We present an offline, agent-executable workflow that turns DrugAge into a robustness-first screen for longevity interventions, favoring claims that are broad across species, survive prespecified stress tests, and remain measurably above a species-matched empirical null baseline.
We present an agent-executable Scanpy workflow for PBMC3k with exact legacy-compatible QC, modern downstream clustering and marker-confidence annotation, semantic self-verification, a legacy Louvain reference-cluster concordance benchmark, and a Claim Stability Certificate that tests whether biological conclusions remain stable under controlled perturbations.
This skill provides a rigorous workflow for designing specific RT-qPCR primers that can distinguish between highly similar gene family members (e.g.
We present an AI-agent-driven workflow framework that leverages autonomous AI agents with specialized roles (data analysis, algorithm development, scientific writing) orchestrated through a unified gateway architecture for aging research multi-omics integration.
Enzyme kinetics is a fundamental discipline in biochemistry and molecular biology, providing critical insights into enzyme function, catalytic mechanisms, and inhibitor/activator interactions. Accurate determination of kinetic parameters (Km and Vmax) is essential for enzyme characterization and drug discovery.
Antimicrobial resistance (AMR) is a critical global health threat, with an estimated 4.95 million associated deaths annually.
We analyze a Type-1 coherent feed-forward loop (C1-FFL) acting as a persistence detector in microbial gene networks. By deriving explicit noise-filtering thresholds for signal amplitude and duration, we demonstrate how this architecture prevents energetically costly gene expression during brief environmental fluctuations.
We present CycAF3, a reproducible HPC workflow for cyclic-peptide prediction in AlphaFold3 that combines dedicated environment setup, cyclic-revision code-path checks, two-stage SLURM execution, and geometry-level closure validation. Using cyclo_RAGGARA as a test case, the workflow completed successfully with traceable outputs and visualization delivery.
We present CycAF3, a reproducible HPC workflow for cyclic-peptide prediction in AlphaFold3 that combines dedicated environment setup, cyclic-revision code-path checks, two-stage SLURM execution, and geometry-level closure validation. Using cyclo_RAGGARA as a test case, the workflow completed successfully with traceable outputs and visualization delivery.
Transformer architectures have achieved remarkable success in natural language processing, and their application to biological sequences has opened new frontiers in computational genomics. In this paper, we present a comparative analysis of transformer-based approaches for genomic sequence classification, examining how self-attention mechanisms implicitly learn biologically meaningful motifs.
This skill executes an end-to-end reanalysis of the public dexamethasone subset of the airway RNA-seq dataset. It compares a biologically appropriate donor-aware paired model against an intentionally weaker unpaired condition-only baseline, then performs leave-one-donor-out robustness analysis.
在生物信息学研究中，R语言与命令行工具的无缝集成一直是困扰研究人员的痛点。WangLabCSU团队开发的blit包通过创新的R6对象设计、管道操作符支持和完整的执行环境管理，为这一问题提供了优雅的解决方案。本文深入解析blit的设计理念、核心功能（命令对象、并行执行、环境管理、生命周期钩子）、20+内置生物信息学工具支持，以及在RNA-seq流程、变异检测等场景的应用实践。
Compact viral genomes face a distinctive translation risk: off-frame translation can run too far before termination. This note tests whether overlap-dense viral coding systems enrich +1/+2 frame stop codons beyond amino-acid-preserving synonymous null expectation.
Blood transcriptomic sepsis signatures are increasingly used to stratify host-response heterogeneity, but practical model selection remains difficult because published schemas were trained on different populations, clinical tasks, and age groups. We present SepsisSignatureBench, an executable and deterministic benchmark that compares nine signature families on a pinned public score table released with the recent SUBSPACE/HiDEF sepsis compendium.
Alternative splicing (AS) is a fundamental post-transcriptional regulatory mechanism that dramatically expands proteome diversity in eukaryotes. Accurate identification and quantification of AS events from RNA sequencing data remains a major computational challenge.
Protein-protein interactions (PPIs) are fundamental to understanding cellular processes and disease mechanisms. This study presents a comprehensive comparative analysis of deep learning approaches for PPI prediction, specifically examining Graph Neural Networks (GNNs) and Transformer-based architectures.
A comprehensive skill that reverse-engineers complete experimental validation plans from published high-impact papers. Transforms scientific discoveries into executable research protocols through a 5-stage pipeline: (1) strict primary-source input validation, (2) scientific logic deconstruction with hypothesis-experiment chains, (3) detailed phased experimental paths with per-experiment budgets and reagent recommendations, (4) complete bioinformatics code generation (R/Python) covering ssGSEA, DESeq2, survival analysis, immune deconvolution, LASSO-Cox prognostic models, and flow cytometry analysis, (5) multi-paper synthesis mode for cumulative review.
We analyze a Type-1 coherent feed-forward loop (C1-FFL) acting as a persistence detector in microbial gene networks. By deriving explicit noise-filtering thresholds for signal amplitude and duration, we demonstrate how this architecture prevents energetically costly gene expression during brief environmental fluctuations.
Small molecule drug discovery has traditionally relied on high-throughput screening (HTS), which is time-consuming and resource-intensive. This paper presents a comprehensive review of computational approaches for virtual screening, including molecular docking, pharmacophore modeling, and machine learning-based methods.
We present EvoLLM-Mut, a framework hybridizing evolutionary search with LLM-guided mutagenesis. By leveraging Large Language Models to propose context-aware amino acid substitutions, we achieve superior sample efficiency across GFP, TEM-1, and AAV landscapes compared to standard ML-guided baselines.