Bibliographic record
Abstract
The discovery of relationships between variables in a biological system is pivotal in bioinformatics and systems biology. Biological network inference is complex and new integrative techniques still need to be explored. In this thesis, a 2-level granular approach is proposed to model, analyze, and compare biological networks. The system comprises two levels of granulation with a base data layer, representing the raw data that are transformed into a common discrete type and then analyzed at the first level of analysis for pairwise relationships, based on normalized statistically significant expected mutual information (NSEMI), and at the second level of analysis for convergent associations that identify highly associative nodes based on two different measures of convergent mutual information: cumulative connectivity and high connectivity. The proposed approach is tested by using benchmark data with known network structures from Yeast and E.coli. The level 1 analysis produces predictions with accuracies above 95%. The level 2 analysis, as expected, results in significant improvements in accuracy due to significant reductions in false positives. Next, the benchmark data is applied to test two additional network discovery methods: K2 and BDeu, used for Bayesian networks. Our approach outperforms both Bayesian methods. The level 2 analysis is integrated into two well cited and adopted information based algorithms: CLR and ARACNE. The results demonstrate similar improvements in false positives and accuracy. Finally, the approach is applied to the Enviropig™ datasets, by using data collected from the muscle tissue for both transgenic and conventional samples. Analyses reveal visible differences between the sexes, as well as between the transgenic and conventional pig lines. Also, strong associations are evident among the minerals and the fatty acids, regardless of the sex and pig line. The detected association patterns are observed to be stronger among males and conventional pigs. Key Words: systems biology, granular computing, transgenic, significant expected mutual information, Enviropig™, mutual associations, convergent associations.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 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 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".