Systematic study of microRNAs in within-species and cross-species interactions
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small noncoding RNAs that play important roles in posttranscriptional gene regulation in eukaryotic cells.They target mRNAs to suppress gene expression by inhibition of translation or promotion of mRNA decay.miRNAs have emerged as key gene regulators in diverse biological pathways, with over half of the human transcriptome predicted to be under miRNA regulation.In conjunction with other post-transcriptional control pathways, the majority of the gene expression cascade can be under direct or indirect miRNA regulation.Given this far-reaching role, it is not surprising that disruption of miRNA functions can contribute to many human diseases.Both computational and experimental methods are now available for identifying miRNA target sites.Many tools have been developed for functional analysis of miRNA targets to help understand miRNA functions.Exosomes are small (50-90 nm) extracellular vesicles (EVs) composed of lipids, proteins, and genetic materials that can mediate communications between cells.Such communications can play important roles in the regulation of physiology and pathological processes.Recently, some studies have demonstrated that exosomes containing small noncoding RNAs from pathogens have potential to regulate the host immune response, and gut microbiota can also be affected by host exosomal miRNAs.Lastly, exosomal miRNAs originating from foreign organisms (xeno-miRNAs), have been detected in the host's circulatory system.Therefore, exosomal miRNAs can potentially play important roles in cross-species interactions.The central objective of my thesis project is to develop bioinformatics tools to help better understand the roles of miRNAs in both within-and cross-species gene regulations.I worked on three different sub-projects to achieve this goal.Firstly, I developed the miRNet web application to
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".