Benjamin J. Raphael

This study shows that caloric restriction before tumor induction delays cancer cachexia by reducing systemic inflammation and preserving lean mass.

The paper identifies altered tryptophan metabolism in cancer cachexia, showing serotonin depletion and kynurenine accumulation, which drive behavioral dysfunction but not wasting.

This paper introduces MGW, a metric-learning framework for aligning spatial multi-omics data across different modalities, showing improved alignment over existing methods.

LAML-Pro jointly infers cell genotypes and lineage trees from lineage tracing data, improving accuracy by correcting genotype errors and producing more spatially coherent trees.

This paper introduces a polyhedral framework for ancestral reconstruction in phylogenetic trees, enabling efficient algorithms with additional constraints and improved performance on real datasets.

MOSAICField aligns spatial slices across modalities to reconstruct 3D tissue models and map morphology, outperforming existing methods on simulated and prostate cancer data.

POTTR identifies recurrent mutation trajectories in cancer and development using posets, resolving uncertainty in phylogenies and discovering significant new patterns.

This study uses single-nucleus DNA sequencing to analyze pancreatic cancer evolution, revealing patterns of mutation and copy number variation across clinical scenarios.

The paper introduces TROUPE, a framework to infer cell differentiation dynamics from lineage trees, allowing for unobserved progenitor types and providing better rate estimates than existing methods.

The paper introduces Carta, an algorithm that infers cell differentiation maps from single-cell lineage tracing data, showing improved accuracy over existing methods in simulated and real data.

This paper introduces SIID, an algorithm that integrates spatial transcriptomics data from different platforms to impute missing gene expression and deconvolve mixed cell-type signals.

The paper presents a spatially resolved transcriptomic and gene regulatory atlas of primate subcortical regions, revealing spatially tuned transcriptional and regulatory programs across cell types.

This study maps spatial metabolic gradients in murine liver and intestine using integrated experimental-computational methods, revealing distinct metabolic patterns and fructose-induced metabolic derangements.

This paper introduces an algorithm to reconstruct longitudinal tumor phylogenies from single-cell sequencing data and demonstrates its ability to detect rare clones persisting through treatment.

The paper identifies genomic alterations in the YAP/TAZ pathway associated with stem cell-like castration-resistant prostate cancer using transcriptomic and whole-genome sequencing data.

The paper introduces fastppm, a fast tool for solving the regression problem in tumor phylogeny reconstruction, achieving significant speedups and accuracy improvements.

GASTON-Mix identifies spatial domains and gradients in SRT data more accurately than existing methods, revealing biological insights in brain and tumor tissues.

DeST-OT aligns spatiotemporal transcriptomics data from developmental timepoints using optimal transport to model cell growth, death, and differentiation.

This paper introduces GASTON, an interpretable deep learning method to map spatial gene expression topography, identifying gradients and domains in spatial transcriptomics data.

This paper introduces HM-OT to infer cell type differentiation maps from developmental transcriptomics time series by simultaneously clustering cells and learning transitions between cell types.

This paper introduces fastBE, a method for phylogenetic reconstruction from bulk DNA sequencing data that improves accuracy and scalability over existing approaches.

CalicoST infers allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics data, achieving higher accuracy than existing methods.

The paper identifies issues with the chimeric formula for quantifying epistasis, showing it can lead to incorrect biological interpretations in higher-order interactions.

DIALECT improves the identification of mutual exclusivity and co-occurrence between driver mutations by distinguishing them from passenger mutations in cancer genomics data.

This paper introduces LAML, a method for inferring time-scaled cell lineage trees from CRISPR-based lineage tracing data, showing improved accuracy over existing methods.

Startle is a method for inferring CRISPR-Cas9 lineage trees using a star homoplasy model, showing improved accuracy on simulated and real datasets.

ConDoR improves tumor phylogeny inference by integrating copy-number constraints with SNV data, outperforming existing methods on simulated data and producing more plausible phylogenies in real datasets.

The paper introduces a zero-agnostic copy number transformation model and develops an algorithm for inferring copy number phylogenies, showing improved performance on simulated and real data.

PASTE2 aligns and integrates multi-slice spatial transcriptomics data with partial overlap, improving alignment accuracy and enabling 3D gene expression reconstruction.

Belayer models both continuous and discrete spatial variation in gene expression from spatially resolved transcriptomics data, accurately identifying tissue layers and spatially varying genes.