The Second International Symposium on Languages in Biology and Medicine (LBM) 2007
Notice bibliographique
Résumé
The 2nd International Symposium on Languages in Biology and Medicine (LBM2007) was held in Singapore in December 2007. It provided a renewed opportunity for interaction between language professionals with different methodological backgrounds. In particular original research and applications of language technologies in biology and medicine were solicited. The relevant themes are listed below. · Natural language: text mining, retrieval and management; · Ontology language: ontology construction, extension and management; · Logic language: knowledge representation and induction; · Sequence language: RNA structure prediction, protein domain prediction; · Database language: database interface, query language; · Visualization language: information visualization, molecular visualization; A total of 47 submissions were received and reviewed by a 37 strong program committee and 19 additional reviewers from Asia, Europe and North America. The committee represented the six afore mentioned research disciplines and participated in a double blind review process with 3 reviews per paper. A selection of 12 papers was accepted (25.5% acceptance rate) for long oral presentations during the symposium and publication in the LBM special issue of BMC Bioinformatics. A further 11 out of the remaining 35 papers were selected for short oral presentation (31.4% acceptance rate). This LBM special issue of BMC Bioinformatics consists of 10 long oral presentation papers, as some papers were withdrawn due to unforeseen circumstances. A proceedings of LBM short paper presentations comprising of 7 papers was published by CEUR and is available at http://ceur-ws.org/Vol-319. From these two editions 8 papers are from Asia (China 1, Japan 3, Korea 1, Singapore 1, TaiWan 2), and 9 papers are from Europe and North America (Finland 1, Hungary 1, Sweden 1, UK 4, Canada 1, USA 1). These papers were presented in five sessions, namely (a) Terminology and Named Entities, (b) Text Classification 1, (c) Text Classification 2, (d) Text Mining, (e) Ontology and Logic. In addition to technical paper presentations, the 3 keynote presentations of the symposium addressed terminology integration (Olivier Bodenreider), text mining services (Sophia Ananiadou) and ontology alignment (Patrick Lambrix). The panel discussion chaired by Junichi Tsujii concluded the symposium with an examination of the synergies among the biomedical language and knowledge technologies. In conclusion we express our deep appreciation to the program committee members and the additional reviewers who worked on a very tight schedule, sharing their valuable time and formidable expertise in support of the LBM review process. We also thank, Ho-Joon Lee from KAIST and Chen Bin from I2R / NUS for their assistance with the EasyChair system, the LBM website and other miscellaneous tasks. We also wish to thank Jong C. Park, Limsoon Wong, the two general chairs and See Kiong Ng for their help and suggestions.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».