Results for “region2vec”
9 skills
k-dense-ai Geniml
Train unsupervised machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.
30.2k · bundle
levalencia Geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
3 · bundle
More results
jackychenlu Geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
0 · bundle
pwdev-solucoes Shadcn Vue
Scaffold, install, and compose shadcn-vue components built on Reka UI v2, including Vue variants, form patterns with vee-validate/Zod, and adaptation of existing components.
2
pwdev-solucoes Reka UI
Compose custom Vue components with Reka UI v2 headless primitives, covering asChild/as composition, controlled state, portals, and Radix Vue migration.
2
chen-yu-hao Geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
5 · bundle
bankrbot Layer 2s
Navigate the Ethereum L2 landscape: Arbitrum, Optimism, Base, zkSync, Unichain, and Celo. Covers how they work, deployment, bridging, and selection criteria, including 2025–2026 updates.
1.2k · bundle
metinduraktr-44 Geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
0 · bundle
galyarderlabs Vercel React Expert
Resolves legacy references to the vercel-react-expert capability by routing to the current runtime agent, plugin, or narrower skill.
20