Bibliographic record
Abstract
The use of DNA microarrays has gained wider acceptance as a standard tool for molecular biology studies over the past decade. In particular, biomedical studies embraced this technology as soon as arrays were produced for the common laboratory species. Slower to develop, however, has been the use of microarray screening with non-standard animal models, even though these species present fascinating physiological phenomena for study. The very high cost and huge amount of work involved in developing and producing a DNA array or microarray for a new species is prohibitive for most researchers working in comparative biology. The alternative is to explore the use of heterologous array hybridization, screening for stress-induced gene expression in one species using an array developed for another species. This chapter provides a comprehensive review of the current literature on heterologous DNA array hybridization and explores the factors that must be taken into account when performing heterologous microarray analysis on nonstandard species. Changes in methodology (e.g. hybridization conditions, stringency of washing) to optimize the percent cross reaction, the potential for false positives and false negatives to occur, and techniques for downstream analysis and confirmation of array data are all discussed. Examples of cross-hybridization using human microarrays are discussed using phylogenetically diverse species ranging from ground squirrels to frogs to snails. As with any new technology, the willingness to grasp cross-species analysis has been slow but the future looks bright for heterologous DNA hybridization and microarray analysis now that the initial hurdles have been overcome.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".