Articles selected from posters presented at the Tenth Annual International Conference on Research in Computational Biology – Preface
Notice bibliographique
Résumé
The synergies among biology, computing and other formal disciplines continue to produce a unique blend of domain-specific and methodological advances that is shaping the very fabrics of the new scientific method. Among the many examples of this phenomenon, the one offered by the unrelenting growth of bioinformatics and computational biology is unique in that nowhere else is the native lexicon of a natural science more directly conducive to digital representation and manipulation. The research articles contained in this Supplement originate from posters presented at the Tenth Annual International Conference on Research in Computational Molecular Biology (RECOMB 2006), which was held in Venice, Italy, on April 2–5, 2006. The RECOMB conference series was started in 1997 by Sorin Istrail, Pavel Pevzner and Michael Waterman. The previous meetings were held in Santa Fe, NM (USA); New York, NY (USA); Lyon, France; Tokyo, Japan; Montreal, Canada; Washington, DC (USA); Berlin, Germany; San Diego, CA (USA); and Boston, MA (USA). RECOMB 2006 was hosted by University of Padova in the Venice Convention Center at Cinema Palace, Venice Lido, Italy. The Tenth Edition of RECOMB was special in several ways. For one, the Program Committee, consisting of 38 specialists of the highest distinction in the field, included all past Committee and Conference Chairs as well as the Members of the Steering Committee. The Committee selected 40 papers out of the received submissions of well over 200. Some of the accepted papers were further expanded and refereed in order to appear in the special issue traditionally devoted to the Conference. For the Tenth Edition, however, in view of the high quality of the contributions submitted for poster presentation, it was felt that another Special Issue, devoted to expanded and duly refereed versions of poster submissions was also warranted. Thus, the present Supplement constitutes one more innovation brought about by the Tenth Anniversary of RECOMB. The eight enclosed papers emerged at the outset of a rigid selection and review and represent a vivid snapshot of work mature enough to be reported within vibrant frameworks still in the making. We hope that they will start one more tradition for RECOMB. This Issue was made possible thanks to the effort of many, in particular, the special task force set up for handling posters, which consisted of Luca Bortolussi (University of Udine, Italy), Giovanni Ciriello (University of Padova, Italy), Matteo Comin (University of Padova, Italy), Claudio Garrutti (University of Padova, Italy), Giosue Lo Bosco (University of Palermo, Italy), Sabrina Mantaci (University of Palermo, Italy) Cinzia Pizzi (University of Padova, Italy, and Helsinki, Finland), Simone Scalabrin (University of Udine, Italy), and Nicola Vitacolonna (University of Udine, Italy). We are also grateful to the external reviewers, the members of the Steering Committee and other colleagues who helped in the process. Finally, we express our thanks to the institutions and corporations who provided financial support for the conference: Broad Institute of MIT and Harvard, USA; College of Computing, Georgia Tech., USA; Department of Energy, USA; Department of Information Engineering, University of Padova, Italy; IBM Corporation, USA; ISMB, International Society for Computational Biology; AICA, Italian Association for Informatics and Automatic Computation; National Science Foundation, USA; University of Padova, Italy.
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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,437 | 0,258 |
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 ».