arXiv Machine Learning

Continuous surrogates versus threshold Boolean networks for modeling Arabidopsis ISR gene regulation

arXiv:2607. 23289v1 Announce Type: cross Abstract: Gene regulatory network modeling often requires balancing predictive accuracy and mechanistic interpretability.

arXiv Machine Learning

Jun 19

SEAGAN: domain-Specific and Edge-Aware Graph Attention Network for Dynamic Plant Processes

arXiv:2606. 19623v1 Announce Type: new Abstract: Graph neural networks (GNNs) provide a flexible framework for learning from scientific data linked through physical, biological, or functional relationships.

By Antriksh Srivastava, Soumyashree Kar

benchmarks

arXiv AI

Jul 23

Making Single-Cell Data Distillation Auditable: Traceable Real-Cell Coresets via Discrete Min-Max Selection

arXiv:2607. 19426v1 Announce Type: cross Abstract: Single-cell datasets are increasingly costly to store, audit, and reuse for model training.

By Yaodi Luo, Peize He, Bowen Han, Lingbei Mengg

efficiency

arXiv Machine Learning

Jul 9

Reliable mechanistic operator recovery with biologically-informed neural networks: principles for architecture and optimisation design

arXiv:2607. 07425v1 Announce Type: cross Abstract: Many biological processes are governed by complex dynamical mechanisms that remain incompletely understood despite increasing volumes of experimental data.

By Rebecca M. Crossley, Yuan Yin, Sarah L. Waters, Ruth E. Baker

rag diffusion benchmarks

arXiv Machine Learning

Jun 24

GRACE: Gated Refinement for Accurate Causal Edge Discovery in High-Dimensional Time Series

arXiv:2606. 23880v1 Announce Type: new Abstract: From climate teleconnections to gene regulation, modern time-series datasets encompass tens or hundreds of interacting variables, making causal discovery increasingly challenging.

By Mohammad Fesanghary, Abhinav Havaldar

benchmarks

arXiv AI

Aug 10

Control-Anchored Residual Flow Matching Conditioned on Gene Geometry for Virtual Cell Perturbation Modeling

arXiv:2608. 06824v1 Announce Type: cross Abstract: A central task in virtual cell modeling is predicting single-cell transcriptional responses to unseen genetic perturbations and drug combinations, and biological networks provide valuable priors on gene relationships.

By Quanquan Li, Yihe Chi, Liuyang Song, Hongbo Zhang, Jingyu Li, Xidong Xi, Conghua Wei, Yijie Sun, Yu Chen, Xin Liu, Qi Hu, Jing Ke, Guitao Cao

diffusion benchmarks

arXiv Machine Learning

Aug 10

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer

arXiv:2608. 06486v1 Announce Type: new Abstract: In a feature-tokenized transformer (arXiv:2106.

By Oren Nelson

llms

arXiv Machine Learning

Aug 5

LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees

arXiv:2602. 22432v2 Announce Type: replace-cross Abstract: Gradient-boosted decision trees are among the strongest off-the-shelf predictors for tabular regression, but point predictions alone do not quantify uncertainty.

By Vagner Santos, Victor Coscrato, Luben Cabezas, Rafael Izbicki, Thiago Ramos

arXiv AI

Aug 7

CASCADE: An Agentic Regulatory Network Framework for Patient-Data-Validated Downstream Perturbation Prediction

arXiv:2608. 05359v1 Announce Type: new Abstract: CASCADE is an agentic framework that predicts downstream transcriptional effects of gene perturbation from precomputed ARACNe regulatory networks, exposed via MCP.

By Jose A. Bird

llms agents benchmarks

arXiv AI

Aug 5

Making Single-Cell Data Distillation Auditable: Traceable Real-Cell Coresets via Discrete Min--Max Selection

arXiv:2607. 19426v2 Announce Type: replace-cross Abstract: Large single-cell datasets are expensive to store, curate, and repeatedly reuse for model training.

By Yaodi Luo, Peize He, Lingbei Meng, Bowen Han, Zheng Lu, Jianqing Zhu, Lian Zhang

rag efficiency benchmarks

arXiv Machine Learning

Jul 31

PlantBGC: Transformer for Plant BGC Discovery via Label-Free Domain Adaptation and Weak Supervision

arXiv:2607. 27258v1 Announce Type: cross Abstract: Plant biosynthetic gene clusters (BGCs) encode specialized-metabolite pathways, yet curated plant BGC labels remain scarce, hindering supervised discovery at genome scale.

By Yuhan Zhao, Nidhi Grover, Zhishan Guo, Ning Sui

llms fine-tuning benchmarks

arXiv AI

Jun 29

GRAFT: Biological Graph and Hypergraph Benchmarks for Linked Gene Expression and Phenotypic Trait Prediction in Arabidopsis thaliana

arXiv:2606. 27413v1 Announce Type: cross Abstract: Understanding which genes control which traits in an organism remains one of the central challenges in biology.

By Manuel Serna-Aguilera, Vanshika Jindal, Fiona L. Goggin, Jiamei Li, Aranyak Goswami, Alexander Bucksch, Suxing Liu, Khoa Luu

multimodal benchmarks

arXiv AI

Aug 18

$D^{2}R^{2}$: Discrete Diffusion with Regulation Reinforcement for Single-Cell Perturbation Prediction

arXiv:2608. 15288v1 Announce Type: new Abstract: Predicting single-cell transcriptomic responses to genetic perturbations is central to functional genomics and virtual-cell modeling.

By Ninghan Fan, Qi Liu, Xunuo Zhu, Yukai Sun, Luyuan Chen, Xuheng Zhou, Yuetian Du, Ming Kong, Xiaojun Zhu, Jie Liu, Zhan Zhou, Qiang Zhu

diffusion

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