Bibliographic record
Abstract
ABSTRACT The discovery of molecular interactions is crucial towards a better understanding of complex biological functions. Particularly protein-protein interactions (i.e., PPIs), which are responsible for a variety of cellular functions from epigenetic modifications to enzyme-substrate specificity, have been studied extensively over the past decades. Position-specific scoring matrices (PSSM) in particular are used extensively to help determine interaction specificity or candidate interaction motifs. However, not all studies successfully report their results as a candidate interaction motif. In many cases, this is the result of a lack of analysis tools for simple analysis and motif generation. Peptide Specificity Analyst (PeSA) is developed with the goal of filling this gap and providing an analysis software to aid peptide array analysis and subsequent motif generation. PeSA utilizes two models of motif creation: (1) frequency-based using a peptide list, and (2) weight-based using a quantified matrix. The ability to generate motifs effortlessly will make analyzing, interpreting and sharing peptide specificity study results in a simple and straightforward process. GRAPHICAL ABSTRACT HIGHLIGHTS Biological motifs are widely used representations for peptide specificity analysis. PeSA populates a list of peptides matching a set threshold from a quantified matrix. Frequency-based motif using a peptide list to spot residue patterns. Use of quantified matrices to create weight-based motifs using residue positions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.027 |
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".