RECENT ADVANCES IN NETWORK PHARMACOLOGY OF FICUS: A REVIEW
Bibliographic record
Abstract
Natural products can alter disease-related biological targets, making them a crucial component of modern medicine. Medicinal plants include bioactive scaffolds that may be used to treat many ailments. The Moraceae family, which houses 50 genera and roughly 1400 species that thrive in tropical and subtropical climates across the world, has enormous botanical value. The main findings indicate that these species have strong anti-inflammatory, anti-cancer, and neuroprotective effects. Network pharmacology has developed as an important method for analyzing the complicated connections between herbal medicines and biological systems. The review emphasizes the use of network pharmacology and bioinformatics to investigate the mechanisms of action of the unique secondary metabolites in the genus Ficus. The study underlines the significance of this comprehensive strategy in drug discovery and development, particularly in these Moraceae plants. Furthermore, this study aims to review a system for identifying new chemical and exploring the biological potential for therapeutic researches in Moraceae plants.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".