Protein-protein interaction confidence assessment and network clustering computational analysis
Bibliographic record
Abstract
Protein-protein interactions represent a crucial source of information for the understanding of the biological mechanisms of the cell. In order to be useful, high quality protein-protein interactions must be computationally extracted from the noisy datasets produced by high-throughput experiments such as affinity purification. Even when filtered protein-protein interaction datasets are obtained, the task of analyzing the network formed by these numerous interactions remains tremendous. Protein-protein interaction networks are large, intricate, and require computational approaches to provide meaningful biological insights. The overall objective of this thesis is to explore algorithms assessing the quality of protein-protein interactions and facilitating the analysis of their networks. This work is divided into four results: 1) a novel Bayesian approach to model contaminants originating from affinity purifications, 2) a new method to identify and evaluate the quality of protein-protein interactions independently in different cell compartments, 3) an algorithm computing the statistical significance of clusterings of proteins sharing the same functional annotation in protein-protein interaction networks, and 4) a computational tool performing sequence motif discovery in 5' untranslated regions as well as evaluating the clustering of such motifs in protein-protein interaction networks.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".