Bibliographic record
Abstract
This thesis presents a preliminary study to evaluate the ability of our sequence-based protein-protein interaction prediction tool (PIPE) to detect changes in the interactome caused by non-synonymous Single Nucleotide Polymorphisms (SNPs).High performance computing is used to explore the eect of SNPs on the interactome and results are mixed.Sequencebased PPI prediction is not sensitive to SNP-induced interactome changes, however sequence-based PPI interaction site prediction can be used to extract some information regarding interactome changes.PIPE on its own does not perform well on detecting the eects of SNPs.However, experiments using a contemporary sequence-based method conrm that this lack of sensitivity is not limited to PIPE, but likely aects all sequence-based methods.Using the interaction site prediction feature of PIPE, PIPE-Sites, a number of interactions are identied for which there is reason to believe that they may, in fact, be aected by SNPs.These interactions are identied using subsets of disease-causing SNPs.To examine the eect of collections of co-occuring SNPs, genotypes are extracted from data arising from the 1000 Genomes initiative.For these genotypes, it appears that PIPE-Sites is able to identify subsets of interactions where SNPs are enriched and therefore likely aected by that particular genotype.List of Tables 3.1 Amino Acid Conversion Table . . . . . . . . . . . . . . . . . . . .
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".