In Silico Modelling of the TP53 pathway in Colorectal cancer
Bibliographic record
Abstract
Abstract Colorectal cancer (CRC) is the third most frequent cancer and the fourth cause of cancer death worldwide. Inactivation of The TP53 pathway is a key genetic alteration in CRC Development. The TP53 gene triggers multiple cellular response by interaction with downstream genes. Researchers are continually seeking to explore such interactions to gain knowledge that can be used for disease management and prevention. This result in several large-scale transcriptomic technologies to estimate whole genome expression profiles for CRC. However, such analytical approaches generate massive volumes of data, which need careful processing to extract meaningful information using statistical and computational approaches. Some of these approaches have been dedicated to studying the disease through interrogation of pathway models based on molecular data and based on mining of the literature corpus in order to obtain deep insights which could help in drug discovery and the achievement of personalized medicine for cancer. These methods tend to address the dimensionality and complexity issues associated with large-scale technologies by presenting the data using signalling network models and pathway knowledge graphs. However, the possibility of identifying novel interactions and disease drivers remains limited, as most of these approaches are based on knowledge obtained from the literature through manual curation. This research applies ANN approaches for pathway data mining through a series of analyses leading to the identification of key interactions and novel drivers associated with the TP53 pathway in CRC. Which can be used to enhance knowledge about the pathway through identifying new elements that can be used to develop novel and effective therapeutic approaches for disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".