Model-based clustering with genes expression dynamics for time-course gene expression data
Bibliographic record
Abstract
Microarray technologies are emerging as a promising tool for genomic studies. A huge body of time-course gene expression data has been and will continuously be produced by microarray experiments. Such gene expression data contains important information and has been proven useful in medical diagnosis, treatment, and drug design. The challenge now is how to analyze such data to obtain the inherent information. Cluster analysis has played an important role in analyzing time-course gene expression data. However, most clustering techniques do not take into consideration the inherent time dependence (dynamics) of time-course gene expression patterns. Accounting for the inherent dynamics of such data in cluster analysis should lead to higher quality clustering. This paper presents a model-based clustering method for time-course gene expression data. The presented method uses Markov chain models (MCMs) to account for the inherent dynamics of time-course gene expression patterns and assumes that expression patterns in the same cluster were generated by the same MCM. For the given number of clusters, the presented method computes cluster models using an EM algorithm and an assignment of genes to these models that maximizes their posterior probabilities. Further, this study employs the average adjusted Rand index (AARI) to evaluate the quality of clustering. The improved performance of the presented method is demonstrated by comparing to the k-means method on a publicly available dataset.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".