Highlights from the 5th Symposium on Biological Data Visualization: Part 1
Notice bibliographique
Résumé
High-throughput and high-resolution experimental methods in biology pose enormous challenges for current biological data visualization approaches. To address these challenges, researchers in the visualization and bioinformatics communities need to engage in the design, implementation, application, and evaluation of novel visualization techniques and tools that provide insight into large and highly complex data sets. BioVis 2015 - the fifth Symposium on Biological Data Visualization - brought together researchers from the visualization, bioinformatics, and biology communities to establish an interdisciplinary dialogue and promote the sharing of expertise between both meeting participants and the communities at large. The meeting educated, inspired, and engaged visualization researchers in problems in biological data visualization as well as bioinformatics and biology researchers in state-of-the-art visualization research. The symposium serves as a platform for researchers from these fields to increase the impact of data visualization approaches in biology. The BioVis 2015 symposium is affiliated with ISMB, the Intelligent Systems for Molecular Biology conference, as a Special Interest Group (SIG) and was colocated with ISMB in Dublin, Ireland, July 10-11 2015. Each paper was reviewed by researchers from both the bioinformatics and visualization fields and was evaluated for improvements over state-of-the-art and for scientific soundness. The review process was organized in two review cycles. In the first review cycle, each paper was reviewed by three to four reviewers. In the second review cycle, the primary reviewers checked whether the required revisions for conditionally accepted papers were successfully included. Based on the reviewers' scores, reviews, and recommendations, the BioVis 2015 Paper and Publication Chairs and the BMC Bioinformatics Section Editor together selected those that would be published as a BMC Bioinformatics supplement. The papers from BioVis 2015 appear in two different proceedings: As of the 5th Symposium on Biological Data Visualization: Part 1 in this BMC Bioinformatics supplement and as of the 5th Symposium on Biological Data Visualization: Part 2 in BMC Proceedings (http://www.biomedcentral.com/bmcproc/supplements/9/S6). From the 21 papers submitted to BioVis 2015, 9 papers are published in this BMC Bioinformatics supplement and 5 papers are published in BMC Proceedings. The articles in this supplement cover a wide spectrum of challenging problems in biological data visualization and their solutions. Overall, three main themes arise from the BioVis 2015 articles: omics, proteins, and imaging. In the omics field, Younesy et al. [1] describe VisRseq: a user-friendly interface for biologists to use libraries in R that provides a method for linking R-apps with interactive components. Chelaru et al. [2] expand on the design behind Epiviz, another tool for bringing genome visualization and computational environments together. Hennig et al. [3] describe Pan-Tetris and Aurisano et al. [4] describe BactoGeNIE: both systems are designed for comparing different genomes. The XCluSim tool by L'Yi et al. [5] has a more general application field and aims to provide insight into how different clustering results relate to each other. In the protein field, Stolte et al. [6] give an overview of the design decisions that underlie Aquaria, a visual analytics tool for exploring protein-related data. Finally, three papers are included from the imaging field. Topics range from image generation, as discussed by Abdellah et al. [7], to a method for parameter optimization in image processing by Pretorius et al. [9] (e.g. for cell nuclei detection and colour deconvolution for histology), and all the way to graph-based exploration of histology images in the GRAPHIE system proposed by Ding et al. [8]. The diversity of topics covered in this issue highlights the wide range of challenges in applying existing visualization techniques to biological data. With this analysis and formalization of our collective experiences, we hope to motivate visualization researchers to think about new problems and new approaches to pressing problems in biology.
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 | 0,001 | 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 ».