Array of Informatics: Applications in Modern Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The advent of microarray technology in the past decade has greatly enhanced gene expression studies and allowed for the acquisition of a vast amount of information simultaneously. Microarrays have been used in numerous scientific fields to identify new genes, to determine the transcriptional activity of cells, and to discover downstream targets of different loci. Recently, DNA microarrays have also been utilized in disease studies to determine outcomes at many levels including diagnosis, prognosis, and drug therapy. The promise of protein microarrays is to allow us to study the molecular interactions of protein, lipids, small molecules, and carbohydrates. They can be exploited to analyze a single protein pair interaction, to address changes in multiple protein levels as a response to treatment (i.e., drug or radiation), or in a pathological condition. Tissue microarrays allow the analysis of numerous tumor samples simultaneously. Finally, live cell-based microarrays provide an opportunity to study the function of the entire proteome en masse within living cells. However, these exciting new areas still have to overcome many inherent problems. In this review, we discuss novel microarray-based approaches that are in development and that have potential in applications for medicine, biotechnology, and basic research.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it