CoV-UniBind: a unified antibody binding database for SARS-CoV-2 [0.03%]
CoV-UniBind：SARS-CoV-2抗体结合统一数据库
Aryan Bhasin,Francesco Saccon,Callum Canavan et al.
Aryan Bhasin et al.
Summary: Since the emergence of SARS-CoV-2, numerous studies have investigated antibody interactions with viral variants in vitro, and several datasets have been curated to compile available protein structures and experim...
ggplotAgent: a self-debugging multi-modal agent for robust and reproducible scientific visualization [0.03%]
ggplotAgent：一种用于稳健且可重复的科学可视化的自调试多模态代理
Zelin Wang,Yuanyuan Yin,Jien Wang et al.
Zelin Wang et al.
Motivation: Creating publication-quality visualizations is essential for bioinformatics but remains a bottleneck for researchers with limited coding expertise. While Large Language Models (LLMs) are proficient at generati...
Prompt-to-Pill: Multi-Agent Drug Discovery and Clinical Simulation Pipeline [0.03%]
从提示到药物：多智能体药物发现和临床模拟流水线
Ivana Vichentijevikj,Kostadin Mishev,Monika Simjanoska Misheva
Ivana Vichentijevikj
Summary: This study presents a proof-of-concept, comprehensive, modular framework for AI-driven drug discovery (DD) and clinical trial simulation, spanning from target identification to virtual patient recruitment. Synthe...
Pipeasm: a tool for automated large chromosome-scale genome assembly and evaluation [0.03%]
Pipeasm：一种用于大型染色体规模基因组组装和评估的自动化工具
Bruno Marques Silva,Fernanda de Jesus Trindade,Lucas Eduardo Costa Canesin et al.
Bruno Marques Silva et al.
Motivation: Although high-quality chromosome-scale genome assemblies are feasible, assembling large ones remains complex and resource-intensive. This demands reproducible and automated workflows that not only implement cu...
SLiMs prediction method based on enhanced attention mechanism and feature fusion [0.03%]
基于增强注意力机制和特征融合的SLiMs预测方法
Yifan Hao,Hao He
Yifan Hao
Motivation: Short linear motifs (SLiMs) are functional regions composed of short sequences of specific amino acids. They usually do not have independent 3D three-dimensional structures, but play important roles in biologi...
Omics BioAnalytics: an RShiny application for multimodal biomarker panel discovery and assessment [0.03%]
基于RShiny的多模态生物标志物检测面板发现和评估应用程序omics bioanalytics
Josh Dyce,Lea Rieskamp,Scott J Tebbutt et al.
Josh Dyce et al.
Motivation: Machine learning offers a powerful approach for building predictive models from high-dimensional molecular data. Omics technologies such as transcriptomics, proteomics, and metabolomics quantify thousands of m...
Prompt-based bioinformatic pipeline generation for a multi-step metaviral workflow [0.03%]
基于提示的生物信息管道生成以用于多步元病毒工作流程
Pengchong Ma,Haoze Zheng,Weijun Yi et al.
Pengchong Ma et al.
Motivation: The rapid evolution of bioinformatics tools and multi-step analytic procedure presents a challenge for building effective pipelines, particularly for researchers without extensive programming expertise. This s...
Beyond synthetic lethality in large-scale metabolic and regulatory network models via genetic minimal intervention set [0.03%]
基于大规模代谢和调控网络模型的合成致死效应及其遗传最小干预集分析方法
Naroa Barrena,Carlos Rodriguez-Flores,Luis V Valcárcel et al.
Naroa Barrena et al.
Motivation: The integration of genome-scale metabolic and regulatory networks has received significant interest in cancer systems biology. However, the identification of lethal genetic interventions in these integrated mo...
Fluoro-forest: a random forest workflow for cell type annotation in high-dimensional immunofluorescence imaging with limited training data [0.03%]
荧光森林：一种用于高维免疫荧光成像细胞类型注释的随机森林工作流（在训练数据有限的情况下）
Joshua Brand,Wei Zhang,Evie Carchman et al.
Joshua Brand et al.
Motivation: Cyclic immunofluorescence (IF) techniques enable deep phenotyping of cells and help quantify tissue organization at high resolution. Due to its high dimensionality, workflows typically rely on unsupervised clu...
DCGAT-DTI: dynamic cross-graph attention network for drug-target interaction prediction [0.03%]
基于动态跨图注意网络的药物-靶点相互作用预测模型
Abrar Rahman Abir,Muhtasim Noor Alif,Wencai Zhang et al.
Abrar Rahman Abir et al.
Motivation: Drug-target interaction (DTI) prediction accelerates drug discovery by identifying interactions between chemical compounds and proteins. Existing methods often rely on drug-drug and protein-protein similarity ...