COMPUTATIONAL EVIDENCE FROM TWO CORRELATED DATA SOURCES AT DIFFERENT MOLECULAR LEVELS FOR AF-VHD-SPECIFIC MICRORNA SIGNATURE
Bibliographic record
Abstract
The important roles of microRNAs (miRNAs) in the pathological process of the cardiovascular system have been recognized. However, identification of miRNAs related to valvular heart disease with atrial fibrillation (AF-VHD) has been difficult and very slow because of complex pathological mechanism of AF-VHD. Analysis of microarray expression profiles provides the possibility to rapid prediction of disease-regulating miRNAs and can lay a theoretical foundation for further experimental studies. A computational method is proposed to predict AF-VHD-specific miRNAs by combining miRNA and gene expression data, which are strongly correlated. Using the proposed method, a 45-miRNA AF-VHD-specific signature is predicted. Compared with other related results, 15 of 45 miRNAs are the same and the rest 30 miRNAs are different. Our analysis shows that 11 of 30 new miRNAs are associated with the diseases inducing AF-VHD and the remaining 19 miRNAs have good combinational discrimination power. Therefore, the AF-VHD signature we have predicted is confirmed to be reliable and specific. In a word, this study proposes an effective computational strategy in prediction of disease-regulating miRNAs and finds some AF-VHD-specific miRNAs, which provides new insight into the further experimental study and molecular mechanism leading to the development of AF-VHD.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".