论文检索

Applications · Genetics, Cell Biology, Health, etc

Zelin Liu, Dongdong Chen, Yusong Sun, Yuqi Hu, Huang Jie, Sicheng Dong, Xu Han, Hongmei Yi, Qiyuan Bao, Lichi Zhang

Segmentation in pathology image requires handling out-of-domain tissue morphologies and new pathologies beyond training distributions, where traditional closed-set segmentation approaches fail to generalize. Reasoning segmentation enables zero-shot generalization via prompting with text queries. However, existing reasoning segmentation models face three barriers when applied to pathology: (1) the vision encoder lack pathology-specific knowledge and robustness to staining variations, (2) the large language model (LLM) backbone for reasoning fails to identify whether it has gathered sufficient semantic context to trigger the segmentation output, and (3) no reasoning segmentation benchmarks and datasets exist for pathology analysis. Consequently, we introduce PathChat-SegR1, a reasoning segmentation model built upon pathology-specific vision encoders trained with a novel stain-invariant self-distillation for robust pathology image representations. Moreover, we propose Segmentation-Optimized GRPO (SO-GRPO), a reinforcement learning method specifically for reasoning segmentation that learns to determine optimal segmentation timing based on accumulated reasoning context. Finally, we construct a pathology-specific reasoning segmentation benchmark of 118,667 triplets of pathology image, ground-truth mask, query, and reasoning chain including both public and private pathology images. Zero-shot evaluation on pathology images with out-of-domain morphologies/pathologies shows 61\% improvement over state-of-the-art segmentation models.

Applications · Genetics, Cell Biology, Health, etc

Tianwei Lin, Zhongwei Qiu, Wenqiao Zhang, Jiang Liu, Yihan Xie, Mingjian Gao, Zhenxuan Fan, Zhaocheng Li, Sijing Li, Zhongle Xie 等

Computed Tomography (CT) is one of the most widely used and diagnostically information-dense imaging modalities, covering critical organs such as the heart, lungs, liver, and colon. Clinical interpretation relies on both slice-driven local features (e.g., sub-centimeter nodules, lesion boundaries) and volume-driven spatial representations (e.g., tumor infiltration, inter-organ anatomical relations). However, existing Large Vision–Language Models (LVLMs) remain fragmented in CT slice versus volumetric understanding: slice-driven LVLMs show strong generalization but lack cross-slice spatial consistency, while volume-driven LVLMs explicitly capture volumetric semantics but suffer from coarse granularity and poor compatibility with slice inputs. The absence of a unified modeling paradigm constitutes a major bottleneck for the clinical translation of medical LVLMs. We present OmniCT, a powerful unified slice–volume LVLM for CT scenarios, which makes three contributions: (i) Spatial Consistency Enhancement (SCE): volumetric slice composition combined with tri-axial positional embedding that introduces volumetric consistency, and an MoE hybrid projection enables efficient slice–volume adaptation; (ii) Organ-level Semantic Enhancement (OSE): segmentation and ROI localization explicitly align anatomical regions, emphasizing lesion- and organ-level semantics; (iii) MedEval-CT: the largest slice–volume CT dataset and hybrid benchmark integrates comprehensive metrics for unified evaluation. OmniCT consistently outperforms existing methods with a substantial margin across diverse clinical tasks and satisfies both micro-level detail sensitivity and macro-level spatial reasoning. More importantly, it establishes a new paradigm for cross-modal medical imaging understanding. Our project is available at https://github.com/ZJU4HealthCare/OmniCT.

Applications · Genetics, Cell Biology, Health, etc

Nic Fishman, Gokul Gowri, Tanush Kumar, Jiaqi Lu, Valentin De Bortoli, Jonathan Gootenberg, Omar Abudayyeh

Many modern biological assays, including RNA sequencing, yield integer-valued counts that reflect the number of molecules detected. These measurements are often not at the desired resolution: while the unit of interest is typically a single cell, many measurement technologies produce counts aggregated over sets of cells. Although recent generative frameworks such as diffusion and flow matching have been extended to non-Euclidean and discrete settings, it remains unclear how best to model integer-valued data or how to systematically deconvolve aggregated observations. We introduce Count Bridges, a stochastic bridge process on the integers that provides an exact, tractable analogue of diffusion-style models for count data, with closed-form conditionals for efficient training and sampling. We extend this framework to enable direct training from aggregated measurements via an Expectation-Maximization approach that treats unit-level counts as latent variables. We demonstrate state-of-the-art performance on integer distribution matching benchmarks, comparing against flow matching and discrete flow matching baselines across various metrics. We then apply Count Bridges to two large-scale problems in biology: modeling single-cell gene expression data at the nucleotide resolution, with applications to deconvolving bulk RNA-seq, and resolving multicellular spatial transcriptomic spots into single-cell count profiles. Our methods offer a principled foundation for generative modeling and deconvolution of biological count data across scales and modalities.

Applications · Genetics, Cell Biology, Health, etc

Jiahao Zhao, Feng Jiang, Shaowei Qin, Zhonghui Zhang, Junhao Liu, Guibing Guo, Hamid Alinejad-Rokny, Min Yang

Large language models (LLMs) are increasingly applied in scientific research, offering new capabilities for knowledge discovery and reasoning. In single-cell biology, however, evaluation practices for both general and specialized LLMs remain inadequate: existing benchmarks are fragmented across tasks, adopt formats such as multiple-choice classification that diverge from real-world usage, and rely on metrics lacking interpretability and biological grounding. We present **SC-ARENA**, a natural language evaluation framework tailored to single-cell foundation models. SC-ARENA formalizes a *virtual cell* abstraction that unifies evaluation targets by representing both intrinsic attributes and gene-level interactions. Within this paradigm, we define five natural language tasks — cell type annotation, captioning, generation, perturbation prediction, and scientific QA — that probe core reasoning capabilities in cellular biology. To overcome the limitations of brittle string-matching metrics, we introduce **knowledge-augmented evaluation**, which incorporates external ontologies, marker databases, and scientific literature to support biologically faithful and interpretable judgments. Experiments and analysis across both general-purpose and domain-specialized LLMs demonstrate that: (i) under the *Virtual Cell* unified evaluation paradigm, current models achieve uneven performance on biologically complex tasks, particularly those demanding mechanistic or causal understanding; and (ii) our knowledge-augmented evaluation framework ensures biological correctness, provides interpretable, evidence-grounded rationales, and achieves high discriminative capacity, overcoming the brittleness and opacity of conventional metrics. **SC-ARENA** thus provides a unified and interpretable framework for assessing LLMs in single-cell biology, pointing toward the development of biology-aligned, generalizable foundation models.

Applications · Genetics, Cell Biology, Health, etc

Weining Fu, Kai Shu, Kui Xu, Qiangfeng Zhang

Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling near-atomic-level visualization of biomolecular assemblies. However, the exponential growth in cryo-EM data throughput and complexity, coupled with diverse downstream analytical tasks, necessitates unified computational frameworks that transcend current task-specific deep learning approaches with limited scalability and generalizability. We present CryoLVM, a foundation model that learns rich structural representations from experimental density maps with resolved structures by leveraging the Joint-Embedding Predictive Architecture (JEPA) integrated with a SCUNet-based backbone, enabling rapid adaptation to various downstream tasks. We further introduce a novel histogram-based distribution alignment loss that accelerates convergence and enhances fine-tuning performance. Through comprehensive evaluation across three critical cryo-EM tasks—density map sharpening, density map super-resolution, and missing wedge restoration—CryoLVM consistently outperforms state-of-the-art baselines across multiple density map quality metrics, confirming its potential as a versatile foundation model for a wide spectrum of cryo-EM applications.

Applications · Genetics, Cell Biology, Health, etc

Chang Yu, Siyuan Li, Zicheng Liu, Jingbo Zhou, Xianglong Guo, Kai Yu, Yuqing Zhou, Ken Li, Zelin Zang, Zhen Lei 等

Linking genomic DNA to quantitative, context-specific expression remains a central challenge in computational biology. Current foundation models capture either tissue context or sequence features, but not both. Cross-omics systems, in turn, often overlook critical mechanisms such as alternative splicing and isoform reuse. We present CDBridge, a post-training strategy that unifies pretrained DNA and protein models into a context-aware framework without full retraining. CDBridge operates in two stages: (a) Seq-context learning, where a splicing-inspired token merge compresses long genomic regions into isoform-aware representations, and (b) Env-context learning, where a conditional decoder injects tissue embeddings to model expression under diverse biological contexts. To benchmark this setting, we introduce GTEx-Benchmark, derived from GTEx and Ensembl, which requires models to capture long-range exon dependencies, resolve isoform reuse, and predict tissue-specific expression levels. Across qualitative and quantitative tasks, CDBridge consistently outperforms prior methods that ignore central dogma constraints or context dependence, offering a scalable and biologically faithful solution for DNA-to-expression modeling.

Applications · Genetics, Cell Biology, Health, etc

Romain Seailles, Jean Masson, Jean Ponce, Julien Mairal

Single‑molecule localization microscopy (SMLM) allows reconstructing cellular organelles and biology-relevant structures far beyond the limited spatial resolution imposed by optics constrains, using tagged biomolecule positions. Currently, efficient SMLM requires non‑overlapping emitting fluorophores, to ensure proper image deconvolution leading to long acquisition times that hinders live‑cell imaging. Recent deep‑learning approaches can handle denser emissions, but they rely on variants of non‑maximum suppression (NMS) layers, which are unfortunately non‑differentiable and may discard true positives with their local fusion strategy. In this presentation, we reformulate the SMLM training objective as a set‑matching problem, deriving an optimal‑transport loss that eliminates the need for NMS during inference and enables end‑to‑end training. Additionally, we propose an iterative neural network that integrates knowledge of the microscope’s optical system inside our model. Experiments on synthetic benchmarks and real biological data show that both our new loss function and architecture surpass the state of the art at moderate and high emitter densities. Code is available at https://github.com/RSLLES/SHOT.

Applications · Genetics, Cell Biology, Health, etc

Linshan Wu, Jia-Xin Zhuang, Hao CHEN

Pan-cancer screening in large-scale CT scans remains challenging for existing AI methods, primarily due to the difficulty of localizing diverse types of tiny lesions in large CT volumes. The extreme foreground-background imbalance significantly hinders models from focusing on diseased regions, while redundant focus on healthy regions not only decreases the efficiency but also increases false positives. Inspired by radiologists' glance and focus diagnostic strategy, we introduce GF-Screen, a Glance and Focus reinforcement learning framework for pan-cancer screening. GF-Screen employs a Glance model to localize the diseased regions and a Focus model to precisely segment the lesions, where segmentation results of the Focus model are leveraged to reward the Glance model via Reinforcement Learning (RL). Specifically, the Glance model crops a group of sub-volumes from the entire CT volume and learns to select the sub-volumes with lesions for the Focus model to segment. Given that the selecting operation is non-differentiable for segmentation training, we propose to employ the segmentation results to reward the Glance model. To optimize the Glance model, we introduce a novel group relative learning paradigm, which employs group relative comparison to prioritize high-advantage predictions and discard low-advantage predictions within sub-volume groups, not only improving efficiency but also reducing false positives. In this way, for the first time, we effectively extend cutting-edge RL techniques to tackle the specific challenges in pan-cancer screening. We conduct training and validation on a large-scale pan-cancer dataset comprising 5,117 CT scans. Extensive experiments on 16 internal and 7 external datasets across 9 lesion types demonstrated the effectiveness of GF-Screen. Notably, GF-Screen leads the public validation leaderboard of MICCAI FLARE25 pan-cancer challenge, surpassing the FLARE24 champion solution by a large margin (+25.6% DSC and +28.2% NSD). In addition, through discarding redundant regions, GF-Screen reduces the computation costs by 5.7 times, significantly improving inference efficiency. The superior performance of GF-Screen remarks a novel and practical breakthrough in pan-cancer screening. Code is available at https://github.com/Luffy03/GF-Screen.

Applications · Genetics, Cell Biology, Health, etc

Alistair Turcan, Kexin Huang, Lei Li, Martin J. Zhang

Scientific discovery is often slowed by the manual development of computational tools needed to analyze complex experimental data. Building such tools is costly and time-consuming because scientists must iteratively review literature, test mod- eling and scientific assumptions against empirical data, and implement these in- sights into efficient software. Large language models (LLMs) have demonstrated strong capabilities in synthesizing literature, reasoning with empirical data, and generating domain-specific code, offering new opportunities to accelerate com- putational method development. Existing LLM-based systems either focus on performing scientific analyses using existing computational methods or on de- veloping computational methods or models for general machine learning without effectively integrating the often unstructured knowledge specific to scientific do- mains. Here, we introduce TusoAI, an agentic AI system that takes a scientific task description with an evaluation function and autonomously develops and optimizes computational methods for the application. TusoAI integrates domain knowledge into a knowledge tree representation and performs iterative, domain-specific op- timization and model diagnosis, improving performance over a pool of candidate solutions. We conducted comprehensive benchmark evaluations demonstrating that TusoAI outperforms state-of-the-art expert methods, MLE agents, and scien- tific AI agents across diverse tasks. Applying TusoAI to two key open problems in genetics improved existing computational methods and uncovered new biology missed by previous methods. Our code is publicly available at https://github.com/Alistair-Turcan/TusoAI.

Applications · Genetics, Cell Biology, Health, etc

KUN WU, Zhiguo Jiang, Xinyu Zhu, Jun Shi, Yushan Zheng

Fusing histopathology images and genomics data with deep learning has significantly advanced precision oncology. However, genomics data is often missing due to its high acquisition cost and complexity in real-world clinical scenarios. Existing solutions aim to reconstruct genomics data from histopathology images. Nevertheless, these methods typically relied only on individual case and overlooked the potential relationships among cases. Additionally, they failed to take advantage of the authentic genomics data of diagnostically related cases that are accessible from training for inference. In this work, we propose a novel Multi-modal Structural Representation Learning (MSRL) framework for data-efficient precision oncology. We pre-train a histopathology-genomics multi-modal representation graph adopting Graph Structure Learning (GSL) to construct inter-case relevance based on the data inherently. During the fine-tuning stage, we dynamically capture structural relevance between the training cases and the acquired authentic cases for precise prediction. MSRL leverages prior inter-case associations and authentic genomics data from diagnosed cases based on the graph, which contributes to effective inference based on the single histopathology image modality. We evaluated MSRL on public TCGA datasets with 7,263 cases across various tasks, including survival prediction, cancer grading, and gene mutation prediction. The results demonstrate that MSRL significantly outperforms existing missing-genomics generation approaches with improvements of 1.44% to 3.12% in C-Index on survival prediction tasks and achieves comparable performance to multi-modal fusion methods. The code and data are available at https://github.com/WkEEn/MSRL.

Applications · Genetics, Cell Biology, Health, etc

Yuxin Zhang, Yiyao Li, Ping Ho, Simon See, Zhenqin Wu, Kevin Tsia

Cell Painting combines multiplexed fluorescent staining, high‑content imaging, and quantitative analysis to generate high-dimensional phenotypic readouts to support diverse downstream tasks such as mechanism-of-action (MoA) inference, toxicity prediction, and construction of drug–disease atlases. However, existing workflows are slow, costly and difficult to interpret. Approaches for drug screening modeling predominantly focus on molecular representation learning, while neglecting actual experimental context (e.g., cell line, dosing schedule, etc.), limiting generalization and MoA resolution. We introduce CP-Agent, an agentic multimodal large language model (MLLM) capable of generating mechanism-relevant, human-interpretable rationales for cell morphological changes under drug perturbations. At its core, CP-Agent leverages a context-aware alignment module, CP-CLIP, that jointly embeds high-content images and experimental metadata to enable robust treatment and MoA discrimination (achieving a maximum F1-score of 0.896). By integrating CP-CLIP outputs with agentic tool usage and reasoning, CP‑Agent compiles rationales into a structured report to guide experimental design and hypothesis refinement. These capabilities highlight CP-Agent’s potential to accelerate drug discovery by enabling more interpretable, scalable, and context-aware phenotypic screening---streamlining iterative cycles of hypothesis generation in drug discovery.

Applications · Genetics, Cell Biology, Health, etc

ZAIXI ZHANG, Zhenghong Zhou, Ruofan Jin, Le Cong, Mengdi Wang

DNA, encoding genetic instructions for almost all living organisms, fuels groundbreaking advances in genomics and synthetic biology. Recently, DNA Language Models have achieved success in designing synthetic functional DNA sequences, even whole genomes of novel bacteriophage, verified with wet lab experiments. Such remarkable generative power also brings severe biosafety concerns about whether DNA language models can design human viruses. With the goal of exposing vulnerabilities and informing the development of robust safeguarding techniques, we perform a systematic biosafety evaluation of DNA language models through the lens of jailbreak attacks. Specifically, we introduce JailbreakDNABench, a benchmark centered on high-priority human viruses, together with an end-to-end jailbreak framework, GeneBreaker. GeneBreaker integrates three key components: (1) an LLM agent equipped with customized bioinformatics tools to design high-homology yet non-pathogenic jailbreak prompts, (2) beam search guided by PathoLM and log-probability heuristics to steer sequence generation toward pathogen-like outputs, and (3) a BLAST- and function-annotation–based evaluation pipeline to identify successful jailbreaks. On JailbreakDNABench, GeneBreaker successfully jailbreaks the latest Evo series models across 6 viral categories consistently (up to 60\% Attack Success Rate for Evo2-40B). Further case studies on SARS-CoV-2 spike protein and HIV-1 envelope protein demonstrate the sequence and structural fidelity of jailbreak output, while evolutionary modeling of SARS-CoV-2 underscores biosecurity risks. Our findings also reveal that scaling DNA language models amplifies dual-use risks, motivating enhanced safety alignment and tracing mechanisms.

Applications · Genetics, Cell Biology, Health, etc

Tianmeng Hu, Yongzheng Cui, Biao Luo, Ke Li

The inverse design of RNA three-dimensional (3D) structures is crucial for engineering functional RNAs in synthetic biology and therapeutics. While recent deep learning approaches have advanced this field, they are typically optimized and evaluated using native sequence recovery, which is a limited surrogate for structural fidelity, since different sequences can fold into similar 3D structures and high recovery does not necessarily indicate correct folding. To address this limitation, we propose RIDER, an RNA Inverse DEsign framework with Reinforcement learning that directly optimizes for 3D structural similarity. First, we develop and pre-train a GNN-based generative diffusion model conditioned on the target 3D structure, achieving a $9\\%$ improvement in native sequence recovery over state-of-the-art methods. Then, we fine-tune the model with an improved policy gradient algorithm using four task-specific reward functions based on 3D self-consistency metrics. Experimental results show that RIDER improves structural similarity by over $100\\%$ across all metrics and discovers designs that are distinct from native sequences.

Applications · Genetics, Cell Biology, Health, etc

Jiahao Zhang, ZEQING ZHANG, Di Wang, Lijie Hu

Protein language models (PLMs) have enabled advances in structure prediction and de novo protein design, yet they frequently collapse into pathological repetition during generation. Unlike in text, where repetition merely reduces readability, in proteins it undermines structural confidence and functional viability. To unify this problem, we present the first systematic study of repetition in PLMs. We first propose quantitative metrics to characterize motif-level and homopolymer repetition and then demonstrate their negative impact on folding reliability. To address this challenge, we propose UCCS (Utility-Controlled Contrastive Steering), which steers protein generation with a constrained dataset. Instead of naively contrasting high- vs. low-repetition sequences, we construct contrastive sets that maximize differences in repetition while tightly controlling for structural utility. This disentanglement yields steering vectors that specifically target repetition without degrading foldability. Injected at inference, these vectors consistently reduce repetition without retraining or heuristic decoding. Experiments with ESM-3 and ProtGPT2 in CATH, UniRef50, and SCOP show that our method outperforms decoding penalties and other baselines, substantially lowering repetition while preserving AlphaFold confidence scores. Our results establish repetition control as a central challenge for PLMs and highlight dataset-guided steering as a principled approach for reliable protein generation.

Applications · Genetics, Cell Biology, Health, etc

Daniel Shao, Joel Runevic, Richard Chen, Drew Williamson, Ahrong Kim, Andrew Song, Faisal Mahmood

Multiple Instance Learning (MIL) is the predominant framework for classifying gigapixel whole-slide images in computational pathology. MIL follows a sequence of 1) extracting patch features, 2) applying a linear layer to obtain task-specific patch features, and 3) aggregating the patches into a slide feature for classification. While substantial efforts have been devoted to optimizing patch feature extraction and aggregation, none have yet addressed the second point, the critical layer which transforms general-purpose features into task-specific features. We hypothesize that this layer constitutes an overlooked performance bottleneck and that stronger representations can be achieved with a low-rank transformation tailored to each patch's phenotype, yielding synergistic effects with any of the existing MIL approaches. To this end, we introduce MAMMOTH, a parameter-efficient, multi-head mixture of experts module designed to improve the performance of any MIL model with minimal alterations to the total number of parameters. Across eight MIL methods and 19 different classification tasks, we find that such task-specific transformation has a larger effect on performance than the choice of aggregation method. For instance, when equipped with MAMMOTH, even simple methods such as max or mean pooling attain higher average performance than any method with the standard linear layer. Overall, MAMMOTH improves performance in 130 of the 152 examined configurations, with an average $+3.8\%$ change in performance.

Applications · Genetics, Cell Biology, Health, etc

YUE PEI, Xuebin Chi, Yu KANG

Language models have advanced sequence analysis, yet DNA foundation models often lag behind task-specific methods for unclear reasons. We present AntigenLM, a generative DNA language model pretrained on influenza genomes with intact, aligned functional units. This structure-aware pretraining enables AntigenLM to capture evolutionary constraints and generalize across tasks. Fine-tuned on time-series hemagglutinin (HA) and neuraminidase (NA) sequences, AntigenLM accurately forecasts future antigenic variants across regions and subtypes, including those unseen during training, outperforming phylogenetic and evolution-based models. It also achieves near-perfect subtype classification. Ablation studies show that disrupting genomic structure through fragmentation or shuffling severely degrades performance, revealing the importance of preserving functional-unit integrity in DNA language modeling. AntigenLM thus provides both a powerful framework for antigen evolution prediction and a general principle for building biologically grounded DNA foundation models.

Applications · Genetics, Cell Biology, Health, etc

Haoran Sun, Yankai Jiang, Zhenyu Tang, Yaning Pan, Shuang Gu, Zekai Lin, Lilong Wang, Wenjie Lou, Lei Liu, LEI BAI 等

The foundation of reproducible science lies in protocols that are precise, logically ordered, and executable. The autonomous generation of these protocols through natural language queries could greatly improve the efficiency of the reproduction process. However, current leading large language models (LLMs) often generate incomplete or inconsistent protocols, limiting their utility. To address this limitation, we first introduce SciRecipe, a large-scale dataset of over 12K structured protocols spanning 27 biological subfields and encompassing both comprehension and problem-solving tasks. To further improve protocol generation, we propose the "Sketch-and-Fill" paradigm, which separates analysis, structuring, and expression to ensure each step is explicit and verifiable. Complementing this, the structured component-based reward mechanism evaluates step granularity, action order, and semantic fidelity, aligning model optimization with experimental reliability. Building on these components, we develop Thoth, trained through a staged Knowledge-to-Action process that progresses from knowledge acquisition to operational reasoning and ultimately to robust, executable protocol generation. Across multiple benchmarks, Thoth consistently surpasses both proprietary and open-source LLMs, achieving significant improvements in step alignment, logical sequencing, and semantic accuracy. Our approach paves the way for reliable scientific assistants that bridge knowledge with experimental execution.

Applications · Genetics, Cell Biology, Health, etc

Chenglei Yu, Chuanrui Wang, Bangyan Liao, Tailin Wu

A central goal in systems biology and drug discovery is to predict the transcriptional response of cells to perturbations. This task is challenging due to the noisy, sparse nature of single-cell measurements and the fact that perturbations often induce population-level shifts rather than changes in individual cells. Existing deep learning methods typically assume cell-level correspondences, limiting their ability to capture such global effects. We present **scDFM**, a generative framework based on conditional flow matching that models the full distribution of perturbed cells conditioned on control states. By incorporating an MMD objective, our method aligns perturbed and control populations beyond cell-level correspondences. To further improve robustness to sparsity and noise, we propose the Perturbation-Aware Differential Transformer architecture (PAD-Transformer), a backbone that leverages gene interaction graphs and differential attention to capture context-specific expression changes. **scDFM** outperforms prior methods across multiple genetic and drug perturbation benchmarks, excelling in both unseen and combinatorial settings. In the combinatorial setting, it reduces MSE by 19.6\% over the strongest baseline. These results highlight the importance of distribution-level generative modeling for robust $\textit{in silico}$ perturbation prediction. The code is available at https://github.com/AI4Science-WestlakeU/scDFM.

Applications · Genetics, Cell Biology, Health, etc

Hiren Madhu, João Felipe Rocha, Tinglin Huang, Siddharth Viswanath, Smita Krishnaswamy, Rex Ying

Single-cell transcriptomics and proteomics have become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellular heterogeneity and gene expression at the single-cell level. With the advent of spatial-omics data, we have the promise of characterizing cells within their tissue context as it provides both spatial coordinates and intra-cellular transcriptional or protein counts. Beyond transcriptomics, proteomics offers a complementary view by directly measuring proteins, which are the primary effectors of cellular function and key therapeutic targets. However, existing models either ignore the spatial information or the complex genetic and proteomic programs within cells. Thus they cannot infer how cell internal regulation adapts to microenvironmental cues. Furthermore, these models often utilize fixed gene vocabularies, hindering their generalizability to datasets with different genes than pretraining. In this paper, we introduce HEIST, a hierarchical graph transformer foundation model for spatial transcriptomics and proteomics. HEIST models tissues as hierarchical graphs. The higher level graph is a spatial cell graph, and each cell in turn, is represented by its lower level gene co-expression network graph. Rather than using a fixed gene vocabulary, HEIST computes gene embeddings from its co-expression network and cellular context. HEIST achieves this by performing both intra-level and cross-level message passing to utilize the hierarchy in its embeddings and can thus generalize to novel datatypes including spatial proteomics without retraining. HEIST is pretrained on 22.3M cells from 124 tissues across 15 organs using spatially-aware contrastive and masked autoencoding objectives. Unsupervised analysis of HEIST embeddings reveals spatially informed subpopulations missed by prior models. Downstream evaluations demonstrate generalizability to proteomics data and state-of-the-art performance in clinical outcome prediction, cell type annotation, and gene imputation across multiple technologies.

Applications · Genetics, Cell Biology, Health, etc

Kirill Vishniakov, Karthik Viswanathan, Aleksandr Medvedev, Praveenkumar Kanithi, Marco Pimentel, Ronnie Rajan, Shadab Khan

The success of Large Language Models has inspired the development of Genomic Foundation Models (GFMs) through similar pretraining techniques. However, the relationship between pretraining performance and effectiveness in downstream genomic tasks remains unclear. Additionally, the high computational cost of pretraining raises questions about its cost-efficiency. To assess the usefulness of pretraining in genomics, we evaluated seven different GFMs across 52 diverse genomic tasks, comparing them to their counterparts with randomly initialized weights. Across benchmarks, we find that randomly initialized models provide surprisingly strong baselines and tokenizer and architecture choices strongly shape both these baselines and the gains from pretraining. Specifically, character‑token models often match or exceed the performance of larger pretrained k‑mer or BPE models, whereas subword models appear to benefit from pretraining. We also find that the evaluated GFMs fail to capture clinically relevant genetic mutations, with embeddings and log‑likelihood ratios showing limited sensitivity to annotated variants. For the tasks we study, these results suggest that current NLP‑style pretraining strategies provide modest, tokenizer‑gated improvements over strong random baselines and motivate more biologically informed tokenization and variant‑aware objectives. Our code is available at https://github.com/m42-health/gfm-random-eval.