Cancer Classification From DNA Microarray Using Genetic Algorithms and Case-Based Reasoning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There are many similarities in the symptoms of several types of cancer and that makes it sometimes difficult for the physicians to do an accurate diagnosis. In addition, it is a technical challenge to classify accurately the cancer cells in order to differentiate one type of cancer from another. The DNA microarray technique (also called the DNA chip) has been used in the past for the classification of cancer but it generates a large volume of noisy data that has many features, and is difficult to analyze directly. This paper proposes a new method, combining the genetic algorithm, case-based reasoning, and the k-nearest neighbor classifier, which improves the performance of the classification considerably. The authors have also used the well-known Mahalanobis distance of multivariate statistics as a similarity measure that improves the accuracy. A case-based classifier approach together with the genetic algorithm has never been applied before for the classification of cancer, same with the application of the Mahalanobis distance. Thus, the proposed approach is a novel method for the cancer classification. Furthermore, the results from the proposed method show considerably better performance than other algorithms. Experiments were done on several benchmark datasets such as the leukemia dataset, the lymphoma dataset, ovarian cancer dataset, and breast cancer dataset.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it