Ruichen Rong

A Generalized Supervised Contrastive Learning Framework for Integrative Multi-omics Prediction Models

This paper introduces a supervised contrastive learning framework for multi-omics prediction models, showing improved accuracy over single-omics approaches in type 2 diabetes and high-fat diet studies.

BayeSMART: Bayesian Clustering of Multi-sample Spatially Resolved Transcriptomics Data

BayeSMART improves multi-sample spatial clustering by integrating AI-reconstructed single-cell data with spatial context, outperforming existing methods in accuracy and efficiency.

ScopeViewer: A Browser-Based Solution for Visualizing Spatial Transcriptomics Data

ScopeViewer provides a browser-based tool for visualizing spatial transcriptomics data with features for interactive exploration, dual viewing, optimized bandwidth, and data security.

Image-based quantification of histological features as a function of spatial location using the Tissue Positioning System

The paper introduces a deep-learning method called TPS to quantify zonal gene expression patterns in tissues like the liver, showing it can detect differences between strains, ages, and disease states.

MB-SupCon: Microbiome-based predictive models via Supervised Contrastive Learning

MB-SupCon integrates microbiome and metabolomics data to improve prediction accuracy in microbiome-only datasets, achieving high accuracy in predicting insulin resistance, sex, and race.

MB-GAN: Microbiome Simulation via Generative Adversarial Network

MB-GAN generates synthetic microbiome data that mimic real datasets, preserving key statistical properties for method development.

HiddenVis: a Hidden State Visualization Toolkit to Visualize and Interpret Deep Learning Models for Time Series Data

HiddenVis visualizes and interprets deep learning models for time series data using accelerometer data from NHANES to identify activity patterns linked to mortality.

Computational staining of pathology images to study tumor microenvironment in lung cancer

The paper develops a deep learning tool to analyze pathology images and identify TME features predictive of survival in lung cancer.

Lesion Attributes Segmentation for Melanoma Detection with Deep Learning

The paper presents two deep learning models for detecting melanoma lesion attributes using dermoscopic images, achieving a Jaccard index of 0.433 on test data.

Skin Lesion Classification Via Combining Deep Learning Features and Clinical Criteria Representations

This paper proposes a two-stage method combining deep learning features and clinical criteria for automated skin lesion classification, achieving promising accuracy on the ISIC 2018 dataset.

Skin Lesion Segmentation with Improved C-UNet Networks

The paper improves the C-UNet network for skin lesion segmentation, achieving an average Jaccard Index of 0.77 on the online validation dataset.