Posts with #singlecellanalysis

#singlecellanalysis

Public posts on X that use #singlecellanalysis.

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  1. Bioinfocodes@bioinfocodes
    How did a miniature fish advance vertebrate mapping? Profiling 607,370 cells in Danionella cerebrum created the first adult vertebrate whole-body single-cell and spatial atlas, revealing region-driven neural organization and brain regeneration. 🧠
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  2. Lingjun Li@LingjunLi2
    A fantastic seminar, cool technology and fun conversations! Thank you Tom @gao_lab for visiting us and sharing with us your exciting science! @LiResearch @yingge2121 #SingleCellAnalysis #proteomics #TeamMassSpec
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    Ying Ge @yingge2121
    Thanks Tom @gao_lab for coming to visit us today and giving such a beautiful talk at our @uwsmph Human Proteomics Program Frontiers in Proteomics seminar! The LevCell technology for fully automated #singlecell #proteomics is really cool! 😎👍
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  3. Ying Ge@yingge2121
    Thanks Tom @gao_lab for coming to visit us today and giving such a beautiful talk at our @uwsmph Human Proteomics Program Frontiers in Proteomics seminar! The LevCell technology for fully automated #singlecell #proteomics is really cool! 😎👍
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  4. RNA-Seq Blog@RNASeqBlog
    #bollito performs both basic and advanced tasks in #singlecellanalysis including: quality control, read alignment, dimensionality reduction, clustering, cell-marker detection, differential expression, functional analysis, trajectory... @CNIOStopCancer
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  5. Ningyu Zhang@ZJU✓@zxlzr
    🚀After over a year of hard work, we’re thrilled to share InstructCell alongside our paper: "A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following." #AI #LLM #SingleCellAnalysis #AI4Science #NLP✨ 🧬 InstructCell bridges natural language and gene expression data to enable: ✅ Cell Type Annotation ✅ Conditional Pseudo-Cell Generation ✅ Drug Sensitivity Prediction 📄 Paper: 📂 Code: 📦 Models: 1 Chat version 2 Instruct version Key Contributions of InstructCell: 1️⃣ Multi-modal Instruction Dataset: Constructs a multi-modal instruction dataset integrating gene expression profiles with biological attributes like species, tissue, and sequencing protocols, while simulating diverse traits such as personalities, motivations, and proficiency levels using LLMs. 2️⃣ Advanced Multi-modal Architecture: Develops a multi-modal architecture capable of processing interleaved natural language and gene expression data with components like a Q-Former, a pre-trained language model, and a cell reconstruction module. 3️⃣ Instruction Tuning for Multi-Modal Tasks: Extends LLM capabilities to the cell-language multi-modal space through instruction tuning, unifying understanding and generation tasks in a single framework. 📢 Let’s explore its potential in advancing #AI4Science research. We look forward to your thoughts and feedback! 🌱
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