GeneSpeak-FP: Target and Compound Retrieval from Observed Cell-Level Perturbation Signatures

• URL: http://arxiv.org/abs/2607.17671v1
• Date: Mon, 20 Jul 2026 08:22:44 GMT
• Title: GeneSpeak-FP: Target and Compound Retrieval from Observed Cell-Level Perturbation Signatures
• Abstract summary: We present model, a Transformer retrieval model for a closed-library setting.<n>Each input is a cell-level perturbation signature formed by contrasting one treated cell with a cell-line-specific mean DMSO reference.<n>We evaluate on Tahoe-100M conditions with mapped target annotations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale single-cell perturbation atlases make it possible to ask an inverse question: given an observed transcriptional response, which annotated targets and compounds in a fixed library are most consistent with that response? We present \model, a Transformer retrieval model for this closed-library setting. Each input is a cell-level perturbation signature formed by contrasting one treated cell with a cell-line-specific mean DMSO reference. The encoder maps the signature to a target-retrieval vector and a molecular-embedding vector, trained jointly with supervised target losses and structure--transcriptome alignment. We evaluate on Tahoe-100M conditions with mapped target annotations using a within-compound stratified 90/10 condition-pair split of 10,505 training and 1,168 validation drug--cell-line pairs. Because compounds and cell lines can occur in both partitions, the experiment measures held-out condition-pair retrieval rather than generalization to unseen compounds or cellular contexts. In a Monte Carlo evaluation over 38,400 sampled validation cells, \model\ achieved target Recall@10 of 0.408 and Recall@20 of 0.544, together with compound Hit@1 of 0.129, Hit@10 of 0.343, and mean reciprocal rank of 0.205 over a 379-compound bank. A separate diagnostic evaluation produced nearly identical values for the main model and large gains over a random-vector control and post-hoc bag-of-genes controls. These results demonstrate that a single multi-task model can recover both mapped target annotations and recorded compound identities from observed cell-level responses in the evaluated Tahoe-100M closed-library setting. Generalization to unseen compounds and cellular contexts remains to be established.

Related papers

• SAM3-LoRA: Parameter-Efficient Adaptation of a Concept-Promptable Foundation Model for Multi-Class Structural Defect Segmentation [0.0]
  This study applies Low-Rank Adaptation (LoRA) to SAM3 for multi-class structural defect segmentation.<n>We describe a supervision procedure that trains a concept-promptable model directly from COCO-style class-labeled instance segmentation.
  arXiv  Detail & Related papers  (2026-08-31T23:19:59Z)
• Analysis of Federated Aggregation under Model Poisoning and Backdoor Attacks: A Reconstructed Cross-Dataset and Cross-Architecture Benchmark [0.0]
  A 500-cell seed-1 evaluation matrix was reconstructed across five aggregation methods.<n>Execution logs were identified for 454 original runs and 36 repaired or rerun executions.<n>Krum attained the highest recorded accuracy under both sign-flipping and Gaussian configurations.
  arXiv  Detail & Related papers  (2026-08-11T20:42:56Z)
• Score Distributions, Not Cells: Evaluating Single-Cell Perturbations Under Class Overlap [0.09558392439655013]
  Two perturbations can produce different populations of cells while overlapping so much that an individual cell could belong to either.<n>Per-cell accuracy measures this overlap rather than model quality.<n>The fix is to score perturbations across the whole population rather than cell by cell.
  arXiv  Detail & Related papers  (2026-07-06T01:47:02Z)
• Modeling Cell-Cycle-Aware Single-Cell Drug Perturbation Responses [7.806328314748778]
  scCycleMol is a cell-cycle-aware perturbation prediction framework built on a curated 24-hour SciPlex3 benchmark.<n> scCycleMol derives cell-cycle supervision from the treated state and applies it to predicted treated expression.
  arXiv  Detail & Related papers  (2026-06-29T01:57:40Z)
• PerturbCellRL: Verifier-Guided Reinforcement Learning for Single-Cell Perturbation Prediction [28.013973806760976]
  Single-cell perturbation models can reduce costly wet-lab screening by predicting how cells respond to interventions.<n>We introduce PerturbCellRL, a reinforcement learning framework that post-trains a pretrained single-cell transcriptomic generator.<n>We evaluate PerturbCellRL on multiple genetic and chemical perturbation benchmarks.
  arXiv  Detail & Related papers  (2026-06-26T06:15:04Z)
• FailureScope: Cross-Regime Behavioral Diagnosis of Language Model Weaknesses [0.0]
  FailureScope is a behavioral-diagnosis method that clusters evaluation probes by their cross-model pass/fail patterns.<n>We show it yields stable, interpretable failure across three regimes usually studied separately: single-turn benchmarks, multi-turn dialogue, and adversarial agent attacks.
  arXiv  Detail & Related papers  (2026-06-03T01:28:00Z)
• Chem-PerturBridge: a harmonized compendium of small molecule perturbation transcriptomic effects [4.860649440823431]
  Chem-PerturBridge is a harmonized multi-dataset resource comprising over 37k compounds, 136 cellular contexts, and 1.25M transcriptomic samples.<n>We use the resource to evaluate matched-condition agreement across datasets and replicate agreement within datasets.
  arXiv  Detail & Related papers  (2026-05-29T16:38:30Z)
• CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment [46.660621971318996]
  Inferring dynamics from population snapshots is a fundamental challenge in machine learning and biology.<n>We introduce CellBRIDGE, which augments feature-based OT with a directed, typed interaction cost derived from ligand-receptor activity.<n>By explicitly modeling cell-cell communication, CellBRIDGE improves cross-snapshot couplings and downstream trajectory estimates.
  arXiv  Detail & Related papers  (2026-05-28T22:37:54Z)
• Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback [56.69850045068714]
  We introduce textitInfluence-Guided Regression (IGSR), a method that frames equation discovery as an iterative two-step process.<n>We demonstrate IGSRs effectiveness on a diverse suite of benchmarks, including LLM-SRBench, pharmacological PKPD models, an epidemiological simulation, and real-world genomic data.
  arXiv  Detail & Related papers  (2026-05-27T23:48:01Z)
• MAT-Cell: A Multi-Agent Tree-Structured Reasoning Framework for Batch-Level Single-Cell Annotation [81.82645086402366]
  We propose MAT-Cell, a neuro-symbolic reasoning framework that reframes single-cell analysis into constructive, verifiable proof generation.<n> MAT-Cell injects symbolic constraints through adaptive Retrieval-Augmented Generation (RAG) to ground neural reasoning in biological axioms and reduce transcriptomic noise.<n>It significantly outperforms state-of-the-art (SOTA) models and maintains robust per-formance in challenging scenarios.
  arXiv  Detail & Related papers  (2026-04-07T04:56:06Z)
• Lingshu-Cell: A generative cellular world model for transcriptome modeling toward virtual cells [48.885716676667776]
  Lingshu-Cell is a discrete diffusion model that learns transcriptomic state distributions and supports conditional simulation under perturbations.<n>It captures complex transcriptome-wide expression dependencies across approximately 18,000 genes without relying on prior gene selection.<n>It achieves leading performance on the Virtual Cell Challenge H1 genetic perturbation benchmark and in predicting cytokine-induced responses in human PBMCs.
  arXiv  Detail & Related papers  (2026-03-26T09:46:27Z)
• SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction [46.645872209565574]
  We present a specialized large-scale foundation model for virtual cell perturbation prediction.<n>First, we build a BioNeMo-based training and inference framework that substantially improves data throughput.<n>Second, we formulate perturbation prediction as conditional transport and implement it with a set-aware flow architecture.<n>Third, we evaluate the model on Tahoe-100M using a rigorous cell-level protocol centered on biologically meaningful metrics.
  arXiv  Detail & Related papers  (2026-03-18T05:51:08Z)
• Unlasting: Unpaired Single-Cell Multi-Perturbation Estimation by Dual Conditional Diffusion Implicit Bridges [68.98973318553983]
  We propose a framework based on Dual Diffusion Implicit Bridges (DDIB) to learn the mapping between different data distributions.<n>We integrate gene regulatory network (GRN) information to propagate perturbation signals in a biologically meaningful way.<n>We also incorporate a masking mechanism to predict silent genes, improving the quality of generated profiles.
  arXiv  Detail & Related papers  (2025-06-26T09:05:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.