Results for “bed”
7 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
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
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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
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
hekivo superpowers-sage-modeling
Content modeling for Sage/Bedrock — classify as CPT ACF fields Blade component Livewire component or Options Page, Poet CPT configuration, ACF Composer fields vs GUI, relational content, static vs dynamic, content architecture decisions, config/poet.php, modeling before building, content classification matrix
13
hekivo superpowers-sage-sage-lando
Sage theme with Acorn (Laravel IoC for WordPress) and Lando — lando start, lando info, lando acorn, Service Providers, View Composers, Blade components, ACF Composer blocks and fields, Poet CPT config/poet.php, AppServiceProvider, ViewServiceProvider, resources/views/, resources/css/app.css @theme Tailwind v4, resources/js/app.js, content/themes/{theme}/, lando yarn dev, lando yarn build, Blade @extends @section @include @component, Bedrock directory structure, lando wp, composer require, lando composer
13 · bundle