Implications of<scp>RNA</scp>‐binding Proteins for Human Diseases
Bibliographic record
Abstract
Abstract RNA‐binding proteins play pivotal roles in ribonucleic acid (RNA) metabolism. The identification of mRNA targets of RNA‐binding proteins has also contributed to the delineation function of these proteins. The different tissue specificity and subcellular localisation of the RNA‐binding proteins show that these proteins can regulate specific aspects of mRNA processing and function in cells, from splicing and transport to translation and stability. Mutations, deletions and/or autoimmune reactions affecting RNA‐binding proteins lead to alterations in cellular processes, normal development and various disorders. The vast majority of these diseases are neurological or neuromuscular disorders and includes myotonic dystrophy, spinal muscular atrophy, oculopharyngeal muscular dystrophy, amyotrophic lateral sclerosis, fragile X syndrome, fragile X associated tremor/ataxia syndrome and paraneoplastic opsoclonus‐myoclonus ataxia. Altered expression of RNA‐binding proteins is also a common feature in various cancers. Understanding the molecular mechanisms of RNA‐binding proteins aberrations in disease could lead to better‐targeted therapies. Key Concepts: RNA‐binding proteins are key components in RNA metabolism. RNA‐binding proteins contain modular amino acid sequences that mediate RNA binding. The two largest RBP families contain the RNA recognition motif (RRM) and the K homology (KH) domains. Deleterious RNA‐dominant loss‐of‐function or gain‐of‐function mechanisms are associated with defects in RBPs. Several neurological and neuro‐muscular diseases are linked to defects in RBPs. FXS is caused by CGG repeat in the 5′ untranslated region of theFMR1gene. Antibodies against RBPs Hu and Nova are implicated in the pathogenesis of paraneoplastic neurologic syndromes. DM1 is associated with the accumulation of RNA aggregates and misregulation of the RBPs, MBNL1 and CUGBP1. Translocations of genes encoding RBPs and aberrant expression RBPs have been associated with various cancers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".