Bibliographic record
Abstract
We present a framework for detecting probes in oligonucleotide microarrays that may add significant error to measurements in hybridization experiments. Four types of so-called degenerate probe behavior are considered: secondary structure formation, self-dimerization, cross-hybridization, and dimerization. The framework uses a well-established model for computing the free energy of nucleic acid sequence hybridization and a novel method for the detection of patterns in hybridization experiment data. Our primary result is the identification of unique patterns in hybridization experiment data that are shown to correlate with each type of degenerate probe behavior. A support function for identifying degenerate probes from a large set of hybridization experiments is given and some preliminary experimental results are given for the Affymetrix HuGeneFL GeneChip. Finally, we show a strong relationship between the Affymetrix discrimination measure for a probe and the free-energy estimate from theoretical models of hybridization. In particular, probes on the HuGeneFL GeneChip with high free-energy estimates (weak hybridization) have almost always approximately zero discrimination. The framework can be applied to any Affymetrix oligonucleotide array, and the software is made freely available to the community.
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 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".