Notice bibliographique
Résumé
In our work, we collected biological entities and their relations, and put them into these networks. The below description is in our manuscript. As soon as the manuscript is published, we will present the access information in this page. --- The molecule type consisted of genes, and the function type consisted of GO terms. The phenotypic type consisted of diseases that are MeSH terms in disease categories, and the concepts in some UMLS concept types: Congenital Abnormality, Acquired Abnormality, Finding, Pathologic Function, Disease or Syndrome, Mental or Behavioral Dysfunction, Cell or Molecular Dysfunction, Sign or Symptom, Anatomical Abnormality, and Neoplastic Process. Those UMLS concept types contained a considerable number of the MeSH terms in disease categories. 18 types of biological relations were defined with their pre-defined relation types and their original resources. The 18 types are presented in the Supplementary Material (Supplementary Table S2). From the CODA, 13 types of relations for this study were defined by combining the six original resources and the eleven relation types that were pre-defined in the CODA. The six original resources were BioGRID41, RegNetwork, TRANSFAC42, EndoNet43, KEGG, GO, and PhenoGO44, which were released until 2016. The six pre-defined types in CODA were: Undirected Link, Directed Link, Positive Increase, Positive Decrease, Negative Increase, and Negative Decrease. Moreover, their reversal types were also made. The Undirected Link means a non-directional association, and the Positive Decrease means that the activity or the amount of a receiver decreases as the activity or the amount of an actor increases19. From the UMLS (version 2016AA), four types of relations were defined for the present study. They were defined only by their original resources: MedlinePlus45, MTHMST46, NCI47, and OMIM. Although the UMLS has more than 200 resources, only the four resources provided a considerable number of relations among the nodes that we considered. The pre-defined types of relations in the four resources were too many for us to properly categorize them. Therefore, we ignored them when defining the types of relations for this study. The MEDLINE (version 2017) provides the co-occurrence frequency of two MeSH terms. It says how many times two MeSH terms have been attached to the same biomedical literature. Only one type of relations was defined for our research. Among functions or phenotypes, if two nodes had any co-occurrence frequency, we determined there is a co-occurrence relation between them. We tried to separate them according to their frequency. However, the performance, AUROC values, in the application was best when the co-occurrence relations were not separated.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,007 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».