Genome-wide association study of colorectal \ncancer using evolutionary computing
Bibliographic record
Abstract
The heritability of complex diseases is usually ascribed to interacting genetic alterations. \nMany diseases have been found that are influenced by genetic factors. \nColorectal cancer (CRC) is a type of cancer starting from the colon or rectum that \nseriously threatens human health, and it has the chance to spread to other parts of \nthe human body. The cause of CRC is multifactorial, including age, sex, intake of \nfat, etc. In addition, it has been suggested that genetic factors also play an essential \nrole. Several genetic variations have been identified as associated with CRC. However, \nthey only explain a small portion of the heritability. More advanced computational \ntechniques are required to identify combinations of genetic factors. Recently, artificial \nintelligence algorithms have became a powerful tool for biomedical data analyses. In \nthis thesis, I design an evolutionary algorithm for the identification of combinations \nof genetic factors, i.e., single nucleotide polymorphisms (SNPs), that can best explain \nthe susceptibility to CRC.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".