J. Lagergren

Climate adaptation in P. trichocarpa: key adaptive loci identified for stomata and leaf traits

This study identifies key genetic loci associated with stomatal and leaf traits in Populus trichocarpa that contribute to climate adaptation, showing smaller stomata in genotypes from arid regions.

Divide and conquer: Using RhizoVision Explorer to aggregate data from multiple root scans using image concatenation and statistical methods

The paper introduces two methods to standardize root measurement aggregation across multiple scans using image concatenation and statistical analysis, showing they produce consistent results except for median diameter.

VICTree - a Variational Inference method for Clonal Tree reconstruction

VICTree is a variational inference method for clonal tree reconstruction that uses a Tree-structured Mixture Hidden Markov Model and evaluates on cancer datasets.

Environmental and ecological signals predict periods of nutritional stress for Eastern Australian flying fox populations.

The paper uses explainable AI to predict food shortages in Eastern Australian flying foxes, identifying environmental and bat-level features that predict nutritional stress with high accuracy.

Spatial transcriptomics of T and B cell receptors uncovers lymphocyte clonal dynamics in human tissue.

This paper introduces Spatial VDJ, a spatial transcriptomics method that maps T and B cell receptors in human tissue, revealing clonal dynamics and spatial organization of lymphocytes.

Tumoroscope: a probabilistic model for mapping cancer clones in tumor tissues

Tumoroscope maps cancer clones in tumor tissues by integrating spatial transcriptomics, whole exome sequencing, and pathological images to reveal spatial patterns and clone-specific gene expression.

Tree of mutually exclusive oncogenes

This paper introduces ToMExO, a probabilistic method to infer cancer driver genes and their interactions using cross-sectional tumor data, demonstrating its performance on synthetic and biological datasets.

Inferring Tumor Progression in Large Datasets

This paper introduces a probabilistic model for inferring tumor progression and demonstrates its superior performance compared to an ILP-based method on synthetic and biological datasets.

Patient-specific detection of cancer genes reveals recurrently perturbed processes in esophageal adenocarcinoma

This study identifies patient-specific cancer genes in esophageal adenocarcinoma using machine learning, revealing recurrent process perturbations that suggest therapeutic targets.