Kjong-Van Lehmann

This paper introduces a method to predict single-cell perturbation responses using neural optimal transport, showing improved performance over existing methods on drug-treated cells.

This study uses attention-based multiple instance learning to predict HRD status from H&E images across ten tumor types, achieving moderate to high AUROC values.

SECEDO detects subclones in tumors using SNVs from ultra-low coverage single-cell DNA sequencing, achieving high accuracy in synthetic and real datasets.

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.

The paper identifies hundreds of genes deregulated by SVs in cancer, showing increased expression linked to SV breakpoints and enhancer elements.

ActivePathways integrates multivariate omics data to identify enriched pathways, revealing cancer driver genes and prognostic pathways in breast cancer.

This study identifies alternative promoters as key regulators in the cancer transcriptome using RNA-Seq data from 1,188 cancer samples.

This paper introduces a Bayesian nonparametric method to find genetic-clinical associations across cancer types, showing it recovers known associations and outperforms classical methods.

The paper analyzes RNA alterations across 27 cancer types using whole-genome sequencing data, identifying gene expression associations with DNA variants and structural fusions.

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.

This study analyzes whole genome and RNA-Seq data from 1,188 cancer patients to map the effects of somatic and germline genetic variation on gene expression in cancer.

The paper compares transcriptome variations between Uterine Carcinosarcoma, Sarcomas, and Endometrial Carcinoma to identify tumor-specific splicing and expression patterns and genetic determinants of expression differences.

The paper analyzes RNA splicing patterns in kidney renal clear cell carcinoma patients to identify tumor-specific isoforms and splicing quantitative trait loci associated with disease.