Predictive modeling of therapy response in multiple sclerosis using gene expression data
Bibliographic record
Abstract
Transcription profiling studies reveal important insights in regards to molecular events that manifest in phenotypic outcomes such as response to drug therapy. Construction of computational models that accurately predict therapy response is only possible when precise data measurements, robust feature/gene selection, and advanced computational modeling methods are combined with stringent statistical validation and large scale verification of results. Due to the large number of gene expression measurements in transcriptional profiling studies, feature selection represents a bottleneck when constructing computational models. The degree of compromise between selection of the optimal feature set and computational efficiency results in many choices for candidate gene sets which leads to a wide range of classification accuracies. Furthermore, constructing a classification model using a larger-than-necessary gene set along with small number of samples may cause over-fitting the data, resulting in highly optimistic classification accuracies. In this study we present OSeMA, a fast, robust and accurate gene selection-classification framework which results in construction of classification models that are highly predictive of the rIFNB therapy response in multiple sclerosis patients. We assess the performance of OSeMA on held out test data. Additionally, we extensively evaluate OSeMA by comparing it to an exhaustive combinatorial gene selection-classification approach
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| 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".