TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular\n Dynamics
Bibliographic record
Abstract
It is increasingly common to encounter data from dynamic processes captured\nby static cross-sectional measurements over time, particularly in biomedical\nsettings. Recent attempts to model individual trajectories from this data use\noptimal transport to create pairwise matchings between time points. However,\nthese methods cannot model continuous dynamics and non-linear paths that\nentities can take in these systems. To address this issue, we establish a link\nbetween continuous normalizing flows and dynamic optimal transport, that allows\nus to model the expected paths of points over time. Continuous normalizing\nflows are generally under constrained, as they are allowed to take an arbitrary\npath from the source to the target distribution. We present TrajectoryNet,\nwhich controls the continuous paths taken between distributions to produce\ndynamic optimal transport. We show how this is particularly applicable for\nstudying cellular dynamics in data from single-cell RNA sequencing (scRNA-seq)\ntechnologies, and that TrajectoryNet improves upon recently proposed static\noptimal transport-based models that can be used for interpolating cellular\ndistributions.\n
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".