Shidan Wang

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.

STIE: Single-cell level deconvolution, convolution, and clustering in spatial transcriptomics by aligning spot level transcriptome to nuclear morphology

Reconstructing Spatial Transcriptomics at the Single-cell Resolution with BayesDeep

This paper introduces BayesDeep, a Bayesian model that reconstructs spatial transcriptomics data at single-cell resolution using histology images and SRT datasets.

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.

Integrating Image and Molecular Profiles for Spatial Transcriptomics Analysis

The paper introduces iIMPACT, a method that integrates histology images and molecular data to improve clustering and domain detection in spatial transcriptomics.

A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

The paper presents a pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis of tumor regions and xenograft-transplant lymphoproliferative disorder prediction.

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.

De-noising Spatial Transcriptomics Data Based on Position and Image Information

This paper develops Sprod, a method to denoise spatial transcriptomics data using location and image information, improving downstream analyses.

Tumor Neoantigenicity Assessment with CSiN Score Incorporates Clonality and Immunogenicity to Predict Immunotherapy Outcomes

The paper introduces the CSiN score to predict immunotherapy outcomes by assessing tumor neoantigen quality through clonality and immunogenicity, showing improved performance over existing methods.

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.