Antibody Engineering & Therapeutics 2016: The Antibody Society's annual meeting, December 11–15, 2016, San Diego, CA
Bibliographic record
Abstract
Antibody Engineering & Therapeutics, the largest meeting devoted to antibody science and technology and the annual meeting of The Antibody Society, will be held in San Diego, CA on December 11-15, 2016. Each of 14 sessions will include six presentations by leading industry and academic experts. In this meeting preview, the session chairs discuss the relevance of their topics to current and future antibody therapeutics development. Session topics include bispecifics and designer polyclonal antibodies; antibodies for neurodegenerative diseases; the interface between passive and active immunotherapy; antibodies for non-cancer indications; novel antibody display, selection and screening technologies; novel checkpoint modulators / immuno-oncology; engineering antibodies for T-cell therapy; novel engineering strategies to enhance antibody functions; and the biological Impact of Fc receptor engagement. The meeting will open with keynote speakers Dennis R. Burton (The Scripps Research Institute), who will review progress toward a neutralizing antibody-based HIV vaccine; Olivera J. Finn, (University of Pittsburgh School of Medicine), who will discuss prophylactic cancer vaccines as a source of therapeutic antibodies; and Paul Richardson (Dana-Farber Cancer Institute), who will provide a clinical update on daratumumab for multiple myeloma. In a featured presentation, a representative of the World Health Organization's INN expert group will provide a perspective on antibody naming. "Antibodies to watch in 2017" and progress on The Antibody Society's 2016 initiatives will be presented during the Society's special session. In addition, two pre-conference workshops covering ways to accelerate antibody drugs to the clinic and the applications of next-generation sequencing in antibody discovery and engineering will be held on Sunday December 11, 2016.
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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 | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.119 | 0.116 |
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".