Biological Network, Gene Regulatory Network Inference Using Causal Inference Approach
Bibliographic record
Abstract
In system biology inference from gene regulatory network (GRN) is a challenging task. There exist different computational techniques to analyze the causal relationships between the pair of genes and to understand the significance of causal relationship in gene regulatory network. The DREAM4 insilico network structure and insilico gene expression time series dataset of DREAM challenge dataset is examined. This gene expression dataset of insilico of size 10 is analyzed for inferring causal relationships of the GRN inference. The analysis of dataset showing the gene expression data values are varying with respect to time. The paper focused on the different models of causal inference approach, Genetic Algorithm framework for the GRN inference. In this dataset, values associated with genes are analyzed using the Granger causality test and clustering to analyze the correlation and interaction or causal relationships among genes. The objective behind analysis and inferring causal information in the GRN is to reveal the study on gene activities to achieve more biological insights.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".