Tootfinder

SEQ-GPT: LLM-assisted Spatial Query via Example
Ivan Khai Ze Lim, Ningyi Liao, Yiming Yang, Gerald Wei Yong Yip, Siqiang Luo
https://arxiv.org/abs/2508.10486 https://

SEQ-GPT: LLM-assisted Spatial Query via Example
Contemporary spatial services such as online maps predominantly rely on user queries for location searches. However, the user experience is limited when performing complex tasks, such as searching for a group of locations simultaneously. In this study, we examine the extended scenario known as Spatial Exemplar Query (SEQ), where multiple relevant locations are jointly searched based on user-specified examples. We introduce SEQ-GPT, a spatial query system powered by Large Language Models (LLMs) …

HypoGeneAgent: A Hypothesis Language Agent for Gene-Set Cluster Resolution Selection Using Perturb-seq Datasets
Ying Yuan, Xing-Yue Monica Ge, Aaron Archer Waterman, Tommaso Biancalani, David Richmond, Yogesh Pandit, Avtar Singh, Russell Littman, Jin Liu, Jan-Christian Huetter, Vladimir Ermakov
https://arxiv.org/abs/2509.09740

HypoGeneAgent: A Hypothesis Language Agent for Gene-Set Cluster Resolution Selection Using Perturb-seq Datasets
Large-scale single-cell and Perturb-seq investigations routinely involve clustering cells and subsequently annotating each cluster with Gene-Ontology (GO) terms to elucidate the underlying biological programs. However, both stages, resolution selection and functional annotation, are inherently subjective, relying on heuristics and expert curation. We present HYPOGENEAGENT, a large language model (LLM)-driven framework, transforming cluster annotation into a quantitatively optimizable task. Init…

Unix question: is there a version of seq(1) for letters instead of numbers?

Computational Drug Repurposing for Alzheimer's Disease via Sheaf Theoretic Population-Scale Analysis of snRNA-seq Data
Sean Cottrell, Seungmin Yoon, Xiaoqi Wei, Alex Dickson, Guo-Wei Wei
https://arxiv.org/abs/2509.25417

Computational Drug Repurposing for Alzheimer's Disease via Sheaf Theoretic Population-Scale Analysis of snRNA-seq Data
Single-cell and single-nucleus RNA sequencing (scRNA-seq /snRNA-seq) are widely used to reveal heterogeneity in cells, showing a growing potential for precision and personalized medicine. Nonetheless, sustainable drug discovery must be based on a population-level understanding of molecular mechanisms, which calls for the population-scale analysis of scRNA-seq/snRNA-seq data. This work introduces a sequential target-drug selection model for drug repurposing against Alzheimer's Disease (AD) targe…

Cell2Text: Multimodal LLM for Generating Single-Cell Descriptions from RNA-Seq Data
Oussama Kharouiche, Aris Markogiannakis, Xiao Fei, Michail Chatzianastasis, Michalis Vazirgiannis
https://arxiv.org/abs/2509.24840

Cell2Text: Multimodal LLM for Generating Single-Cell Descriptions from RNA-Seq Data
Single-cell RNA sequencing has transformed biology by enabling the measurement of gene expression at cellular resolution, providing information for cell types, states, and disease contexts. Recently, single-cell foundation models have emerged as powerful tools for learning transferable representations directly from expression profiles, improving performance on classification and clustering tasks. However, these models are limited to discrete prediction heads, which collapse cellular complexity …

A Novel cVAE-Augmented Deep Learning Framework for Pan-Cancer RNA-Seq Classification
Vinil Polepalli
https://arxiv.org/abs/2508.02743 https://arxiv.org/pdf…

A Novel cVAE-Augmented Deep Learning Framework for Pan-Cancer RNA-Seq Classification
Pan-cancer classification using transcriptomic (RNA-Seq) data can inform tumor subtyping and therapy selection, but is challenging due to extremely high dimensionality and limited sample sizes. In this study, we propose a novel deep learning framework that uses a class-conditional variational autoencoder (cVAE) to augment training data for pan-cancer gene expression classification. Using 801 tumor RNA-Seq samples spanning 5 cancer types from The Cancer Genome Atlas (TCGA), we first perform feat…

sysctl vm.pageout_oom_seq=120
― for FreeBSD on slow storage.
Pictured: a 2828-step forced reinstallation of 1534 packages – 568 FreeBSD-base, 966 FreeBSD-ports – with root-on-ZFS, 8 G swap, and less than 1 GB memory.
FreeBSD 15.0-PRERELEASE on AMD64 with KDE Plasma and applications, SDDM, and a few other things from the ports collection.
ZFS is not tuned in any way.
zfs --version
zfs-2.4.0-rc1-FreeBSD_g00dfa094a
zfs-kmod-2.4.0-zfs-2.4.0-rc1-FreeBSD_g…

Generalised Joyal disks and $\Theta_d$-colored $(d 1)$-operads
Boris Shoikhet
https://arxiv.org/abs/2510.05813 https://arxiv.org/pdf/2510.05813

Generalised Joyal disks and $Θ_d$-colored $(d+1)$-operads
In this paper, we propose a method for constructing a colored $(d+1)$-operad $\mathbf{seq}_d$ in $\mathrm{Sets}$, in the sense of Batanin [Ba1,2], whose category of colors (=the category of unary operations) is the category $Θ_d$, dual to the Joyal category of $d$-disks [J], [Be2,3]. For $d=1$ it is the Tamarkin $Δ$-colored 2-operad $\mathbf{seq}$, playing an important role in his paper [T3] and in the solution loc.cit. to the Deligne conjecture for Hochschild cochains. We expect that for hig…

A tree-based kernel for densities and its applications in clustering DNase-seq profiles
Yuliang Xu, Kaixuan Luo, Li Ma
https://arxiv.org/abs/2509.15480 https://

A tree-based kernel for densities and its applications in clustering DNase-seq profiles
Modeling multiple sampling densities within a hierarchical framework enables borrowing of information across samples. These density random effects can act as kernels in latent variable models to represent exchangeable subgroups or clusters. A key feature of these kernels is the (functional) covariance they induce, which determines how densities are grouped in mixture models. Our motivating problem is clustering chromatin accessibility profiles from high-throughput DNase-seq experiments to detec…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/4]:
- Knowledge-Guided Biomarker Identification for Label-Free Single-Cell RNA-Seq Data: A Reinforcemen...
Meng Xiao, Weiliang Zhang, Xiaohan Huang, Hengshu Zhu, Min Wu, Xiaoli Li, Yuanchun Zhou

A Quantum Generative Framework for Modeling Single-Cell Transcriptomes with Gene-Gene and Cell-Cell Interactions
Selim Romero, Vignesh Kumar, Robert S. Chapkin, James J. Cai
https://arxiv.org/abs/2510.12776

A Quantum Generative Framework for Modeling Single-Cell Transcriptomes with Gene-Gene and Cell-Cell Interactions
Single-cell RNA sequencing (scRNA-seq) data simulation is limited by classical methods that rely on linear correlations, failing to capture the intrinsic, nonlinear dependencies and the simultaneous gene-gene and cell-cell interactions. We introduce qSimCells, a novel hybrid quantum-classical simulator that leverages quantum entanglement to model single-cell transcriptomes. The core innovation is a quantum kernel that uses a parameterized quantum circuit with CNOT gates to encode complex, nonli…

Why?
Because in some tests with vm.pageout_oom_seq not tuned:
— 12 GB memory was insufficient for a pkg upgrade routine.
Last month's <https://www.reddit.com/r/freebsd/comments/1lcm1ze/comment/nb37hsb/>, for example, although – be warned…

Enhanced Single-Cell RNA-seq Embedding through Gene Expression and Data-Driven Gene-Gene Interaction Integration
Hojjat Torabi Goudarzi, Maziyar Baran Pouyan
https://arxiv.org/abs/2509.02639

Enhanced Single-Cell RNA-seq Embedding through Gene Expression and Data-Driven Gene-Gene Interaction Integration
Single-cell RNA sequencing (scRNA-seq) provides unprecedented insights into cellular heterogeneity, enabling detailed analysis of complex biological systems at single-cell resolution. However, the high dimensionality and technical noise inherent in scRNA-seq data pose significant analytical challenges. While current embedding methods focus primarily on gene expression levels, they often overlook crucial gene-gene interactions that govern cellular identity and function. To address this limitatio…

Addressing the Cold-Start Problem for Personalized Combination Drug Screening
Antoine de Mathelin, Christopher Tosh, Wesley Tansey
https://arxiv.org/abs/2509.07850 https://

Addressing the Cold-Start Problem for Personalized Combination Drug Screening
Personalizing combination therapies in oncology requires navigating an immense space of possible drug and dose combinations, a task that remains largely infeasible through exhaustive experimentation. Recent developments in patient-derived models have enabled high-throughput ex vivo screening, but the number of feasible experiments is limited. Further, a tight therapeutic window makes gathering molecular profiling information (e.g. RNA-seq) impractical as a means of guiding drug response predict…

scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction
Zhaokang Liang, Shuyang Zhuang, Xiaoran Jiao, Weian Mao, Hao Chen, Chunhua Shen
https://arxiv.org/abs/2510.11726

scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction
This paper introduces the Single-Cell Perturbation Prediction Diffusion Model (scPPDM), the first diffusion-based framework for single-cell drug-response prediction from scRNA-seq data. scPPDM couples two condition channels, pre-perturbation state and drug with dose, in a unified latent space via non-concatenative GD-Attn. During inference, factorized classifier-free guidance exposes two interpretable controls for state preservation and drug-response strength and maps dose to guidance magnitude…

Multiple Hypothesis Testing To Estimate The Number Of Communities in Stochastic Block Models
Chetkar Jha, Mingyao Li, Ian Barnett
https://arxiv.org/abs/2507.15471

Multiple Hypothesis Testing To Estimate The Number Of Communities in Stochastic Block Models
Clustering of single-cell RNA sequencing (scRNA-seq) datasets can give key insights into the biological functions of cells. Therefore, it is not surprising that network-based community detection methods (one of the better clustering methods) are increasingly being used for the clustering of scRNA-seq datasets. The main challenge in implementing network-based community detection methods for scRNA-seq datasets is that these methods \emph{apriori} require the true number of communities or blocks f…

MultiSurv: A Multimodal Deep Survival Framework for Prostrate and Bladder Cancer
Noorul Wahab, Ethar Alzaid, Jiaqi Lv, Adam Shephard, Shan E Ahmed Raza
https://arxiv.org/abs/2509.05037

MultiSurv: A Multimodal Deep Survival Framework for Prostrate and Bladder Cancer
Accurate prediction of time-to-event outcomes is a central challenge in oncology, with significant implications for treatment planning and patient management. In this work, we present MultiSurv, a multimodal deep survival model utilising DeepHit with a projection layer and inter-modality cross-attention, which integrates heterogeneous patient data, including clinical, MRI, RNA-seq and whole-slide pathology features. The model is designed to capture complementary prognostic signals across modali…

SiCmiR Atlas: Single-Cell miRNA Landscapes Reveals Hub-miRNA and Network Signatures in Human Cancers
Xiao-Xuan Cai, Jing-Shan Liao, Jia-Jun Ma, Yu-Xuan Pang, Yi-Gang Chen, Yang-Chi-Dung Lin, Yi-Dan Chen, Xin Cao, Yi-Cheng Zhang, Tao-Sheng Xu, Tzong-Yi Lee, Hsi-Yuan Huang, Hsien-Da Huang
https://arxiv.org/abs/2508.05692

SiCmiR Atlas: Single-Cell miRNA Landscapes Reveals Hub-miRNA and Network Signatures in Human Cancers
microRNA are pivotal post-transcriptional regulators whose single-cell behavior has remained largely inaccessible owing to technical barriers in single-cell small-RNA profiling. We present SiCmiR, a two-layer neural network that predicts miRNA expression profile from only 977 LINCS L1000 landmark genes reducing sensitivity to dropout of single-cell RNA-seq data. Proof-of-concept analyses illustrate how SiCmiR can uncover candidate hub-miRNAs in bulk-seq cell lines and hepatocellular carcinoma, …

TrajLens: Visual Analysis for Constructing Cell Developmental Trajectories in Cross-Sample Exploration
Qipeng Wang, Shaolun Ruan, Rui Sheng, Yong Wang, Min Zhu, Huamin Qu
https://arxiv.org/abs/2507.15620

TrajLens: Visual Analysis for Constructing Cell Developmental Trajectories in Cross-Sample Exploration
Constructing cell developmental trajectories is a critical task in single-cell RNA sequencing (scRNA-seq) analysis, enabling the inference of potential cellular progression paths. However, current automated methods are limited to establishing cell developmental trajectories within individual samples, necessitating biologists to manually link cells across samples to construct complete cross-sample evolutionary trajectories that consider cellular spatial dynamics. This process demands substantial…

Generalized Additive Modeling of TRPM4-Ribo Transcriptional Space in Prostate Cancer
Kah Keng Wong
https://arxiv.org/abs/2509.12073 https://arxiv.org/pdf/2…

Generalized Additive Modeling of TRPM4-Ribo Transcriptional Space in Prostate Cancer
TRPM4 is overexpressed in prostate cancer (PCa) associated with metastasis or recurrence. There is paucity of information pertaining to TRPM4 characterization and functions at single-cell level in PCa. In this study, generalized additive model (GAM) was utilized to model the relationship between TRPM4 and genes shortlisted using Spearman-Kendall dual-filter in aggressive PCa and benign prostate (BP) control cells derived from scRNA-seq dataset. Seven ribosomal genes (RPL10, RPL27, RPL28, RPS2, …

Network Community Detection and Novelty Scoring Reveal Underexplored Hub Genes in Rheumatoid Arthritis
Neda Amirirad, Hiroki Sayama
https://arxiv.org/abs/2509.00897 https://

Network Community Detection and Novelty Scoring Reveal Underexplored Hub Genes in Rheumatoid Arthritis
Understanding the modular structure and central elements of complex biological networks is critical for uncovering system-level mechanisms in disease. Here, we constructed weighted gene co-expression networks from bulk RNA-seq data of rheumatoid arthritis (RA) synovial tissue, using pairwise correlation and a percolation-guided thresholding strategy. Community detection with Louvain and Leiden algorithms revealed robust modules, and node-strength ranking identified the top 50 hub genes globally…

Nullstrap-DE: A General Framework for Calibrating FDR and Preserving Power in DE Methods, with Applications to DESeq2 and edgeR
Chenxin Jiang, Changhu Wang, Jingyi Jessica Li
https://arxiv.org/abs/2507.20598

Nullstrap-DE: A General Framework for Calibrating FDR and Preserving Power in DE Methods, with Applications to DESeq2 and edgeR
Differential expression (DE) analysis is a key task in RNA-seq studies, aiming to identify genes with expression differences across conditions. A central challenge is balancing false discovery rate (FDR) control with statistical power. Parametric methods such as DESeq2 and edgeR achieve high power by modeling gene-level counts using negative binomial distributions and applying empirical Bayes shrinkage. However, these methods may suffer from FDR inflation when model assumptions are mildly viola…

Replaced article(s) found for q-bio.GN. https://arxiv.org/list/q-bio.GN/new
[1/1]:
- Knowledge-Guided Biomarker Identification for Label-Free Single-Cell RNA-Seq Data: A Reinforcemen...
Meng Xiao, Weiliang Zhang, Xiaohan Huang, Hengshu Zhu, Min Wu, Xiaoli Li, Yuanchun Zhou

Crosslisted article(s) found for stat.AP. https://arxiv.org/list/stat.AP/new
[1/1]:
- A tree-based kernel for densities and its applications in clustering DNase-seq profiles
Yuliang Xu, Kaixuan Luo, Li Ma

CARTEpigenoQC: A Quality Control Toolkit for CAR-T Single-Cell Epigenomic Data
Kaitao Lai
https://arxiv.org/abs/2507.23048 https://arxiv.org/pdf/2507.23048…

CARTEpigenoQC: A Quality Control Toolkit for CAR-T Single-Cell Epigenomic Data
CARTEpigenoQC is an R-based toolkit designed to streamline quality control (QC) for single-cell epigenomic datasets involving Chimeric Antigen Receptor (CAR)-engineered T cells. With the growing application of scATAC-seq, scCUT&Tag, and scBS-seq to characterize CAR-T cell states, it has become critical to perform customized QC that not only addresses standard metrics like FRiP (Fraction of Reads in Peaks) and TSS enrichment, but also directly detects signal from CAR vector insertion sites. CART…

Neural Proteomics Fields for Super-resolved Spatial Proteomics Prediction
Bokai Zhao, Weiyang Shi, Hanqing Chao, Zijiang Yang, Yiyang Zhang, Ming Song, Tianzi Jiang
https://arxiv.org/abs/2508.17389

Neural Proteomics Fields for Super-resolved Spatial Proteomics Prediction
Spatial proteomics maps protein distributions in tissues, providing transformative insights for life sciences. However, current sequencing-based technologies suffer from low spatial resolution, and substantial inter-tissue variability in protein expression further compromises the performance of existing molecular data prediction methods. In this work, we introduce the novel task of spatial super-resolution for sequencing-based spatial proteomics (seq-SP) and, to the best of our knowledge, propo…

scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis
Ping Xu, Zaitian Wang, Zhirui Wang, Pengjiang Li, Ran Zhang, Gaoyang Li, Hanyu Xie, Jiajia Wang, Yuanchun Zhou, Pengfei Wang
https://arxiv.org/abs/2509.25884

scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis
Single-cell RNA sequencing (scRNA-seq) technology enables systematic delineation of cellular states and interactions, providing crucial insights into cellular heterogeneity. Building on this potential, numerous computational methods have been developed for tasks such as cell clustering, cell type annotation, and marker gene identification. To fully assess and compare these methods, standardized, analysis-ready datasets are essential. However, such datasets remain scarce, and variations in data …

Single-cell sequencing of trophoblasts in preeclampsia and chemical hypoxia in BeWo b30 cells reveals EBI3, COL17A1, miR-27a-5p, and miR-193b-5p as hypoxia-response markers
Evgeny Knyazev (Faculty of Biology and Biotechnology, HSE University, Moscow, Russia, Laboratory of Microfluidic Technologies for Biomedicine, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia), Timur Kulagin (Faculty of Biology and Biotechnology, HSE Universi…

Single-cell sequencing of trophoblasts in preeclampsia and chemical hypoxia in BeWo b30 cells reveals EBI3, COL17A1, miR-27a-5p, and miR-193b-5p as hypoxia-response markers
Background. Preeclampsia (PE) complicates 2-8% of pregnancies and is marked by placental hypoxia and HIF-pathway activation, especially in early-onset PE (eoPE). Integrating patient tissue analyses with experimental models may reveal common molecular markers of trophoblast hypoxia. Methods. We analyzed scRNA-seq data from 10 eoPE, 7 late-onset PE (loPE), and corresponding control placentas, identifying villous cytotrophoblast (VCT), syncytiotrophoblast (SCT), and extravillous trophoblast (EVT…