COMPUTATIONAL EVIDENCE FROM TWO CORRELATED DATA SOURCES AT DIFFERENT MOLECULAR LEVELS FOR AF-VHD-SPECIFIC MICRORNA SIGNATURE
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 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