Analysis of Differences Between Human Alternative Splicing Protein Isoforms and Their Links to Diseases
Bibliographic record
Abstract
Alternative Splicing (AS) is a process that is believed to have links to cellular function changes and some diseases in humans.Although AS was first discovered in the 1970s, not much research has been conducted on its role in functional implications on the proteome level.This study aims to use PIPE, a protein-protein interaction prediction algorithm, along with a tissue expression dataset to build a pipeline that differentiates between AS isoform products by analyzing isoform sequence changes, functional changes, and tissue expression changes that AS introduces.The study found that isoform sequence changes in alternative isoforms tend to be conserved deletions of amino-acid sub-sequences.The study also found that there is a statistically significant overlap between PIPE-predicted protein-protein interaction (PPI) network changes and tissue expression changes of alternatively spliced isoforms (ASIs) relative to their canonical isoforms (CIs) with a p-value of 8.25×10 -5 .Finally, among the analysis pipeline top ten genes with predicted significant ASIs' PPI network changes, LMO2, THOC2, and UBE2L3 are genes that were suspected of having links to different diseases such as basel-type breast cancer, intellectual disability (ID) and numerous autoimmune diseases according to literature studies.i Firstly, I would like to thank my supervisor Dr. Frank Dehne for his outstanding support throughout my Master's degree.Dr. Dehne provided me with invaluable advice, support, and kindness during my Master's journey.I could not have asked for a better supervisor.I would
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".