Tae Hyun Hwang

Generative 3D Spatial Reconstruction of Cellular Organization from Single Cell Expression

ORBIS reconstructs 3D tissue geometry from single-cell gene expression without spatial references, achieving lower shape error and faster sampling than existing methods.

Tumor context determines ARID1A effects on gastric cancer immunity

This study shows that ARID1A loss in gastric cancer affects immune evasion depending on tumor context, with different outcomes in mouse models and human subtypes.

UniST: A Unified Computational Framework for 3D Spatial Transcriptomics Reconstruction

UniST reconstructs dense 3D spatial transcriptomics data from sparse serial sections, restoring structural continuity and biologically meaningful expression patterns in multiple tissue contexts.

Multiscale confidence quantification for virtual spatial transcriptomics with UTOPIA

UTOPIA quantifies confidence in virtual spatial transcriptomics predictions across scales, improving reliability and preventing false conclusions.

Pixel2Gene enables histology-guided reconstruction and prediction of spatial gene expression

Pixel2Gene improves spatial gene expression reconstruction and prediction by integrating histology images with spatial transcriptomics data.

Reconstructing multi-scale tissue spatial architecture from single-cell RNA-seq with REMAP

REMAP reconstructs spatial tissue architecture from scRNA-seq using deep learning and ST references, revealing microenvironmental features and cancer subtypes.

HistoSweep enables cellular-resolution tissue quality control for gigapixel images in digital pathology and spatial omics

HistoSweep improves tissue quality control in gigapixel images by removing artifacts and preserving biologically relevant structures, enhancing analysis accuracy in digital pathology and spatial omics.

Designing smart spatial omics experiments with S2Omics

S2Omics automatically selects regions of interest from histology images to maximize molecular information content in spatial omics experiments, improving reproducibility and downstream biological discovery.

Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE

iSCALE predicts super-resolution gene expression and annotates tissue architecture in large tissues, revealing features missed by conventional spatial transcriptomics.

Transmembrane Serine Protease TMPRSS11B promotes an acidified tumor microenvironment and immune suppression in lung squamous cell carcinoma

This study shows that TMPRSS11B promotes an acidified and immunosuppressive tumor microenvironment in lung squamous cell carcinoma, suggesting it as a therapeutic target.

Deep Gaussian Process with Uncertainty Estimation for Microsatellite Instability and Immunotherapy Response Prediction Based on Histology

MSI-SEER uses deep Gaussian processes to predict microsatellite instability from histology images, achieving high accuracy in MSI prediction and ICI response prediction.

miR-551a and miR-551b target GLIPR2 and promote tumor growth in High-Risk Head and Neck Cancer by modulating autophagy

Spatial heterogeneity and organization of tumor mutation burden and immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer

The paper uses whole slide images to analyze spatial heterogeneity of tumor mutation burden and immune infiltrates in bladder cancer, finding that low spatial heterogeneity with high TILs correlates with improved survival.

Spatial analysis of tumor infiltrating lymphocytes based on deep learning using histopathology image to predict progression-free survival in colorectal cancer

This paper uses deep learning to analyze histopathology images of colorectal cancer to predict progression-free survival by quantifying tumor infiltrating lymphocytes.

Bayesian semi-nonnegative matrix tri-factorization to identify pathways associated with cancer phenotypes

This paper introduces a Bayesian semi-nonnegative matrix tri-factorization method to identify cancer-associated pathways from real-valued gene expression data, showing improved performance over existing methods.

CRISPR/Cas9-based mutagenesis frequently provokes on-target mRNA misregulation

This study shows that CRISPR/Cas9-induced INDELs can lead to mRNA misregulation by creating foreign mRNAs or proteins in about half of edited cell lines.

Fraction and Spatial Organization of Tumor-Infiltrating Lymphocytes Correlating with Bladder Cancer Patient Survival

This paper develops a CNN-based method to analyze TILs in bladder cancer WSIs, showing their fraction and spatial organization correlate with patient survival.

STATISTICAL LOCAL BINARY PATTERNS (SLBP): APPLICATION TO PROSTATE CANCER GLEASON SCORE PREDICTION FROM WHOLE SLIDE PATHOLOGY IMAGES

This paper introduces a texture descriptor called SLBP for prostate cancer Gleason score prediction from whole slide images, achieving over 80% accuracy in distinguishing grades ≤7 vs ≥8.

Gaussian Process Based Heteroscedastic Noise Modeling for Tumor Mutation Burden Prediction from Whole Slide Images

This paper develops a Gaussian process-based method for TMB prediction from whole slide images, outperforming baseline methods by incorporating heteroscedastic noise modeling.

Automatic Classification of Prostate Cancer Gleason Scores from Digitized Whole Slide Tissue Biopsies

This paper presents an automatic method for Gleason score classification of prostate cancer using CSLBP descriptors and SVM, achieving over 79% accuracy on TCGA data.

Molecular Subtypes of Anaplastic Gliomas Identified with Somatic-Mutation and Pathway Based Gene Signature

The paper develops a 39-gene signature to stratify anaplastic gliomas into two subgroups with distinct survival outcomes, validated in an external dataset.

miR-16 suppresses growth of rhabdoid tumor cells

This study shows that miR-16 overexpression suppresses growth of rhabdoid tumor cell lines by reducing proliferation and increasing apoptosis.

An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types

This study identifies cancer-type-specific altered pathways linked to survival outcomes across 19 cancer types using integrated somatic mutation and gene expression data.