Prediction of CircRNA-Disease Associations Using KATZ Model Based on Heterogeneous Networks
Bibliographic record
Abstract
Circular RNAs (circRNAs) are a large group of endogenous non-coding RNAs which are key members of gene regulatory processes. Those circRNAs in human paly significant roles in health and diseases. Owing to the characteristics of their universality, specificity and stability, circRNAs are becoming an ideal class of biomarkers for disease diagnosis, treatment and prognosis. Identification of the relationships between circRNAs and diseases can help understand the complex disease mechanism. However, traditional experiments are costly and time-consuming, and little computational models have been developed to predict novel circRNA-disease associations. In this study, a heterogeneous network was constructed by employing the circRNA expression profiles, disease phenotype similarity and Gaussian interaction profile kernel similarity. Then, we developed a computational model of KATZ measures for human circRNA-disease association prediction (KATZHCDA). The leave-one-out cross validation (LOOCV) and 5-fold cross validation were implemented to investigate the effects of these four types of similarity measures. As a result, KATZHCDA model yields the AUCs of 0.8469 and 0.7936+/-0.0065 in LOOCV and 5-fold cross validation, respectively. Furthermore, we analyze the candidate association between hsa_circ_0006054 and colorectal cancer, and results showed that hsa_circ_0006054 may function as miRNA sponge in the carcinogenesis of colorectal cancer. Overall, it is anticipated that our proposed model could become an effective resource for clinical experimental guidance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".