Microarray Analysis of Alternative Splicing
Bibliographic record
Abstract
Alternative splicing, defined as the generation of multiple RNA transcript species from a common mRNA precursor, is one of the mechanisms for the diversification and expansion of cellular proteins from a smaller set of genes. Current estimates indicate that at least 60% of genes in the human genome exhibit alternative splicing. Over the past decade, alternative splicing has increasingly been recognized as a major regulatory process with a critical role in normal development. Furthermore, the importance of alternative splicing in disease development and treatment is starting to be appreciated. Therefore, an increasing number of high-throughput genomics and proteomics studies are being performed in order to delineate (a) the changes in alternative splicing under various conditions; (b) the properties and functions of protein isoforms; and (c) the splicing and alternative splicing regulation process. Strategies for the parallel analysis of alternative splice forms by microarray experiments have been conceived, and examples have been published. In addition to the differences in microarray probe design, the analysis of microarrays with probes for exons, exon/exon junctions as well as specific splice forms is significantly different from the standard experiment. Several methods are being developed in order to address the particular needs of alternative splicing microarrays. Many reviews have already dealt with alternative splicing. However, high-throughput analysis methods that are becoming increasingly popular have not received much attention. Here, we will provide an overview of the tools and analysis methods that were developed specifically for alternative splicing microarrays described in terms of specific experiments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".