supporting mass spectrometric data analysis and processing. Computing and data analysis in mass spectrometry is very diverse and in many cases must be tailored to a specific experiment. Often, multiple analysis steps have to be performed (identification, quantification, post-translational modification analysis, filtering, FDR analysis etc.) in an analysis pipeline, which requires high flexibility in the analysis. This is where Python truly shines, due to its flexibility, visualization capabilities and the ability to extend computation with a large number of powerful libraries. Python can be used to quickly prototype software, combine existing libraries into powerful analysis workflows while avoiding the trap of re- inventing the wheel for a new project. Here, we will describe data analysis with Python using the pyOpenMS package. An extended documentation and tutorial can also be found online at https://pyopenms.readthedocs.io . To allow the reader to follow all steps in the tutorial, we will also describe the installation process of the software. Our installation is based on Anaconda, an open- source Python distribution that includes the Spyder integrated development environment (IDE) that allows development with pyOpenMS in a graphical environment.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.104 | 0.153 |
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".