Jean Yee Hwa Yang

TRACE: a framework for traceable biomedical resource construction demonstrated through single-cell glaucoma data

TRACE provides a framework for creating traceable biomedical resources, demonstrated with a single-cell glaucoma dataset, enabling reproducible analyses and resource reuse.

BenchHub enables an inclusive and transparent ecosystem for community-focused benchmarking in computational biology

BenchHub provides a modular ecosystem for community-focused benchmarking in computational biology by linking datasets, metrics, and ground truth to improve reproducibility and sustainability.

dioscRi enables transferable prediction of clinical outcomes in multi-parameter cytometry data

dioscRi improves transferability of deep learning models for cytometry data by integrating normalization, cell type hierarchies, and clinical outcome prediction.

CCIDeconv: Hierarchical model for deconvolution of subcellular cell-cell interactions in single-cell data

The paper introduces CCIDeconv, a method to deconvolve whole-cell cell-cell interactions into subcellular compartments using scRNA-seq data and spatial transcriptomics datasets.

Generalisable tissue-wide molecular reconstruction from histology

GHIST+ reconstructs tissue-wide molecular maps from H&E histology, enabling cohort-scale molecular reconstruction from sparse spatial transcriptomic data.

AI-Driven Spatial Transcriptomics Unlocks Large-Scale Breast Cancer Biomarker Discovery from Histopathology

Path2Space predicts spatial gene expression and tumor microenvironment from histopathology slides, identifying new breast cancer subgroups and improving biomarker prediction.

Large Language Models for Accessible Reporting of Bioinformatics Analyses in Interdisciplinary Contexts

This study evaluates how large language models generate summaries of bioinformatics analyses, finding they produce readable and safe summaries but often misinterpret visualizations and lack novel insights.

CLUEY enables knowledge-guided clustering and cell type detection from single-cell omics data

CLUEY improves cell type detection and clustering in single-cell omics by integrating biological knowledge to guide clustering and enhance interpretability.

Spatial gene expression at single-cell resolution from histology using deep learning with GHIST

GHIST predicts spatial gene expression at single-cell resolution from histology images, showing superior performance across different resolutions and datasets.

The current landscape and emerging challenges of benchmarking single-cell methods

This paper benchmarks single-cell methods by analyzing 282 papers, highlighting challenges in combining benchmarking studies and preventing fatigue.

Multi-view gene panel characterization for spatially resolved omics

The paper develops a framework to characterize and design gene panels for spatially resolved omics, showing that both computational and manually curated panels have strengths in capturing cell types and reducing redundancy.

A Message Passing Framework for Precise Cell State Identification with scClassify2

scClassify2 is a message-passing neural network for cell state identification, outperforming existing methods in annotating adjacent cell states and applicable to multiple data types.

Endothelial Colony-Forming Cell Transcriptomic Profiling in CT-defined Coronary Artery Disease from the BioHEART-CT Study Implicate CCBE1 in Mitochondrial Dysfunction-associated Atherosclerosis

This study identifies CCBE1 as a gene associated with mitochondrial dysfunction in atherosclerosis using transcriptomic analysis of endothelial cells from CAD patients.

Multi-task benchmarking of spatially resolved gene expression simulation models

The paper benchmarks spatially resolved gene expression simulation models using a framework that evaluates 13 methods across 10 datasets and 35 metrics, highlighting performance differences and factors affecting model accuracy.

eNODAL: an experimentally guided nutriomics data clustering method to unravel complex drug-diet interactions

eNODAL is a clustering method for nutriomics data that integrates experimental design with unsupervised learning to identify interpretable clusters of omics features affected by drug-diet interactions.

SpaNorm: spatially-aware normalisation for spatial transcriptomics data

SpaNorm improves spatial transcriptomics data normalisation by separating spatial library size effects from biological signals, enhancing domain and spatially variable gene identification.

Construction and optimization of multi-platform precision pathways for precision medicine

The paper develops a framework to construct multi-platform precision diagnostic pathways automatically, optimizing for accuracy and cost-effectiveness in clinical settings.

scTIE: data integration and inference of gene regulation using single-cell temporal multimodal data

scTIE integrates temporal multimodal data and infers gene regulatory relationships predictive of cellular state changes, outperforming existing methods in data integration and biological signal preservation.

Benchmarking the translational potential of spatial gene expression prediction from histology

This study benchmarks six methods for predicting spatial gene expression from histology images using breast and skin cancer datasets and TCGA data, evaluating their performance and translational potential.

Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2

scMerge2 integrates large-scale multi-sample multi-condition single-cell data across cohorts, improving disease progression discrimination through cell-type expression signatures.

Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data

BIDCell improves subcellular spatial transcriptomics segmentation by combining self-supervised learning with biologically informed loss functions, outperforming existing methods across multiple tissue types.

Ensemble deep learning of embeddings for clustering multimodal single-cell omics data

SnapCCESS improves clustering of multimodal single-cell omics data by integrating multiple data modalities using an ensemble deep learning framework.

Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data

This study benchmarks analytical combinations for predicting COVID-19 outcomes using scRNA-seq data, finding that ensemble learning and multi-view features improve performance.

Deep Multimodal Graph-Based Network for Survival Prediction from Highly Multiplexed Images and Patient Variables

This paper introduces a deep multimodal graph-based network to predict breast cancer survival by integrating spatial phenotype data from imaging mass cytometry with patient variables.

Scope+: An open source generalizable architecture for single-cell atlases at sample and cell levels

Scope+ provides an open-source architecture for accessing and analyzing large single-cell atlases at the cell level, demonstrated through the Covidscope portal for COVID-19 data.

Characterization of cell-cell communication in COVID-19 patients

The paper characterizes cell-cell communication patterns in COVID-19 patients to identify differences between moderate and severe disease outcomes using single-cell RNA-seq data.

scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction

scFeatures creates interpretable molecular representations of single-cell and spatial data to predict disease outcomes using diverse feature types.

Cross-Platform Omics Prediction procedure enables precision medicine in patients with stage-III melanoma

The paper introduces a method to handle omics data variation across platforms and times, showing its use in three disease risk models for clinical application.

scJoint: transfer learning for data integration of atlas-scale single-cell RNA-seq and ATAC-seq

scJoint integrates atlas-scale scRNA-seq and scATAC-seq data using transfer learning, achieving higher cell type accuracy and joint visualizations than existing methods.

scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model

scREMOTE predicts regulatory gene relationships and builds a computational model for cell reprogramming using multimodal single-cell data, showing improved long-term prediction of transcription factor effects.