SACSANN: identifying sequence-based determinants of chromosomal compartments
Bibliographic record
Abstract
Genomic organization is critical for proper gene regulation and based on a hierarchical model, where chromosomes are segmented into megabase-sized, cell-type-specific transcriptionally active (A) and inactive (B) compartments. Here, we describe SACSANN, a machine learning pipeline consisting of stacked artificial neural networks that predicts compartment annotation solely from genomic sequence-based features such as predicted transcription factor binding sites and transposable elements. SACSANN provides accurate and cell-type specific compartment predictions, while identifying key genomic sequence determinants that associate with A/B compartments. Models are shown to be largely transferable across analogous human and mouse cell types. By enabling the study of chromosome compartmentalization in species for which no Hi-C data is available, SACSANN paves the way toward the study of 3D genome evolution. SACSANN is publicly available on GitHub: https://github.com/BlanchetteLab/SACSANN
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".