Text Mining Identifies Key Pattern Analysis and Process in Prostate Cancer
Bibliographic record
Abstract
One of the most typical malignancies in males is prostate cancer, and its global burden is increasing. Using text-mining technology, this study seeks to pinpoint important genes and biochemical processes related to prostate cancer. Using certain terms related to gene expression, PUBMED abstracts of interest were found. The extracted abstracts included gene pairings and functional connections. On the genes identified from the function interactions, biological procedures enrichment, network analysis, and gene prioritizing utilizing edge centrality of betweenness were carried out. For the modules containing at least five genes, which were retrieved from the network analysis, gene clustering and pathway enrichment analyses were built. The biological functions of the newly identified genes showed that they were involved in positive transcriptional regulation from the RNA polymerase II promoter, positive regulation of cell proliferation, and drug responsiveness. The prostate cancer enrichment analysis processes revealed that the NF signalling pathway, PI3k-Art signalling pathway, thyroid hormone signalling, and ErbB signalling pathways were enriched. According to the network analysis results, which were further sorted by their values for degree of between-ness, it was discovered that AKT1, AR, and KDM3A were the important genes. In conclusion, by concentrating on the discovered hub genes, prostate cancer can be medically treated.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".