BioNovoGene mzkit: Data toolkits for processing NMR, MALDI MSI, LC-MS and GC-MS raw data, chemoinformatics data analysis and data visualization
Bibliographic record
Abstract
Mzkit is an open source raw data file toolkit for mass spectrometry data analysis, provides by the BioNovoGene corporation. The features of mzkit inlcudes: raw data file content viewer(XIC/TIC/Mass spectrum plot), build molecule network, formula de-novo search and de-novo annotation. This open source mass spectrometry data toolkit is developed at the BioDeep R&D laboratory and brought to you by BioNovoGene corporation. Downloads: http://www.biodeep.cn/downloads?lang=en-US new add tissue morphology map overlaps to MSI viewer new add tissue morphology map editor feature to create custom tissue region for data analysis new add HE stain image analysis module new add a new general table data viewer for open microsoft excel table files new add a general data visualization module for plot data based on the table viewer content new add new ggplot package for data plot pipeline task enhancement add data visualization template rendering for MS-imaging plot enhancement enable view multiple sample MS-imaging data enhancement add online pubchem metabolite database query function for the ion feature in MSI raw data enhancement add data compatibility with the bruker SCiLS lab software enhancement make improvements of the ms1 peak list data annotation function enhancement update internal metabolite database, extends database list from KEGG only to KEGG/lipidmaps/chebi enhancement make the molecular networking viewer interactive enhancement add ms1 peak deconvolution function to raw data viewer enhancement add peak finding analysis feature to the general signal data analysis enhancement new application installer experience enhancement upgrade the internal Rstudio environment from .NET4.8 assembly to .NET6.0 assembly enhancement add mzwork project file for share the workspace between the device enhancement make improvements of the MRM/GCMS targetted data viewer fixed add fix patch script to install internal Rstudio environment fixed upgrade mzPack format to version 2.0, make improvements of the data compatibility between the single sample data and multiple sample data fixed make bugs fixed of the style tweaking for raw data plot viewer fixed handling of the data serialization error when raw data file its file path is a windows UNC path
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.123 | 0.131 |
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".