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Enregistrement W2961295341 · doi:10.1093/bioinformatics/btz439

ISMB/ECCB 2019 Proceedings

2019· article· en· W2961295341 sur OpenAlexaff
Yana Bromberg, Nadia El-Mabrouk, Predrag Radivojac

Notice bibliographique

RevueBioinformatics · 2019
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueSingle-cell and spatial transcriptomics
Établissements canadiensUniversité de Montréal
Organismes subventionnairesnon disponible
Mots-clésComputer science

Résumé

récupéré en direct d'OpenAlex

The biennial joint meeting of ISMB (27th Annual Conference on Intelligent Systems for Molecular Biology) and ECCB (18th European Conference on Computational Biology) was held in Basel, Switzerland, July 21–25, 2019. ISMB is the flagship conference of the International Society for Computational Biology and the world’s premier forum for dissemination of scientific research in computational biology and its intersection with other areas. ECCB is similarly a top venue in the field, with a long tradition of publishing and presenting world-class research. This special issue serves as the Proceedings of ISMB/ECCB 2019. Following a successful model with a centralized manuscript review and acceptance process, this year’s conference organization provided the community with a unified submission interface for high-quality papers in the field of computational biology. The review process across 10 scientific areas was supervised by the Senior Program Committee (SPC), consisting of the Proceedings Chairs and Area Chairs (AC). About a third of the ACs were nominated by the Communities of Special Interests (COSIs), reflecting the desire of the ISMB/ECCB 2019 Steering Committee to involve COSIs in conference organization and the review process. Overall, the SPC consisted of 21 individuals; see Table 1. Thematic areas of ISMB/ECCB 2019 Note: The table lists the ACs for each theme, the number of reviewed papers, the number of accepted papers, and the acceptance rate for each area. A special area of General Computational Biology was created for papers not fitting in any of the predefined areas. Thematic areas of ISMB/ECCB 2019 Note: The table lists the ACs for each theme, the number of reviewed papers, the number of accepted papers, and the acceptance rate for each area. A special area of General Computational Biology was created for papers not fitting in any of the predefined areas. The scope of the conference includes theoretical papers, algorithms and statistical methods that allow for important novel biological insights and broadly defined intellectual contributions. We invited submission of papers in nine general scientific areas, organized by the relevant biological problems (Table 1). The 10th area of General Computational Biology was created to accommodate innovation outside of specified fields. The papers submitted to this area were largely in Text Mining, Mass Spectrometry and Visualization subfields, indicating community interest in these areas. All papers submitted to all areas were expected to present methodological and scientific contributions to the specific areas of submission. Abstracts, previously published papers, position papers, perspectives and reviews are not eligible for submission to the ISMB/ECCB Proceedings track. In total, 366 papers were submitted—a 10.6% increase over ISMB 2018. Of these, 363 papers were sent to review, receiving 1303 reviews from 388 Program Committee members. This constitutes an average of 3.6 reviews per submission. Three submissions had two completed reviews, 175 had three, 157 had four, 24 had five and four submissions had six completed reviews. The conditional acceptance information was provided to the authors a month after the submission deadline and final acceptance conveyed in another month. Overall, 69 manuscripts were accepted for a final acceptance rate of 18.9%. The distribution of papers reviewed in different areas is shown in Table 1. As was the case in 2018, the authors had the opportunity to request that their accepted manuscripts be presented in one of the COSI sessions. The most requests were made for MLCSB (64), followed by NetBio (39), Evolution (37), RegSys (37), TransMed (34), HiTSeq (33), Function (30), Microbiome (18), VarI (16), RNA (15), Text Mining (11), BioVis (10), Bio-Ontologies (7), CAMDA (4), Education (2) and CompMS (2). A further 35% of the submitted manuscripts made no specific COSI session request. Final assignments to COSI sessions were made by the Proceedings Chairs on the basis of the author and COSI requests. Three papers were assigned to the General Computational Biology session for presentation. The distribution of accepted papers to COSIs is shown in Table 2. Distribution of ISMB/ECCB 2019 Proceedings papers to COSIs Distribution of ISMB/ECCB 2019 Proceedings papers to COSIs We thank the ISMB/ECCB 2019 Steering Committee for their support and guidance. Special thanks go to the SPC and reviewers for their fantastic work dedicated to maintaining the high-quality standards of ISMB/ECCB in a compressed time frame. We are particularly grateful to Steven Leard, Diane Kovats and Pat Rodenburg for world-class organizational support. We would also like to thank the COSIs for nominating the ACs and the COSI contacts for their help in identifying reviewers and incorporating accepted papers into their programs. Finally, we thank the community for their interest and engagement in this conference—ISMB/ECCB 2019 belongs to you! With this, we invite you to read the Proceedings of ISMB/ECCB 2019. See you next year in Montreal, Canada, for ISMB 2020. Conflict of Interest: none declared.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,260
Score d'incertitude au seuil0,870

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,005
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,001
Communication savante0,0050,002
Science ouverte0,0020,003
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,2600,300

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.

Tête enseignante Opus0,007
Tête enseignante GPT0,202
Écart entre enseignants0,195 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2019
Routes d'admission1
Résumé présentnon

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