Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.260 | 0.300 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".