Effective induction of gene regulatory networks using a novel recommendation method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we introduce a method based on recommendation systems to predict the structure of Gene Regulatory Networks (GRNs) making use of data from multiple sources. Our method is based on collaborative filtering approach enhanced with multiple criteria to predict the relationships of genes, i.e., which genes regulate others. We conduct experiments on two data sets to demonstrate the applicability and sustainability of our proposal. The first data set is composed of microarray data and Transcription Factor (TF) binding data, and it is evaluated by precision, recall and the F1-measure. The second data set is the Dream4 In Silico Network Challenge data set, and it is evaluated by the measures that are used during the challenge, namely the Area Under Precision and Recall curve (AUC-PR), the Area Under the Receiver Operating Characteristic curve (AUC-ROC) and their averages. The experimental results show that applying algorithms from the recommendation systems domain on the problem of inference of GRN structures is effective. Also, we observed that combining information from multiple data sets gives better results.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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