Luis Kress

Computing tumor specificity of cancer antigen targets by k-mer indexing of healthy tissue transcriptomes

Johannes Hausmann, Franziska Lang, Özlem Muslu, Luis Kress et autres

Abstract Individualized cancer immunotherapies rely on tumor-specific T-cell antigens, often predicted from somatic mutations as neoantigens. For tumors with low mutational burden, mRNA transcript variants, including gene fusions and novel splice junctions, can serve as important alternative targets. A main challenge in …

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VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data

Özlem Muslu, Thomas Bukur, Pablo Riesgo-Ferreiro, Sameesh Kher et autres

BACKGROUND: Accurately identifying somatic variants from genomic sequencing is crucial for understanding and treating cancer. Previously, methods based on statistics and heuristics, as well as methods based on machine learning were proposed for somatic single nucleotide variant (SNV) calling from matched tumor-normal …

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Additional file 2 of VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data

Özlem Muslu, Thomas Bukur, Pablo Riesgo-Ferreiro, Sameesh Kher et autres

Additional file 2. Supplementary Figures (Fig. S1-S16).

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VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data

Özlem Muslu, Thomas Bukur, Pablo Riesgo-Ferreiro, Sameesh Kher et autres

Abstract Background Accurately identifying somatic variants from genomic sequencing is crucial for understanding and treating cancer. Previously, methods based on statistics and heuristics, as well as methods based on machine learning were proposed for somatic single nucleotide variant (SNV) calling from matched …

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Additional file 1 of VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data

Özlem Muslu, Thomas Bukur, Pablo Riesgo-Ferreiro, Sameesh Kher et autres

Additional file 1. Supplementary Tables (Table S1-S13).

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Additional file 1 of VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data

Özlem Muslu, Thomas Bukur, Pablo Riesgo-Ferreiro, Sameesh Kher et autres

Additional file 1. Supplementary Tables (Table S1-S13).

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VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data

Özlem Muslu, Thomas Bukur, Pablo Riesgo-Ferreiro, Sameesh Kher et autres

Abstract Background Accurately identifying somatic variants from genomic sequencing is crucial for understanding and treating cancer. Previously, methods based on statistics and heuristics, as well as methods based on machine learning were proposed for somatic single nucleotide variant (SNV) calling from matched …

de (code pays fourni par la source)

Additional file 2 of VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data

Özlem Muslu, Thomas Bukur, Pablo Riesgo-Ferreiro, Sameesh Kher et autres

Additional file 2. Supplementary Figures (Fig. S1-S16).

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TRON-Bioinformatics/VariantMedium: v1.2.1

Özlem Muslu, Sameesh Kher, Luis Kress, Shaya Akbarinejad et autres

VariantMedium is a deep learning-based somatic variant caller for matched tumor-normal short-read sequencing data. It integrates machine learning–based filtering and 3D convolutional neural networks to classify candidate sites as somatic, germline, or non-variant, with high sensitivity and robustness across diverse genomic contexts …

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TRON-Bioinformatics/VariantMedium: v1.2.1

Özlem Muslu, Sameesh Kher, Luis Kress, Shaya Akbarinejad et autres

VariantMedium is a deep learning-based somatic variant caller for matched tumor-normal short-read sequencing data. It integrates machine learning–based filtering and 3D convolutional neural networks to classify candidate sites as somatic, germline, or non-variant, with high sensitivity and robustness across diverse genomic contexts …

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Snakemake Hackathon 2026

Thomas Mulvaney, Ben Carillo, David Koppstein, Manar Abdalazem et autres

Reproducible, scalable, and portable data-analysis pipelines are now a fundamental prerequisite for modern data analysis research across all research domains. Snakemake has emerged as one of the most widely adopted systems for declarative pipeline orchestration, combining a concise Python-based DSL with native …

Snakemake Hackathon 2026

Manar Abdalazem, Felix Bartusch, Ed Bennett, Oliver But et autres

Reproducible, scalable, and portable data-analysis pipelines are now a fundamental prerequisite for modern data analysis research across all research domains. Snakemake has emerged as one of the most widely adopted systems for declarative pipeline orchestration, combining a concise Python-based DSL with native …