Introducing the Concept of Second Neighbours to FPNC algorithm for Improving the Functional Modules Detection
Bibliographic record
Abstract
Proteins are biological polymers of amino acid residues. Pro- teins perform various functions within living organisms. Multiple pro- teins carry out these tasks by forming functional modules. Each func- tional module possesses community structure. For identifying functional modules, a lot of community detection or clustering algorithms were de- signed, but most of those algorithms suer by inappropriate clustering results which do not make any sense biologically. Though some of the al- gorithms came out with better results but too high time complexity was not of great help. Recently an ecient algorithm was designed which out- performed other existing algorithms, named Fast Protein Network Clus- tering or FPNC algorithm. We have worked on that algorithm and im- proved its performance by introducing the concept of second neighbours (neighbours of neighbours of any vertex), named as Second order Fast Protein Network Clustering algorithm or 2nd order FPNC algorithm. By coming up with the concept of 2nd neighbours, 2nd order FPNC algo- rithm has better scoring function and better functional module mapping results indicating ecient identication
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".