Scientific Workflow Automation

Researchers have been duct taping biology workflows together for decades. SciSpace just flipped the script. They built a BioMed Agent that takes you from idea to interpretation in a single reasoning chain. Not a chatbot. Not a toy. A system that thinks like a biological scientist. Modern biology is chaos. You search papers. Design constructs. Run omics. Analyze variants. Draft figures. All in different tools. All manually stitched. BioMed Agent pulls this into one unified intelligence. It is not a general assistant. It is a domain-built engine trained for molecular, cellular, and clinical reasoning. You ask biological questions. It responds with workflows, not text blurbs. I tried it on a cloning problem. One prompt defined the strategy, vector backbone, restriction sites, primers, and QC checks. It even flagged conflicts in the design automatically. That is real experimental planning, not autocomplete. Then I switched to immune profiling. I dropped in bulk RNA-seq from activated T cells. It processed counts, normalized data, ran differential expression, and surfaced pathway enrichments. A full analysis cycle in minutes, not days. Genomics is where it really shows depth. Feed it a variant list and phenotype notes. It prioritizes candidates, reads ClinVar logic, checks inheritance patterns, and scores pathogenicity. This is built for real cases, not conference demos. It also runs drug logic. You can compare inhibitors, predict ADMET liabilities, map pathway effects, and surface off-target risks in a single flow. It feels like the scaffolding of an integrated discovery engine. And then there is the illustration layer. Describe a mechanism, a signaling axis, or a clinical workflow. The agent creates clear, publication-grade diagrams that match your scientific intent. Need revisions? Remove a component. Add a molecular event. Change directionality. The system redraws the entire figure around your biological instructions. A design tool built on scientific logic, not clip art. For labs, clinics, and biotech teams, this unlocks something new: Experiment design, computational analysis, variant interpretation, and figure creation finally live in one place. It compresses timelines at every stage of discovery. If you want to feel what an integrated scientific agent actually is, here is early access: Try BioMed Agent → https://lnkd.in/gbtTHuGn Global: PPBIO20 (20% off monthly), PPBIO40 (40% off annual). India: PPBIO30 (30% off monthly & annual on Premium/Advanced). Give it your hardest prompt. Watch how it builds the reasoning chain you used to assemble by hand.

Have you ever wished your automation tool could combine the flexibility of code with the simplicity of visual flow design? That's precisely why n8n is making waves right now - and how I built a visual semantic search system in under a day. And here’s how I put that flexibility to work: I built an end-to-end image embedding + retrieval system using n8n, OpenAI, Cohere, and Qdrant. Workflow highlights: 1) Generate images from prompts (OpenAI - DALL-E 3) 2) Save them to Google Drive with shareable links 3) Create image embeddings via Cohere 4) Upsert everything into Qdrant for vector search 5) Retrieve all pre-existing images which are similar to the currently generated image Two BIG challenges I overcame: 1) Qdrant Upsert Node required very very very precise JSON syntax - even tiny misplacements in id, vector, or payload would lead to 400 errors. 2) Parallel branches broke timing - my initial design ran image upload and embedding in parallel, but race conditions led to lost URLs. I resolved it by switching to sequential execution to ensure reliability. Want the full breakdown? I wrote about every node, why each step matters, and how it connects in closed-loop fashion. Read my full article on Medium: https://lnkd.in/gK4TzcJU Try it yourself - download the n8n workflow: https://lnkd.in/g2cgBbeU n8n isn’t just hype. It’s a force multiplier for anyone building automation - without sacrificing control, flexibility, or efficiency. #n8n #WorkflowAutomation #Cohere #Qdrant #AutomationSolutions #ImageGeneration #ArtificialIntelligence #OpenAI #DataScience #MachineLearning #DeepLearning #VectorSearch #EmbeddingsSearch #PrompttoImage

A GitHub repo just gave Claude Code 140+ scientific skills. Drug discovery. Single-cell genomics. Proteomics. Clinical trials. Graph ML. All callable through natural language. The repo, claude-scientific-skills by K-Dense AI, connects tools like RDKit, DiffDock, Scanpy, ChEMBL, and PyTorch Geometric behind one instruction layer. You describe the research intent. The agent assembles the pipeline. Most people will see this as a workflow improvement. It is actually a role boundary shift. For years, strong computational biologists were defined by three things: Tool fluency. Library knowledge. Pipeline assembly. That layer is no longer the differentiator. A researcher can now chain a ChEMBL query into a DiffDock screen into structure-activity analysis in one session. Execution is becoming automated infrastructure. The constraint moves upstream. What does not get automated: Choosing the right hypothesis. Interpreting ambiguous results. Knowing when exploration becomes diminishing returns. Most organizations never separated those skills. The same people who designed the experiments also built the pipelines. That coupling is breaking. The teams that move fastest will not be the ones with the best pipeline engineers. They will be the ones with the strongest research judgment. Execution is becoming software. Scientific thinking is the leverage. Repo: https://lnkd.in/gzAHEHis If you are working through what this means for your research or AI strategy, I am happy to think through it with you.

MIT: Automating Scientific Discovery with SciAgents: A Multi-Agent Approach ... AI is revolutionizing scientific research, empowering us to explore uncharted territories and uncover new discoveries. However, traditional methods often fall short in harnessing the full potential of AI. That's where SciAgents comes in – a cutting-edge framework developed by MIT researchers Alireza Ghafarollahi and Markus J. Buehler that automates scientific discovery through multi-agent intelligent graph reasoning. Key Innovations 👉 1. Automated Hypothesis Generation SciAgents leverages large-scale ontological knowledge graphs, powerful language models, and specialized AI agents to autonomously generate and refine research hypotheses. By uncovering hidden interdisciplinary relationships, SciAgents has successfully produced innovative material discoveries in fields like biologically inspired materials. 👉 2. Multi-Agent Collaboration The framework employs a team of agents, each with a distinct role (e.g., ontologist, scientist, critic), who work together to collaboratively generate and critique hypotheses. This synergistic approach enhances the efficiency and depth of scientific inquiry, leading to breakthroughs that would be difficult to achieve through traditional methods. 👉 3. Enhanced Research Capabilities By integrating large language models and knowledge graphs, SciAgents amplifies the exploratory power of research. Case studies demonstrate how SciAgents has generated groundbreaking material discoveries, showcasing its potential to revolutionize materials science. 👉 4. Assessing Novelty and Feasibility SciAgents employs tools like the Semantic Scholar API to assess the novelty and feasibility of generated hypotheses against existing literature. This ensures that new research directions are both innovative and grounded in current scientific knowledge. 👉 5. Implications for the Future The automation of scientific discovery has far-reaching implications. SciAgents has the potential to accelerate research, foster interdisciplinary collaboration, and lead to more sustainable and efficient research practices across various scientific fields. SciAgents represents a significant leap forward in the integration of AI and scientific research. By harnessing the power of multi-agent systems, knowledge graphs, and language models, this framework opens up new avenues for exploration and discovery.

"𝗠𝗮𝗸𝗶𝗻𝗴 𝘀𝗲𝗻𝘀𝗲 𝗼𝗳 𝗯𝗶𝗼𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗰𝘀 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺𝘀 - 𝗮𝗻𝗱 𝘄𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗲𝗮𝗰𝗵" One of the hardest things in bioinformatics isn’t writing code. It’s choosing the right ecosystem for the right problem - without getting lost in a sea of packages. Here’s a field guide I wish I had on Day 1 👇 ⸻ ✅ 𝗥 + 𝗕𝗶𝗼𝗰𝗼𝗻𝗱𝘂𝗰𝘁𝗼𝗿  • 𝗖𝗼𝗿𝗲 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵: Statistical genomics and mature biological workflows.  • 𝗪𝗵𝗲𝗿𝗲 𝗶𝘁 𝘀𝗵𝗶𝗻𝗲𝘀: Bulk RNA-seq (DESeq2, edgeR, limma), single-cell (Seurat, monocle3), methylation (minfi), proteomics (MSnbase), microbiome (phyloseq).  • 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Deep integration with biology-specific objects (SummarizedExperiment, SingleCellExperiment) and decades of domain-specific methods.  • 𝗣𝗿𝗼 𝘁𝗶𝗽: Even if you prefer Python, many legacy pipelines, high-impact papers, and standardized workflows are still R-first. ⸻ ✅ 𝗣𝘆𝘁𝗵𝗼𝗻 + 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 / 𝗠𝗟 𝗦𝘁𝗮𝗰𝗸  • 𝗖𝗼𝗿𝗲 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵: Scalability, interoperability with machine learning, and handling large datasets.  • 𝗪𝗵𝗲𝗿𝗲 𝗶𝘁 𝘀𝗵𝗶𝗻𝗲𝘀: Single-cell (Scanpy, scvi-tools), spatial omics (Squidpy), genomics (pysam, pybedtools), image analysis (OpenCV, napari), multi-omics integration.  • 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Plays well with AI/ML (PyTorch, TensorFlow) and can handle millions of data points without buckling.  • 𝗣𝗿𝗼 𝘁𝗶𝗽: Learn AnnData and Pandas - they're the backbone of Python bioinformatics workflows. ⸻ ✅ 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 & 𝗥𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗖𝗼𝗿𝗲 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵: Scaling analyses beyond one-off scripts. 𝗪𝗵𝗲𝗿𝗲 𝗶𝘁 𝘀𝗵𝗶𝗻𝗲𝘀: Snakemake, Nextflow, WDL/Cromwell for automating complex pipelines. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Makes your work reproducible, portable, and production-ready - a must in regulated environments (FDA, CLIA). 𝗣𝗿𝗼 𝘁𝗶𝗽: If you ever rerun a 4-hour pipeline just because you changed 1 parameter, it’s time to learn one of these. ⸻ ✅ 𝗗𝗼𝗺𝗮𝗶𝗻-𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗧𝗼𝗼𝗹𝗰𝗵𝗮𝗶𝗻𝘀 𝗚𝗲𝗻𝗼𝗺𝗶𝗰𝘀: GATK, bcftools, VEP, DeepVariant 𝗣𝗿𝗼𝘁𝗲𝗼𝗺𝗶𝗰𝘀: MaxQuant, OpenMS 𝗠𝗲𝘁𝗮𝗴𝗲𝗻𝗼𝗺𝗶𝗰𝘀: QIIME2, mothur, DADA2 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝗯𝗶𝗼𝗹𝗼𝗴𝘆: PyMOL, ChimeraX, AlphaFold 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀: Cytoscape, igraph 𝗜𝗺𝗮𝗴𝗶𝗻𝗴: Fiji/ImageJ, CellProfiler ⸻ 𝗛𝗼𝘄 𝘁𝗼 𝗰𝗵𝗼𝗼𝘀𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝘀𝘁𝘂𝗰𝗸:  1. Anchor yourself in one ecosystem (R or Python) for depth.  2. Stay ecosystem-aware - you don’t have to be fluent in all, but know what’s possible.  3. Focus on concepts (data structures, workflows) so you can switch tools easily.  4. Let the problem drive the tool, not your comfort zone. ⸻ Tech stacks will change. Methods will evolve. But the people who can bridge ecosystems will always stay relevant - and in demand.

🎥 From Beamtime to Insight: Real-Time Adaptive Experiments with National Science Data Fabric (NSFD) --- What if scientific experiments could adapt themselves in real time as data is collected? In this video, we present a collaboration between the University of Tennessee, Knoxville, University of Utah, Oak Ridge National Laboratory, National Institute of Standards and Technology (NIST), and Cornell High Energy Synchrotron Source The work demonstrates how the National Science Data Fabric (NSDF) enables a closed-loop workflow connecting: 🔹 Experimental instruments 🔹 Streaming scientific data 🔹 Real-time AI-driven analysis 🔹 Adaptive experiment steering Experimental data from X-ray scattering experiments on wire-arc additive-manufactured materials flow through NSDF to computing resources. There, INTERSECT@ORNL’s Distributed Active Learning (DIAL) builds surrogate models that recommend the next measurement locations while the experiment is still running. The beamline executes those measurements—and the loop continues. This architecture illustrates a reusable pattern for autonomous scientific experiments, enabling researchers to move from beamtime to insight faster, while addressing practical challenges such as latency, metadata, provenance, and operator control. ▶️ Watch the short video to learn more about how NSDF helps connect instruments, data, AI, and compute to accelerate discovery. A big thank you to the outstanding team of collaborators across UTK, Utah, ORNL, NIST, and CHESS partners who made this work possible. Valerio Pascucci Marshall McDonnell Jack Marquez Werner Sun Global Computing Laboratory Scientific Computing and Imaging Institute at the University of Utah National Science Foundation (NSF) #NSDF #AIforScience #AutonomousLabs #ScientificWorkflows #OpenScience #HPC #DataInfrastructure

The Data Science Bootstrap Notes just got a major upgrade. 8 years, countless changes—2025 demands a new approach. Curious what’s changed and why it matters for your workflow? Read on. After 8 years, I realized my original guide was no longer serving the needs of today’s data scientists. I took a hard look at what actually works in production and rebuilt everything from the ground up. I moved from a web of Obsidian notes to a streamlined MkDocs-powered book, focusing on clarity and accessibility. Tools and best practices evolve fast. What was cutting-edge in 2017 is now outdated. Embracing new tools like pixi and uv has made my workflows more reproducible and collaborative. Pixi’s automatic lock files and feature-based environments have replaced my old conda setups, solving environment drift and dependency headaches. The new edition isn’t just a facelift—it’s a complete rewrite for the modern Python data science ecosystem. I’ve replaced conda with pixi for environment management, introduced uv for tool isolation, and automated project scaffolding with pyds-cli. There’s a dedicated chapter on practical AI integration—how to use generative tools for documentation, code review, and learning, while maintaining intellectual rigor. Automation is now at the core: GitHub Actions handle testing, documentation, and deployment for both your projects and the book itself. This means less manual work and more time for real analysis. I distilled my core philosophies—know your stack, automate relentlessly, and organize everything—into actionable practices with modern tools. Outdated advice is gone; what’s left is what works in production today. I’d be honored if you checked out the new edition and shared your thoughts or questions. Your feedback helps me make it even better! Read the full story and access the guide here: https://lnkd.in/evi6WyUd What’s the biggest challenge you’re facing in your data science workflow right now? #datascience #python #automation #ai #opensource

Building AI agents for materials discovery is becoming an arms race for data and compute, but what if we built on shared infrastructure instead, in the name of open science? Modern materials research increasingly relies on models that integrate large public datasets, simulation tools, computational chemistry, and digital workflows. But assembling and maintaining the infrastructure to support that is resource-intensive and difficult for individual organizations to sustain. A more scalable and equitable approach is to develop community-driven, open, shared platforms. That's the principle behind #AURA (Autonomous Universal Research Assistant), developed by Alejandro Strachan et al. at Purdue University. It is built on top of #nanoHUB, a community ecosystem hosting over 340 simulation tools and 1.6 million FAIR-compliant data entries, and acts as a domain-agnostic multi-agent AI system that plans and executes scientific workflows across disciplines. Here's how AURA integrates with nanoHUB and scales its capabilities: 🔹Metadata-driven tool selection: Automatically identify and use appropriate simulation workflows 🔹FAIR data integration: Pulls structured results directly from nanoHUB for model training or decision-making 🔹Multi-step orchestration: Automates workflows requiring multiple simulation tools 🔹Community-driven expansion: Introduces new capabilities as researchers publish standardized tools and datasets to nanoHUB AURA represents a promising step toward building shared research infrastructure for AI-driven materials discovery. Looking ahead, the next evolution could involve integrating remote-accessible, autonomous experimental platforms ("cloud labs"), bringing us closer to fully closed-loop discovery systems grounded in open science. 📄 Autonomous Universal Research Assistant (AURA): Agentic AI meets nanoHUB's FAIR Workflows and Data, ChemRxiv, November 25, 2025 🔗 https://lnkd.in/eK6F76J2

Bioinformatics isn't just about knowing a bunch of tools—it's about learning how to connect them into workflows that solve real problems. To grow in this field, you want to move progressively toward more advanced capabilities, but always start solving problems right away, no matter what level you’re at. Here’s a roadmap to help you make sense of the NGS Data Analysis journey: 1. Basic Pipelines – Get Things Working Start by running simple, well-defined pipelines. These take raw sequencing data (like FASTQ files) and turn it into useful outputs—aligned reads, gene counts, variant calls, etc. At this stage, your goal is clarity: understand each step, see what each tool does, and begin connecting the dots. You’ll also start building the habit of troubleshooting when things go wrong (and they will). 2. Automating Workflows – Work Smarter, Not Harder Once you’re confident about running things manually, it’s time to save time and reduce human error. That’s where automation comes in. You can start simple—with shell scripts or Makefiles—or use dedicated workflow managers like Snakemake, Nextflow But remember: there’s no single way to automate. What you use depends on your current problem, team setup, and comfort level. 3. Reproducibility – Make It Shareable and Reliable When your workflows become more than one-off experiments, you’ll need to ensure others (or your future self) can reproduce the results. This means using: Git for version control Conda, Docker, or Singularity for managing environments Clear documentation Reproducibility isn’t optional—it’s the foundation of trustworthy science. 4. Scaling – If You Need It, Make It Bigger As you move to larger datasets or more complex analyses, scaling becomes important. This might involve running pipelines on high-performance computing clusters (HPCs) or in the cloud. That said, not every job requires this. You may never need to scale, especially if your role focuses on solving small- to medium-sized problems in a local environment. That’s completely valid—bioinformatics isn't one-size-fits-all. 5. Production-Ready Workflows – Make It Bulletproof Eventually, you may need to build reliable, reusable pipelines that others can run without babysitting. These “production-level” workflows need to be well-tested, efficient, and easy to maintain. Think of them as bioinformatics products—clean, robust, and built for repeated use in research or clinical settings. The Big Picture This roadmap gives you direction—but it’s not a rigid ladder. You don’t need to wait until you’ve mastered automation or containers before solving real problems. In fact, you should always be applying what you know to real-world use cases. The secret is to grow in layers: Solve a problem → learn something new → solve a bigger problem → automate → improve Next post: Why parallel learning is the smartest way to master NGS data analysis. #Bioinformatics #NGS #DataScience #LearningInPublic #NGSForBiologists

🚦 From Instrument Automation to Whole-Lab Orchestration For decades, lab automation has focused on improving individual steps—a faster liquid handler, a better robotic arm, a smarter scheduling tool. Those advances have been critical, but we are now seeing something different emerge: orchestration platforms that coordinate entire experimental workflows across people, instruments, robots, and data systems. One recent example is the Artificial orchestration system (arXiv, 2025), which connects devices and researchers in real time. Instead of static scripts, the platform allows adaptive workflows where AI models, scheduling engines, and robotics continuously adjust based on experimental results. The promise is clear: • Faster iteration in drug discovery • More reliable biobanking and sample management at scale • Improved reproducibility and compliance • Scientists free to focus on science, not troubleshooting instruments But challenges remain. True interoperability across vendors is still limited. Regulatory teams will rightly demand transparency in how orchestration software makes decisions. And adoption requires a cultural shift—trusting that orchestration layers can deliver the same confidence we once placed in manual protocols. The opportunity ahead is to move from “automated instruments” to truly self-driving labs. That shift won’t happen overnight, but it’s coming—and it could change how we think about both experimentation and collaboration. Where do you see orchestration fitting into your lab or organization’s future? #LRIG #LabAutomation #LaboratoryAutomation #DrugDiscovery #SampleManagement