Bibliographic record
Abstract
New Generation Sequencing (NGS) technologies offer new insights to researchers in the field of oncogenomics. These technologies provide valuable genetic information by rapidly detecting and identifying expected mutations to improve clinical treatments. To be used effectively, this large amount of data has to be processed, explored and interpreted carefully and quickly. Meanwhile, cancer research continues to publish new theories and findings based on large-scale collaborative projects that provide publicly available genomic and clinical cancer data. However, researchers have a hard time using the data to its full potential although it's readily available. Between the growing output size and complexity of NGS technologies, and the growing number of publicly available heterogeneous databases, processing and exploring this data can become a challenge for the average researcher. This paper presents GenomeViewer's functionalities, which specializes in visualization of somatic mutations in cancer genomics. This easy to use software will enable cancer researchers to seamlessly compare their data against publicly available resources. GenomeViewer uses "Big Data" technologies such as Spark and Parquet, and is based on the UC Berkeley's Analysis Data Model (ADAM) genomic format for cloud scale computing. Our hope is that GenomeViewer will become the preferred tool for viewing somatic mutations for researchers in cancer genomics.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.202 | 0.090 |
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".