Daniel J. Spakowicz

This study shows that NDUFB5 amplification in NSCLC drives tumor hypoxia and therapy resistance by increasing mitochondrial oxygen consumption.

This study investigates how the breast tumor microbiome interacts with CD8+ TILs and metabolites, showing that Staphylococcus influences anti-tumor immunity and T cell-associated metabolites.

The paper identifies microbiome markers in primary colorectal cancer tumors that predict distant metastasis risk using cohort data and molecular techniques.

This study examines epigenetic, microbiome, and immune differences in early-onset colorectal cancer (EOCRC) compared to average-onset CRC, finding accelerated epigenetic aging and stronger immune-microbe correlations in EOCRC.

This study identifies Candida albicans colonization in colorectal tumors as a factor linked to reduced survival and impaired radiation response.

mt.surv is an R package that performs multi-threshold survival analysis to assess the association of continuous predictor variables with time-to-event outcomes, providing a scalar measure for ranking biological variables.

The paper examines microbiome and immune changes in depression during a psychosocial intervention, finding a protective role for Prevotella-dominant microbiomes and reduced inflammation.

This paper introduces MICAH, a graph neural network framework that identifies cancer-associated microbial communities in intratumoral microbiome data across five cancer types.

The paper examines how hypoxia affects the tumor microbiome and its interaction with radiation response in colorectal cancer using RNA-seq data and experimental models.

This study tracks microbiome changes across four body sites in 86 participants over six years, revealing site-specific stability, individualized dynamics, and altered patterns in insulin-resistant individuals.

The paper introduces MEGA, a tool to identify cancer-associated microbes using RNA-seq data from the ORIEN dataset, leveraging graph attention networks and microbial community information.

This paper applies Bayesian structural time series to biomedical sensor data to evaluate interventions, showing its effectiveness in assessing treatment impacts with complex covariates.

The paper identifies gene signatures predictive of asthma severity using a denoising autoencoder and random forest classifier applied to sputum gene expression data.

The paper integrates heterogeneous RNA-seq data from asthmatic patients to uncover gene-microbe interactions and their clinical associations.