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03:03:50",{"accountNo":154,"login":155,"displayName":156,"avatarUrl":157,"accountType":107,"accountTypeDesc":108,"profileDescription":158,"skillCount":159,"repoCount":160,"professionCount":5,"scenarioCount":6,"totalStars":161,"updatedAt":162},"01M6AC83DS133TPXFSP1DHNCC6","github","GitHub","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","How people build software.",940,28,238688,"2026-11-14 04:13:45",{"accountNo":164,"login":165,"displayName":166,"avatarUrl":167,"accountType":107,"accountTypeDesc":108,"profileDescription":18,"skillCount":168,"repoCount":169,"professionCount":5,"scenarioCount":6,"totalStars":170,"updatedAt":171},"01M697XGDPXJAVQ8391V2BCYTE","anthropics","Anthropic","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F76263028?v=4",674,17,232203,"2026-11-14 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Deterministic Python script that auto-chains structure prediction, pocket detection, de novo design, docking, scoring, and ADMET filtering into reproducible workflows.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdrug-design\u002FSKILL.md",[211,214],{"categoryNo":212,"categoryName":213,"taxonomyType":11,"taxonomyTypeDesc":12},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":66,"categoryName":67,"taxonomyType":58,"taxonomyTypeDesc":59},[216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236],"design","Drug Design","orchestration","workflows","pipeline","biopython","rdkit","discovery","auto","python script","deterministic","scipy","scoring","structure","docking","end","synthetic","de","prediction","Structure Prediction","Pocket Detection","2026-11-10 10:12:30",{"skillNo":239,"name":240,"nameZh":18,"description":241,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":242,"categories":243,"keywords":244,"publishedAt":237},"01M6XFGKPXMJSCHA0A913ZEBTX","diffdock","Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB\u002FSMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdiffdock\u002FSKILL.md",[],[240,216,245,246,221,222,223,247,248,229,249,230,250,232,251,252,253,254,234,255],"deep learning","pypi","learning","virtual","deep","molecular","virtual screening","pdb","screening","for","Protein",{"skillNo":257,"name":258,"nameZh":18,"description":259,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":260,"categories":261,"keywords":264,"publishedAt":237},"01M6XFGEXH90YD3TSF6YPTPX2Q","denovo-design","De novo molecule generation for drug discovery. Scaffold-based analog enumeration, fragment growing\u002Flinking, structure-based design, multi-objective optimization, and drug-likeness filtering.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdenovo-design\u002FSKILL.md",[262,263],{"categoryNo":212,"categoryName":213,"taxonomyType":11,"taxonomyTypeDesc":12},{"categoryNo":66,"categoryName":67,"taxonomyType":58,"taxonomyTypeDesc":59},[216,265,266,267,268,223,269,229,270,232,271,233,272,273,254,274,275,276,277,278],"denovo design","scaffold","Optimization","enumeration","filtering","generation","drug discovery","multi","fragment","linking","objective","likeness","Structure-Based Design","multi-objective",{"skillNo":280,"name":281,"nameZh":18,"description":282,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":283,"categories":284,"keywords":285,"publishedAt":237},"01M6XFG8538D64STPGJZ1S2NNT","deepchem","Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdeepchem\u002FSKILL.md",[],[281,219,286,287,288,289,290,291,292,223,293,294,250,232,295,296,254,297,298,234],"experiments","neural networks","pytorch","benchmark","graph","networks","pytdc","use","options","quick","you","graph neural networks","when",{"skillNo":300,"name":301,"nameZh":18,"description":302,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":303,"categories":304,"keywords":305,"publishedAt":237},"01M6XFFSBC3KDVA67E6K8SR913","binding-affinity","Hybrid ML + physics binding affinity prediction. Empirical scoring, MM\u002FGBSA rescoring, multi-method consensus, and batch virtual screening for protein-ligand complexes.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fbinding-affinity\u002FSKILL.md",[],[306,307,308,246,221,222,223,248,309,227,228,310,232,251,311,253,254,312,234,255],"binding","Binding Affinity","batch","consensus","physics","mm","hybrid",{"skillNo":314,"name":315,"nameZh":18,"description":316,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":317,"categories":318,"keywords":319,"publishedAt":237},"01M6XFFM7AQ9593CDGGY3WJJFM","admet-reasoning","Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fadmet-reasoning\u002FSKILL.md",[],[320,321,322,323,246,324,222,223,325,326,327,328,232,329,330,331,332,333,334,335],"reasoning","admet reasoning","interpretation","pandas","analysis","park","maps","numpy","structural","drug-discovery","pathways","2025","with","2026","ADMET","toxicity",{"skillNo":337,"name":338,"nameZh":18,"description":339,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":340,"categories":341,"keywords":342,"publishedAt":237},"01M6XFFG529NVVKDQ1CVWWM9YF","admet-prediction","ADMET property prediction for drug candidates. Full pharmacokinetic panel (Caco-2, PPB, clearance, CYP), toxicity (hERG, AMES, DILI), drug-likeness (Lipinski, QED), using RDKit descriptors and TDC models.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fadmet-prediction\u002FSKILL.md",[],[234,343,334,222,344,345,232,346,254,276,335],"admet prediction","models","full","panel",{"skillNo":348,"name":349,"nameZh":18,"description":350,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":351,"categories":352,"keywords":354,"publishedAt":237},"01M6XFEY3B4C2905VGXA8JY607","synthetic-biology","Synthetic biology design and simulation tools. Codon optimization, gene circuit ODE modeling with growth feedback, SBML model creation, bifurcation analysis, barcode sequencing fitness analysis, and therapeutic genome engineering. For metabolic modeling use cobrapy; for sequence tools use biopython.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fsynthetic-biology\u002FSKILL.md",[353],{"categoryNo":212,"categoryName":213,"taxonomyType":11,"taxonomyTypeDesc":12},[355,232,356,216,357,221,358,324,359,360,361,362,363,364,365,366,367,368,369,370,371],"synthetic biology","biology","Tools","cobrapy","simulation","model","feedback","modeling","creation","barcode","sequencing","circuit","sequence tools","gene","sequence","fitness","Bifurcation",{"skillNo":373,"name":374,"nameZh":18,"description":375,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":376,"categories":377,"keywords":378,"publishedAt":237},"01M6XFDCSNZBBXCPMXRQM541C6","pysam","Genomic file toolkit. Read\u002Fwrite SAM\u002FBAM\u002FCRAM alignments, VCF\u002FBCF variants, FASTA\u002FFASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpysam\u002FSKILL.md",[],[374,379,380,381,382,383,384,385,386,387,232,388,389,390,356,391,254,392,393,394],"toolkit","file","data","data processing","data processing pipelines","calculate","read","write","coverage","FASTQ","bam","vcf","processing","variants","sequences","FASTA",{"skillNo":396,"name":397,"nameZh":18,"description":398,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":399,"categories":400,"keywords":405,"publishedAt":237},"01M6XFD4F45GTXGS02YR56MV0K","pylabrobot","Vendor-agnostic lab automation framework. Use when controlling multiple equipment types (Hamilton, Tecan, Opentrons, plate readers, pumps) or needing unified programming across different vendors. Best for complex workflows, multi-vendor setups, simulation. For Opentrons-only protocols with official API, opentrons-integration may be simpler.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpylabrobot\u002FSKILL.md",[401,404],{"categoryNo":402,"categoryName":403,"taxonomyType":11,"taxonomyTypeDesc":12},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":57,"categoryName":60,"taxonomyType":58,"taxonomyTypeDesc":59},[397,406,219,407,408,409,410,359,411,293,412,413,232,356,414,415,416,254,417,298],"automation","official","framework","protocols","api","programming","only","across","pumps","types","lab","plate",{"skillNo":419,"name":420,"nameZh":18,"description":421,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":422,"categories":423,"keywords":425,"publishedAt":237},"01M6XFCWVQTJY6TFYKKYSF2KB5","pyhealth","Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III\u002FIV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpyhealth\u002FSKILL.md",[424],{"categoryNo":402,"categoryName":403,"taxonomyType":11,"taxonomyTypeDesc":12},[420,426,427,245,428,429,381,430,431,432,249,433,434,232,435,356,436,437,298,234,438],"testing","machine learning","tasks","gnn","healthcare","coding","signals","medical","clinical","medical coding","implementing","retain","NDC",{"skillNo":440,"name":441,"nameZh":18,"description":442,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":443,"categories":444,"keywords":445,"publishedAt":237},"01M6XFCA0S2BHME7736QVKZN7H","pydeseq2","Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano\u002FMA plots, for RNA-seq analysis.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpydeseq2\u002FSKILL.md",[],[441,446,324,447,448,449,232,450,451,452,233,356,453,454,455,254,368,456],"python","RNA","gene expression","tests","expression","DESeq2","volcano","rna-seq","bulk","seq","fdr",{"skillNo":458,"name":459,"nameZh":18,"description":460,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":461,"categories":462,"keywords":463,"publishedAt":237},"01M6XFBSY49AC4FFAVY4M6QZ26","pharmacology-wetlab","Computational analysis of pharmacology wet-lab experiments. Western blot densitometry, xenograft tumor growth inhibition, pharmaceutical stability modeling (Arrhenius), radiolabeled antibody biodistribution, MIRD dosimetry, and adverse event grading. For drug databases use chembl-database or fda-database; for molecular docking use diffdock.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpharmacology-wetlab\u002FSKILL.md",[],[464,465,466,286,467,240,324,468,293,469,470,230,471,362,250,472,356,473,416,254],"pharmacology","pharmacology wetlab","stability","database","growth","fda","databases","computational","chembl","event",{"skillNo":475,"name":476,"nameZh":18,"description":477,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":478,"categories":479,"keywords":480,"publishedAt":237},"01M6XFAG7YMR3WQ145T1QETJPJ","neuropixels-analysis","Neuropixels neural recording analysis. Load SpikeGLX\u002FOpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen\u002FIBL curation, AI-assisted visual analysis, for Neuropixels 1.0\u002F2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneuropixels-analysis\u002FSKILL.md",[],[324,481,482,483,381,484,293,485,486,487,488,232,489,356,490,491,492,493,254,298],"neuropixels analysis","spike","metrics","open","recordings","Neural","motion","visual","unit","sorting","curation","recording","load",{"skillNo":495,"name":496,"nameZh":18,"description":497,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":498,"categories":499,"keywords":500,"publishedAt":237},"01M6XFAAHV9P6C935WT0Q6FV9F","neurokit2","Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002FSKILL.md",[],[496,501,502,379,503,324,504,293,505,506,432,507,232,356,391,272,473,254,508,298],"research","assessment","integration","system","complexity","biosignal","brain","signal",{"skillNo":510,"name":511,"nameZh":18,"description":512,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":513,"categories":514,"keywords":517,"publishedAt":237},"01M6XF9VQ2WP34FQGA55RC68GA","molecular-cloning","Molecular cloning simulation and design. PCR amplicon prediction, restriction enzyme digestion, Golden Gate and Gibson assembly simulation, primer design, CRISPR sgRNA design, and plasmid annotation. For protein-level sequence analysis use biopython or esm; for database lookups use gene-database or ensembl-database.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fmolecular-cloning\u002FSKILL.md",[515,516],{"categoryNo":212,"categoryName":213,"taxonomyType":11,"taxonomyTypeDesc":12},{"categoryNo":66,"categoryName":67,"taxonomyType":58,"taxonomyTypeDesc":59},[518,250,519,216,467,520,221,324,359,293,521,522,523,356,524,254,368,369,234,255],"cloning","molecular cloning","PCR","gate","crispr","primer","primer design",{"skillNo":526,"name":527,"nameZh":18,"description":528,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":529,"categories":530,"keywords":531,"publishedAt":237},"01M6XF9MXYQ9MB2A375JFHEY59","microbial-dynamics","Microbial population dynamics modeling and analysis. Bacterial growth curve fitting (logistic, Gompertz, Baranyi), Lotka-Volterra community dynamics, Gillespie stochastic simulation, biofilm quantification, CFU enumeration, and genome annotation. For metabolic modeling use cobrapy; for sequence analysis use biopython.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fmicrobial-dynamics\u002FSKILL.md",[],[532,533,268,221,358,324,359,468,293,534,362,535,536,537,538,356,254,539,369,540],"dynamics","microbial dynamics","community","genome annotation","annotation","quantification","sequence analysis","curve","Curve Fitting",{"skillNo":542,"name":543,"nameZh":18,"description":544,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":545,"categories":546,"keywords":547,"publishedAt":237},"01M6XF8PXSY18GDMV6Q8TYEQFE","immunology-assays","Computational analysis of immunology experimental data. ATAC-seq differential accessibility, immune cell tracking from microscopy, ELISA data processing with 4-parameter logistic fitting, immunohistochemistry quantification, antibody titer analysis, and cell cycle phase duration estimation. For flow cytometry use flow-cytometry-analysis; for scRNA-seq use scanpy.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fimmunology-assays\u002FSKILL.md",[],[548,549,550,551,381,324,552,382,293,553,554,555,471,556,356,391,332,455,254,557],"immunology","immunology assays","accessibility","flow","scanpy","Immune","cycle","estimation","atac","phase",10824,18]