The max-min high-order dynamic Bayesian network learning for identifying gene regulatory networks from time-series microarray data
Why this work is in the frame
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Bibliographic record
Abstract
We propose a new high-order dynamic Bayesian network (HO-DBN) learning approach, called Max-Min High-Order DBN (MMHO-DBN), for discrete time-series data. MMHO-DBN explicitly models the time lags between parents and target in an efficient manner. It extends the Max-Min Hill-Climbing Bayesian network (MMHC-BN) technique which was originally devised for learning a BN's structure from static data. Both Max-Min approaches are hybrid local learning methods which fuse concepts from both constraint-based Bayesian techniques and search-and-score Bayesian methods. The MMHO-DBN first uses constraint-based ideas to limit the space of potential structure and then applies search-and-score ideas to search for an optimal HO-DBN structure. We evaluated the ability of our MMHO-DBN approach to identify genetic regulatory networks (GRN's) from gene expression time-series data. Preliminary results on artificial and real gene expression time-series are encouraging and show that it is able to learn (long) time-delayed relationships between genes, and faster than current HO-DBN learning methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it