Allen Tannenbaum

Multi-Scale Geometric Network Analysis Identifies Melanoma Immunotherapy Response Gene Modules

This study identifies gene modules associated with melanoma immunotherapy response using dynamic Ollivier-Ricci curvature analysis of gene expression data.

Multi-omic integrated curvature study on pan-cancer genomic data

This paper introduces a network curvature framework to identify cancer subtypes from multi-omic data, showing significant survival differences across cancers.

The Maximum Entropy Principle For Compositional Data

This paper introduces a method based on the maximum entropy principle to model compositional data, demonstrating its effectiveness in capturing inter-species relationships and gene-gene interactions.

Automatic identification of drug-induced liver injury literature using natural language processing and machine learning methods

The paper develops an NLP/ML model to identify DILI-related literature using titles and abstracts, achieving high accuracy across multiple validation sets.

Dynamic Network Curvature Analysis of RNA-Seq Data in Sarcoma

This study uses geometric network analysis of RNA-Seq data to cluster pediatric sarcoma tumors and identify therapeutic targets through unsupervised methods.

Long-term p21 and p53 trends regulate the frequency of mitosis events and cell cycle arrest

The paper analyzes two single-cell datasets to study how p21 and p53 dynamics influence cell cycle arrest and mitosis frequency after radiation exposure.

vWCluster: A Network Based Clustering of Multi-omics Breast Cancer Data Based on Vector-Valued Optimal Transport

The paper introduces vWCluster, a network-based clustering method for multi-omics breast cancer data using vector-valued optimal transport, identifying tumor subtypes with distinct survival rates and immune microenvironment profiles.

Periodicity Scoring of Time Series Encodes Dynamical Behavior of the Tumor Suppressor p53

The paper introduces a periodicity scoring algorithm for p53 time series and shows its correlation with cell division events.

Measuring Robustness of Brain Networks in Autism Spectrum Disorder with Ricci Curvature

This study uses Ricci curvature to measure changes in brain network robustness in ASD patients after stem cell infusion, identifying regions not detected by traditional methods.

Network Curvature as a Hallmark of Brain Structural Connectivity

The paper applies network curvature to brain structural networks to identify robust/fragile regions, track development/aging changes, and detect connectivity differences in Autism Spectrum Disorders.

Network-based clustering for drug sensitivity prediction in cancer cell lines

This study introduces a network-based method combining optimal mass transport and machine learning to predict drug sensitivity in cancer cell lines by clustering genomic and chemical features.

aWCluster: A Novel integrative Network-based Clustering of Multiomics Breast Cancer Data

This paper introduces iWCluster, a network-based multiomics clustering method for breast cancer data, which recovers known subtypes and identifies hypoxia-related genes associated with poor prognosis.

Hierarchical Network Exploration using Gaussian MixtureModels

The paper introduces a framework using optimal mass transport to construct a network reduction hierarchy for interactive exploration and community detection, applied to gene regulatory networks and validated on biological datasets.

Optimal Mass Transport Kinetic Modeling for Head and Neck DCE-MRI: Initial Analysis

This study introduces an optimal mass transport model to analyze contrast agent flow in head and neck cancer patients, showing comparable results to traditional methods.

Control and the Analysis of Cancer Growth Models

The paper analyzes two cancer growth models using system-theoretic approaches to evaluate optimal radiotherapy strategies and tumor heterogeneity considerations.

A Quantitative Analysis of Localized Robustness of MYCN in Neuroblastoma

This paper uses network curvature to show that MYCN amplification in neuroblastoma correlates with higher biological robustness, suggesting new indirect targeting strategies.