Multi-Dimensional Genomic Data Management for Region-Preserving Operations
Bibliographic record
Abstract
In previous work, we presented GenoMetric Query Language (GMQL), an algebraic language for querying genomic datasets, supported by Genomic Data Management System (GDMS), an open-source big data engine implemented on top of Apache Spark. GMQL datasets are represented as genomic regions (i.e. intervals of the genome, included within a start and stop position) with an associated value, representing the signal associated to that region (the most typical signals represent gene expressions, peaks of expressions, and variants relative to a reference genome.) GMQL can process queries over billions of regions, organized within distinct datasets. In this paper, we focus on the efficient execution of region-preserving GMQL operations, in which the regions of the result are a subset of the regions of one of the operands; most GMQL operations are region-preserving. Chains of region-preserving operations can be efficiently executed by taking advantage of an array-based data organization, where region management can be separated from value management. We discuss this optimization in the context of the current GDMS system which has a row-based (relational) organization, and therefore requires dynamic data transformations. A similar approach applies to other application domains with interval-based data organization.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".