Highlighting the Role of Cdk6 Associated MicroRNAs in Cancer Treatment Using In Silico Approaches
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small non-coding RNA’s that controls the regulation of a gene. Due to the over expression or under expression of miRNAs it leads to cause tumor or any other type of cancers such as, melanoma, lymphoma, cardiovascular issue, breast cancer etc. So, miRNAs can be used as a drug target for cancer therapy. This study aimed to check binding cavities of microRNA's involved in regulation of CDK6 protein. There are 23 different families of miRNAs that are involved in regulation of CDK6. Each family has one or more miRNAs. All these miRNAs are involved in the up regulation or downregulation of a gene, which lead to different type of cancers. All miRNAs of each family docked with mRNA CDK6 protein. After performing in silico analysis of binding interactions of mRNA with miRNAs the results were further refined by their comparison with information regarding their energies, interaction of the mRNA and miRNAs. The results show that all miRNAs lie in Protein Kinase domain, but the residues that lie is different within the families and across the families.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".