Exploring Epigenetic Drug Discovery Using Computational Approaches
Bibliographic record
Abstract
The misregulation of epigenetic mechanisms has been linked to disease. Current drugs that treat these dysfunctions have had some success, however many have variable potency, instability in vivo and lack target specificity. This may be due to the limited knowledge of epigenetic mechanisms, especially at the molecular level, and their association with gene expression and its link to disease. Computational approaches, specifically in molecular modeling, have begun to address these issues by complementing phases of drug discovery and development, however, more research is needed on the relationship between genetic mutation and epigenetics and their roles in disease. Gene regulatory network models have been used to better understand diseases, however inferring these networks poses several challenges. To address some of these issues, a multi-label classification technique to infer regulatory networks (MInR), supplemented by semi-supervised, learning is presented. MInR's performance was found to be comparable to other methods that infer regulatory networks when evaluated on a benchmark E. coli dataset. In order to better understand the association of epigenetics with gene expression and its link with disease, MInR was used to infer a regulatory network from a Kidney Renal Clear Cell Carcinoma (KIRC) dataset and was supplemented with gene expression and methylation analysis. Gene expression and methylation analysis revealed a correlation between 5 differentially methylated CpGs and their matched differentially expressed transcripts. Incorporating this information into a network allowed for the visualization of potential systems that may be involved in KIRC. Future analysis of these systems alongside the drug discovery and development process may lead to the discovery of novel therapeutics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".