Comparing gene regulatory inferring algorithms with different perspective
Bibliographic record
Abstract
More than hundred algorithms were developed to infer Gene Regulatory Networks (GRN) describing relations between genes. GRN construction has been a field of interest to researchers since the beginning of the current century. Many competitions were held to encourage the development of GRN inference algorithms, such competitions offer synthetic data to enable the validation of proposed algorithms. A GRN is constructed from an adjacency matrix which contains relations between genes. The developers of many of the GRN inference algorithms set a threshold on the adjacency matrix to construct GRN based on high gene-gene relation weights. This threshold strategy was followed in previous studies to increase the accuracy of any algorithm but yet based on no well-known rule. A different perspective here is to compare different GRN inference algorithms without setting any threshold. Comparison in this work is among different GRN inference algorithms by implementing all algorithms with no threshold on values of adjacency matrices: Differential Equation methods (TSNI), Granger Causality, GP4GRN, GENIE3, NIMEFI (SVR), and PLSNET. Another comparison between different distance metric equations to create adjacency matrix is also studied in an attempt to construct GRN. GP4GRN and GENIE3 participate in producing best results for dream4 InSilico_Size10 while GENIE3 produce best results for all networks of dream4 InSilico_Size100.RÉSUMÉ.Plus de cent algorithmes ont é té dé veloppé s pour dé duire des ré seaux de ré gulation de gè nes (GRN) dé crivant les relations entre gè nes.La construction de GRN est un domaine d'intérêt pour les chercheurs depuis le début du siècle actuel.De nombreux concours ont é té organisé s pour encourager le dé veloppement d'algorithmes d'infé rence GRN.Ces concours offrent des donné es synthé tiques pour permettre la validation des algorithmes proposé s.Un GRN est construit à partir d'une matrice d'adjacence qui contient les relations entre les gè nes.Les dé veloppeurs de nombreux algorithmes d'infé rence GRN ont dé fini un seuil pour la matrice d'adjacence afin de construire un GRN basé sur des poids de relation gè ne-gè ne é levé s.Cette straté gie de seuil a é té suivie dans des é tudes pré cé dentes pour augmenter la pré cision de tout algorithme, sans pour autant s'appuyer sur aucune rè gle bien connue.Une autre perspective consiste à comparer diffé rents algorithmes d'infé rence GRN sans dé finir de seuil.La comparaison dans ce travail est faite entre diffé rents algorithmes d'infé rence GRN en implé mentant tous les algorithmes sans seuil sur les valeurs des matrices d'adjacence: Mé thodes d'é quation diffé rentielle (TSNI), causalité de Granger, GP4GRN, GENIE3, NIMEFI (SVR) et PLSNET.Une autre comparaison entre diffé rentes é quations mé triques de distance pour cré er une matrice d'adjacence est é galement é tudié e dans le but de construire un GRN.GP4GRN et GENIE3 contribuent à produire les meilleurs ré sultats pour dream4 InSilico_Size10, tandis que GENIE3 fournit les meilleurs ré sultats pour tous les ré seaux de dream4 InSilico_Size100.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".