# q-bio.CB

Published articles for q-bio.CB.

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## Thalamus-Reasoning: A Multimodal Large Language Model for Multi-Sample Omics Reasoning

DevFeed: [Thalamus-Reasoning: A Multimodal Large Language Model for Multi-Sample Omics Reasoning](<https://devfeed.tech/articles/thalamus-reasoning-a-multimodal-large-language-model-for-multi-sample-omics-reasoning-120021.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2605.06728>)

Author: Maciej Sypetkowski, {\\L}ukasz Smoli\\'nski, Remigiusz Kinas, Joanna Krawczyk, Bart{\\l}omiej Hofman, Aleksandra Mo\\.zwi{\\l}{\\l}o, Micha{\\l} Pikusa, Tomasz Jetka, Rafa{\\l} Powalski

Published: 2026-11-06T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [llm-reasoning](<https://devfeed.tech/topics/llm-reasoning.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [best](<https://devfeed.tech/tags/best.md>), [compare](<https://devfeed.tech/tags/compare.md>), [continuous](<https://devfeed.tech/tags/continuous.md>), [conversations](<https://devfeed.tech/tags/conversations.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [expression](<https://devfeed.tech/tags/expression.md>), [geo](<https://devfeed.tech/tags/geo.md>), [language-model](<https://devfeed.tech/tags/language-model.md>), [large-language-model](<https://devfeed.tech/tags/large-language-model.md>), [measurements](<https://devfeed.tech/tags/measurements.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [performance](<https://devfeed.tech/tags/performance.md>), [prediction](<https://devfeed.tech/tags/prediction.md>), [pretraining](<https://devfeed.tech/tags/pretraining.md>), [q-bio-cb](<https://devfeed.tech/tags/q-bio-cb.md>), [q-bio-gn](<https://devfeed.tech/tags/q-bio-gn.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [sample](<https://devfeed.tech/tags/sample.md>), [supervised-fine-tuning](<https://devfeed.tech/tags/supervised-fine-tuning.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

Thalamus-Reasoning is a 9B multimodal model that represents bulk or single-cell transcriptomes as compact continuous embeddings, which can be combined with natural-language instructions and multiple samples in one context. Its training uses 20 billion tokens of continued pretraining and 5.21 million supervised conversations across 157 task groups, including examples linking measurements to biological annotations and outcomes. On the publication-disjoint GEO-OmicsQA benchmark, it leads the evaluated models on all six variants; it also leads on three of four held-out annotation tasks and has the best average PerturbQA performance among compared methods. Ablations favor continuous omics representations over text-only gene lists. These results are reported for the evaluations described in the abstract.

### Source excerpt

arXiv:2605.06728v3 Announce Type: replace-cross Abstract: Large language models can reason over biological knowledge, but they are not naturally equipped to work with quantitative omics measurements. Existing approaches either convert expression profiles into long textual representations, often losing quantitative information, or connect omics encoders to language models for narrow tasks. We introduce Thalamus-Reasoning, a 9B multimodal language model that represents each bulk or single-cell transcriptome as a compact continuous embedding that can be interleaved with natural-language instructions and with other samples in the same context. A key contribution is a training corpus that connects experimental measurements with biological knowledge and task supervision. We combine templates linking transcriptomic profiles to annotations and experimental outcomes, reasoning examples connecting evidence across sources, and LLM-generated supervision with task-specific quality checks. The instruction-tuning corpus spans 157 biological and general-purpose task groups. Training includes continued pretraining on 20 billion tokens and supervised fine-tuning on 5.21 million conversations. We further introduce GEO-OmicsQA, a publication-disjoint benchmark of 3,000 questions requiring models to interpret and compare expression profiles from real GEO studies. Thalamus-Reasoning achieves the best results among the evaluated models on three of four held-out annotation tasks and on all six GEO-OmicsQA variants, and the best average performance among compared methods on PerturbQA perturbation-response prediction. Ablations show a clear advantage for continuous omics representations over a text-only gene-list representation. These results show that quantitative transcriptomic measurements and biological language can be integrated within a general conversational model, enabling both predictive tasks and language-guided interpretation of experimental samples.

## STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations

DevFeed: [STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations](<https://devfeed.tech/articles/strand-sequence-conditioned-transport-for-single-cell-perturbations-103741.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2602.10156>)

Author: Boyang Fu, Sameer Gabbita, George Dasoulas, Xiang Lin, Shanghua Gao, Xiaorui Su, Soumya Ghosh, Marinka Zitnik

Published: 2026-10-28T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [few-shot](<https://devfeed.tech/topics/few-shot.md>), [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [ablation](<https://devfeed.tech/tags/ablation.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [best](<https://devfeed.tech/tags/best.md>), [cells](<https://devfeed.tech/tags/cells.md>), [conditional](<https://devfeed.tech/tags/conditional.md>), [control](<https://devfeed.tech/tags/control.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [discrimination](<https://devfeed.tech/tags/discrimination.md>), [dna](<https://devfeed.tech/tags/dna.md>), [element](<https://devfeed.tech/tags/element.md>), [elements](<https://devfeed.tech/tags/elements.md>), [encoding](<https://devfeed.tech/tags/encoding.md>), [few-shot](<https://devfeed.tech/tags/few-shot.md>), [generative](<https://devfeed.tech/tags/generative.md>), [identifier](<https://devfeed.tech/tags/identifier.md>), [model](<https://devfeed.tech/tags/model.md>), [modeling](<https://devfeed.tech/tags/modeling.md>), [models](<https://devfeed.tech/tags/models.md>), [prediction](<https://devfeed.tech/tags/prediction.md>), [q-bio-cb](<https://devfeed.tech/tags/q-bio-cb.md>), [q-bio-gn](<https://devfeed.tech/tags/q-bio-gn.md>), [sequence](<https://devfeed.tech/tags/sequence.md>), [transport](<https://devfeed.tech/tags/transport.md>)

### AI overview

STRAND predicts cellular responses to genetic perturbations by encoding the DNA sequence at the targeted locus and conditioning a transport process from control to perturbed single-cell states. This allows locus-specific inputs and zero-shot predictions for targets absent from training, addressing the fact that different loci in one gene can produce distinct responses. In CRISPRi screens across K562, Jurkat, and RPE1 cells, it achieved the best average rank on few-shot and unseen-gene benchmarks, with up to 33% gains in perturbation discrimination and up to 0.12 improvement in ΔPearson when transferring to held-out cell lines. Ablations attributed the gains to sequence conditioning and conditional transport. The supplied text is an abstract and does not provide further evaluation details.

### Source excerpt

arXiv:2602.10156v2 Announce Type: replace-cross Abstract: Prediction of the transcriptional response of a cell to a genetic perturbation is a central problem in the computational modeling of gene regulation. Perturbations of the same gene can produce different transcriptional responses depending on the targeted locus, such as an alternative transcription start site or a distal regulatory element, and gene-level perturbation models represent these distinct interventions with a single gene identifier. We introduce STRAND, a generative model that encodes the regulatory DNA sequence at the perturbation locus and uses this encoding to parameterize a conditional transport process from control to perturbed single-cell states. Sequence conditioning enables zero-shot prediction for perturbation targets absent from training and supports locus-resolved inputs at 128 bp model resolution within a 524 kb sequence window. On CRISPRi screens in K562, Jurkat, and RPE1 cells, STRAND achieves the best average rank on few-shot and unseen-gene benchmarks, with gains of up to 33% in perturbation discrimination, and improves transfer to held-out cell lines by up to 0.12 in $\Delta$Pearson. Ablation studies attribute these gains to sequence conditioning and to conditional transport. At the locus level, STRAND distinguishes alternative transcription start sites and validated regulatory elements that gene-identifier models represent as a single perturbation. STRAND is available at https://github.com/mims-harvard/STRAND.