Analyzing Alzheimer's disease gene expression dataset using clustering and association rule mining
Bibliographic record
Abstract
Biological data like Gene expression datasets are already complex and are hard to process manually. The larger such types of datasets become, harder it becomes to manually process such datasets and makes more sense to use data mining techniques can be applied to discover or identify interesting patterns in the data. This paper presents various data mining techniques for analyzing Alzheimer's disease Gene Expression Dataset using Clustering and Association Rule Mining. The DNA-microarrays method allows acquiring a lot of data on gene expression. Due to the environmental and experimental factor, the variability of the gene expression is wide and unpredictable. This huge amount of data must be processed in order to retrieve relevant medical information. To do so, numerous methods of clustering are performed. There are two main goals: classify the gene expression and provide tools to retrieve the information. These techniques include basic data mining, two types of clustering and it discusses the use of association rules mining for such data. Emphasis is made on the particular dataset used in this research: the neurofibrillary tangles dataset that contains gene expression data for normal neurons and "sick" neurons for ten different patients suffering from a mid-stage Alzheimer's 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".