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Explore models and methods for spatial transcriptomics, focusing on alignment, integration, and modeling of gene expression variation in spatially resolved data.
Explore insights from the largest genome-wide association study on human height, examining genetic variants, heritability, and the impact of rare variations on this complex trait.
Explore AI applications in healthcare, from electronic health records to medical imaging. Discover how deep learning enhances tumor identification, stroke detection, and depression prediction.
Explore challenges in creating a universal metagenomic diagnostic test for disease detection, focusing on scalability, sensitivity, and application in community surveillance.
Explore Project Logan's ambitious goal of assembling all public sequencing data. Learn about innovative techniques and challenges in large-scale genomic data processing and analysis.
Explore applications of k-mers in computational genomics, focusing on efficient data structures and algorithms for large-scale sequence analysis and comparison.
Explore advanced techniques for controlling false discoveries in genomics data analysis, focusing on null hypothesis formulation and its impact on statistical inference in high-dimensional biological datasets.
Explore evolutionary models for cancer and lineage tracing, focusing on tumor phylogeny inference and CRISPR-Cas9 lineage tracing approaches.
Explore observational causal inference techniques to enhance and audit machine learning models in healthcare, focusing on distribution shifts, safe policy learning, and robust off-policy evaluation.
Explore leveraging medical Twitter data to develop a visual language AI model for pathology, enhancing tumor microenvironment characterization and spatial gene expression analysis.
Explore selective inference techniques for computational genomics, focusing on false discovery rate control, conformal prediction, and data-driven hypothesis weighting in genome-scale testing.
Explore methods for comparing, summarizing, and visualizing clonal trees in tumor evolution, focusing on weighted distance-based approaches and relaxing infinite sites assumptions.
Explore computational methods for inferring cancer metastasis patterns, focusing on parsimonious migration histories and multi-strain infections in tumor evolution.
Explore polygenic methods for testing and refining depression disease models, focusing on phenotype integration, symptom differences, genetic heterogeneity, and epistasis in complex traits.
Explore computationally scalable methods for characterizing genetic architectures across diverse ancestries, focusing on fine-mapping and heritability estimation techniques.
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