A Comprehensive Expression Analysis of the Arabidopsis Proline-rich Extensin-like Receptor Kinase Gene Family using Bioinformatic and Experimental Approaches
Bibliographic record
Abstract
The Arabidopsis proline-rich extensin-like receptor kinase (PERK) family consists of 15 predicted receptor kinases. A comprehensive expression analysis was undertaken to identify overlapping and unique expression patterns within this family relative to their phylogeny. Three different approaches were used to study AtPERK gene family expression, and included analyses of the EST, MPSS and NASCArrays databases as well as experimental RNA blot analyses. Some of the AtPERK members were identified as tissue-specific genes while others were more broadly expressed. While in some cases there was a good association between these different expression patterns and the position of the AtPERK members in the kinase phylogeny, in other cases divergence of expression patterns was seen. The PERK expression data identified by the bioinformatics and experimental approaches were found generally to show similar trends and supported the use of data from large-scale expression studies for obtaining preliminary expression data. Thus, the bioinformatics survey for ESTs and microarrays is a powerful comprehensive approach for obtaining a genome-wide view of genes in a multigene family.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".