Zenodo Archive for "Improved radiation expression profiling by sequential application of sensitive and specific gene signatures"
Bibliographic record
Abstract
This is the Zenodo archive for the research article "Improved radiation expression profiling by sequential application of sensitive and specific gene signatures" (Mucaki et al. submitted). This archive contains the following dataset which contains violin plots which visualize gene expression patterns of individuals with various hematological conditions or RNA viral infections: "Violin_Plots_for_Mucaki_et_al_2021.zip" Description: Normalized gene expression distributions for genes found in the radiation gene signatures described in the Mucaki et al. (submitted) study (M1-M4, KM3-KM7, SM1-SM5) were visualized by as violin plots. Plots are divided into 3 main sections: Expression of individuals with a hematological condition (Malaria, S. aureus, Sickle Cell and Thrombosis); expression of influenza A infected individuals; or expression from individuals infected with dengue. For each dataset evaluated, diseased individuals are divided by those correctly predicted by the radiation models to be non-irradiated (true negatives; TN), and those erroneously predicted to be radiation-exposed (false positives; FP). If a gene is missing from a particular dataset, both columns for that particular dataset will be empty. Expression from radiation-exposed individuals from Gene Expression Omnibus (GEO) datasets GSE6874 and GSE10640 are also present on the right side of each violin plot (irradiated [Irr.] and non-irradiated [Non]). Note that the Influenza violin plots display expression from 5 sets of influenza infection datasets, and as a consequence, these plots exclude the radiation dataset GSE6874. The number of individuals that comprise each violin plot can be obtained from the Supplementary Tables of Mucaki et al. (submitted). These tables describe the number of false positive count of all models for each dataset evaluated. To determine the number of true negatives present in each violin plot, simply subtract the false positive count from the total number of individuals with the associated disease in the dataset being evaluated (provided in each supplementary table). False positive counts for bloodborne diseases can be found in Supplementary Tables S2B (M1-M4, KM3-KM7) and S6B (SM1-SM5). False positive counts for both Dengue and Influenza Datasets are found in Supplementary Tables S2A (M1-M4, KM3-KM7) and S6D (SM1-SM5).
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.211 | 0.193 |
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".