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Pavel-Kravchenko
GitHub 제작자 프로필

Pavel-Kravchenko

1개 GitHub 저장소에서 수집된 213개 skills를 저장소 단위로 보여줍니다.

수집된 skills
213
저장소
1
업데이트
2026-07-03
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skills가 있는 위치

수집된 skill 수가 많은 주요 저장소와 이 제작자 카탈로그 내 비중, 직업 분포를 보여줍니다.

저장소 탐색

저장소와 대표 skills

advanced-string-structures
소프트웨어 개발자

Build tries, Aho-Corasick, and suffix arrays with Kasai LCP to index DNA/text and match many patterns in one pass. Use for genome motif scanning, k-mer indexing, longest-repeat search, or BWA/FM-index groundwork.

2026-07-03
ai-science-alphafold-protein-design
소프트웨어 개발자

Interpret AlphaFold2/AF3 pLDDT/PAE scores, fetch AlphaFold DB models by UniProt ID, and rank RFdiffusion/ProteinMPNN designs. Use when asked about pLDDT, PAE, AF2 vs AF3, AlphaFold DB fetch, or design triage.

2026-07-03
ai-science-diffusion-generative-models
소프트웨어 개발자

Code DDPM/DDIM diffusion samplers, linear/cosine noise schedules, and DDRM inverse-problem solving (denoising, inpainting, super-resolution) in NumPy/PyTorch. Use for forward/reverse diffusion, score matching, or DDIM sampling.

2026-07-03
ai-science-enformer-regulatory
소프트웨어 개발자

Predict CAGE/DNase/ATAC/ChIP-seq tracks from raw DNA with Enformer/Borzoi, run in-silico mutagenesis (ISM), and score noncoding variant effects. Use when predicting enhancer/promoter activity from sequence, running ISM, scoring a noncoding SNP, or prioritizing GWAS/eQTL variants.

2026-07-03
ai-science-epigenomic-sequence-models
소프트웨어 개발자

Choose Borzoi (RNA-seq coverage, 32bp) vs Epiformer (sequence+PhyloP, chromatin accessibility) vs AlphaGenome for epigenomic prediction. Use when picking a model for RNA-seq, ATAC/DNase, or variant-effect scoring.

2026-07-03
ai-science-esm2-embeddings
소프트웨어 개발자

Generate ESM2 protein embeddings (fair-esm/transformers) and predict structure with ESMFold. Use when embedding sequences, scoring mutations zero-shot, annotating protein function, or doing fast MSA-free structure prediction.

2026-07-03
ai-science-geneformer-scgpt
소프트웨어 개발자

Tokenize scRNA-seq via Geneformer gene-rank or scGPT expression-bin encoding; annotate cell types, simulate in-silico knockouts. Use for foundation-model cell annotation, Geneformer/scGPT tokenization, or perturbation prediction.

2026-07-03
ai-science-genomic-llms
소프트웨어 개발자

Embed DNA with genomic foundation models (Nucleotide Transformer, HyenaDNA, Evo) via HuggingFace transformers; k-mer tokenize, probe promoter motifs. Use for DNA LLMs, genomic embeddings, or NT/HyenaDNA/Evo choice.

2026-07-03
이 저장소에서 수집된 skills 213개 중 상위 8개를 표시합니다.
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