Antibody Engineering & Therapeutics 2015: The Antibody Society's annual meeting December 7–10, 2015, San Diego, CA
Bibliographic record
Abstract
Antibody Engineering & Therapeutics, the annual meeting of The Antibody Society, will be held in San Diego, CA in early December 2015. In this meeting preview, the chairs provide their thoughts on the importance of their session topics, which include antibody effector functions, reproducibility of research and diagnostic antibodies, new developments in antibody-drug conjugates (ADCs), preclinical and clinical ADC data, new technologies and applications for bispecific antibodies, antibody therapeutics for non-cancer and orphan indications, antibodies to harness the cellular immune system, overcoming resistance to clinical immunotherapy, and building comprehensive IGVH-gene repertoires through discovering, confirming and cataloging new germline IGVH genes. The Antibody Society's special session will focus on "Antibodies to watch" in 2016, which are a subset of the nearly 50 antibodies currently in Phase 3 clinical studies. Featuring over 100 speakers in total, the meeting will commence with keynote presentations by Erica Ollmann Saphire (The Scripps Research Institute), Wayne A. Marasco (Dana-Farber Cancer Institute/Harvard Medical School), Joe W. Gray (Oregon Health & Science University), and Anna M. Wu (University of California Los Angeles), and it will conclude with workshops on the promise and challenges of using next-generation sequencing for antibody discovery and engineering from synthetic and in vivo libraries and on computational antibody design.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.095 | 0.070 |
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".