Journal Articles
A benchmarking study showing that the two dominant scRNA-seq pipelines, Seurat and Scanpy, produce substantially different results across filtering, dimensionality reduction, clustering, and differential expression - differences as large as those from sequencing under 5% of reads - and that even package version changes can alter outcomes, raising reproducibility concerns.
Rich JM, Moses L, Einarsson PH, Luebbert L, Booeshaghi AS, Antonsson S, et al. (2026). "The impact of package selection and versioning on single-cell RNA-seq analysis." Cell Systems.
A comparative study pitting segmentation foundation models (SAM, SAM2, SAM-Med2D) - prompted with bounding boxes from human experts or from nnU-Netv2 predictions - against a supervised nnU-Netv2 baseline for outlining prostate-cancer bone metastases on CT, finding nnU-Netv2 still segments best but that prompt-guided foundation models are a viable low-data, low-compute alternative.
Pawan SJ, Malewar S, Buren IV, Smith I, Rich J, Prajwal R, et al. (2025). "Analyzing foundation models for segmentation of osseous metastatic lesions in prostate cancer on CT scans." European Journal of Radiology Artificial Intelligence.
A study using a conditional GAN (trained on 333 patients) to synthesize any missing phase of a four-phase kidney CT from the other three, then showing the imputed images preserve clinically relevant features - over 85% agreement on all categorical imaging features plus key radiomic measures - as well as the real images do.
Rich J, Le J, Raad R, Tejura T, Rastegarpour A, Gill I, et al. (2025). "Image imputation with conditional generative adversarial networks captures clinically relevant imaging features on computed tomography." PLOS Digital Health.
A generalizability study that trains nnU-Netv2 to segment kidneys and kidney tumors on CT and cross-tests it between the public KiTS19 dataset and a larger single-institution cohort, showing Dice accuracy drops substantially on external data (e.g., 0.85 to 0.66) and underscoring the need for diverse training data.
Raman AG, Fisher D, Rich JM, Weight C, Heller N, Desai M, et al. (2025). "Evaluation of nnU-Net for kidney tumor segmentation on a large external patient cohort." European Journal of Radiology Artificial Intelligence.
A retrospective study of 479 children who underwent skeletal surveys at one academic hospital, comparing suspected physical-abuse cases before versus during the COVID-19 pandemic; overall abuse incidence stayed roughly the same, but intracranial and retinal hemorrhages dropped significantly during the pandemic, suggesting a shift in injury patterns or reporting.
Li VR, Pickering TA, Imagawa KK, Rich JM, Sura AS. (2025). "An analysis of child abuse detected by skeletal surveys before and during the COVID-19 pandemic." Pediatric Discovery.
A benchmarking study comparing deep-learning classifiers (EfficientNet, ResNet34, DenseNet) and segmentation networks (nnU-Netv2, U-Net, ResUNet, ResAttUNet) for automatically detecting and outlining prostate-cancer bone metastases on CT, finding EfficientNet best for detection and nnU-Netv2 best for segmentation (Dice 0.74) with radiomic features matching manual segmentations.
Pawan SJ*, Rich J*, Malewar S, Patel D, Muellner M, et al. (2025). "Deep learning-based detection and segmentation of osseous metastatic prostate cancer lesions on computed tomography." European Journal of Radiology Artificial Intelligence.
A scoping review of quantitative imaging methods - radiomics, machine learning, and deep learning - for detecting, diagnosing, and monitoring prostate cancer bone metastases, summarizing current approaches and calling for more clinically actionable tools.
Pawan SJ, Rich J, Le J, Yi E, Triche T, Goldkorn A, Duddalwar V. (2024). "Artificial intelligence and radiomics applied to prostate cancer bone metastasis imaging: a review." IRADIOLOGY.
A study of 95 preterm infants comparing radiologists' chest X-ray diagnoses of chronic lung disease of prematurity against several standard clinical definitions, finding over 90% agreement and that radiologists often diagnose the condition, in the more severely affected infants, before the 36-week postmenstrual-age milestone those definitions use.
Rich JM, Lin LJ, Le JL, Abe JRC, Sura A. (2024). "Assessing the agreement of chronic lung disease of prematurity diagnosis between radiologists and clinical criteria." Maternal Health, Neonatology and Perinatology.
A systematic review (PubMed, 2017-2022) cataloging biomarkers in metastatic renal cell carcinoma that predict treatment response, indicate prognosis, or enable therapeutic monitoring across immune-checkpoint, targeted, and VEGF-inhibitor therapies, making the case for biomarker-guided personalized treatment.
Dani KA*, Rich JM*, Kumar SS, Cen H, Duddalwar VA, D'Souza A. (2023). "Comprehensive systematic review of biomarkers in metastatic renal cell carcinoma: predictors, prognostics, and therapeutic monitoring." Cancers.
A case report of a rare multifocal retroperitoneal ganglioneuroma that appeared aggressively infiltrative and fatty on CT yet proved benign on histology after resection, arguing it belongs in the differential for fatty retroperitoneal or mediastinal tumors and distinguishing it from lipomatous ganglioneuroma.
Rich JM, Duddalwar VA, Ter-Oganesyan R, Hu P, Chopra S, Cheng PM, Aron M. (2023). "Localized multifocal retroperitoneal ganglioneuroma with an infiltrative appearance on imaging: a case report." Case Reports in Oncology.
A PRISMA systematic review and meta-analysis of 41 studies on deep-learning segmentation of malignant bone lesions across CT, MRI, and PET/CT, finding that mostly U-Net-based models achieve strong accuracy (median Dice ~0.85-0.9) while dataset homogeneity and clinical generalization remain the key challenges.
Rich JM, Bhardwaj LN, Shah A, Gangal K, Rapaka MS, Oberai AA, et al. (2023). "Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis." Frontiers in Radiology.
A case report of a 35-year-old man with a rare feminizing adrenocortical tumor (high estrogen, low testosterone, and gynecomastia) that recurred locally twice within a few years despite complete low-grade resection with clear margins, highlighting the high recurrence risk and need for close post-surgical surveillance.
Rich JM, Duddalwar V, Cheng PM, Aron M, Daneshmand S. (2023). "Feminizing adrenocortical tumor with multiple recurrences: a case report." Case Reports in Oncology.
A review of how artificial intelligence applied to histopathology images (pathomics) and gene-expression data (genomics) is advancing renal cell carcinoma diagnosis, subtyping, and survival prediction, including identifying subtype-specific gene patterns that could guide targeted therapy.
Knudsen JE, Rich JM, Ma R. (2023). "Artificial intelligence in pathomics and genomics of renal cell carcinoma." Urologic Clinics of North America.
A widely-used negative control formin mutant retains some actin polymerization activity
A biochemistry study finding that a formin mutant commonly relied on as an inactive negative control in actin-assembly experiments in fact retains some residual actin polymerization activity, complicating its use as a true zero-activity baseline.
Rich JM, Bradley AO, Quinlan ME. (2020). "A widely-used negative control formin mutant retains some actin polymerization activity." UCLA MCDB Honors Thesis (Highest Honors).
Preprints
CellSweep, a single-cell genomics decontamination tool that removes free-floating ambient molecules and bulk contamination introduced during library preparation; it works across a wide variety of platforms and outperforms existing decontamination methods across benchmarks.
Caskey M, Rich J*, Weber R, Mortazavi A, Pachter L, Hallgrimsdottir I. (2026). "Single-Cell Genomics Decontamination with CellSweep." bioRxiv.
An R package (wompwomp) for alluvial plots that formalizes the ordering and coloring of alluvia as optimization problems and adapts the NeighborNet algorithm from phylogenetics to produce clearer, less-cluttered layouts.
Rich J, Oakes C, Pachter L. (2025). "Optimizing alluvial plots." arXiv.
varseek, a reference-based variant-detection tool that uses k-mer pseudoalignment to screen reads against an index of known variants, improving detection of indels and low-coverage variants and enabling variant calling at single-cell resolution (demonstrated on tumor-specific COSMIC variants in glioblastoma single-cell data).
Rich JM, Luebbert L, Sullivan DK, Rosa R, Pachter L. (2025). "Reference-based variant detection with varseek." bioRxiv.
SpatialFeatureExperiment, a Bioconductor R/S4 data class that extends SpatialExperiment with the Simple Features geospatial standard to store and run spatial operations on spatial-omics data from platforms like Visium, Xenium, MERFISH, SeqFISH, and Slide-seq within the SingleCellExperiment ecosystem.
Moses L, Huseynov A, Rich JM, Pachter L. (2025). "Geospatially informed representation of spatial genomics data with SpatialFeatureExperiment." bioRxiv.
* denotes co-first author
