Sha Cao

This paper introduces mmFEA, a kinetic-aware framework for estimating metabolic flux changes from transcriptomics and metabolomics data, showing improved accuracy over baseline methods.

FusedFCR identifies marker genes associated with developmental transitions in time-course scRNA-seq data by combining lasso and fusion penalties for interpretable gene selection.

This paper introduces EmitGCL, a deep-learning framework that predicts future metastasis in cancer with high accuracy, identifies biomarkers, and validates therapeutic targets.

MetaKnogic-Alpha transforms biomedical literature into a hyper-relational knowledge base for metabolic reasoning, achieving high mechanistic accuracy through automated synthesis and biochemical grounding.

This paper develops TDMA, a transcriptomics-based model to estimate DNA methylation activity and epigenetic aging from bulk, single-cell, and spatial transcriptomics data.

The paper identifies resistance mechanisms to KRAS-MEK pathway targeting in PDAC by analyzing clinical trial data and multi-omics profiles, showing that KRAS/MYC co-gain correlates with resistance and that co-targeting these genes is synergistic.

This study shows that HuR promotes immune evasion in PDAC by inhibiting T cell infiltration and function, suggesting HuR as a therapeutic target with immunotherapy.

The paper introduces a Bayesian negative binomial regression model with group-structured priors for spatially correlated count data, applied to cancer registry and spatial omics datasets.

The paper develops a machine learning model to predict SARS-CoV-2 mutation trends using historical data, achieving low prediction error and robustness across mutation patterns.

S3R models spatially varying associations in spatial transcriptomics data by estimating location-specific coefficients with structured sparsity and smoothness penalties, showing effectiveness in synthetic and real datasets.

This study identifies population-specific transcriptional changes in skeletal muscle myonuclei during pancreatic cancer cachexia, revealing distinct gene activation patterns rather than uniform upregulation of cachexia mediators.

The paper evaluates deconvolution methods for inferring immune infiltration from bulk gene expression using a DREAM Challenge, finding that some community-contributed methods, especially deep learning-based ones, perform well.

This study uses TCGA transcriptomics data to analyze metabolic shifts in 11 cancer subtypes and their adjacent normal tissues, revealing patterns in glucose uptake, glycolysis, TCA cycle, and glutaminolysis.

scFLUX predicts single-cell metabolic fluxome and variations using transcriptomics data, offering a web server and local tools for individual cell or sample-level analysis.

This study shows HSF1 suppresses CD8+ T cell infiltration in breast tumors by reducing CCL5 expression, thereby preventing immune-mediated tumor destruction.

This paper introduces scFEA, a graph neural network model to estimate cell-wise metabolic flux from scRNA-seq data, validated with matched metabolomics data and applied to multiple datasets.

SSMD improves cell type identification and deconvolution in mouse transcriptomics by addressing variations in genetics, experimental platforms, and small sample sizes.

RobMixReg provides an R package for robust, flexible, and high-dimensional mixture regression to address outliers and complex regression forms.

ICTD improves tissue microenvironment analysis by enhancing cell type identification, marker gene discovery, and functional inference through a semi-supervised deconvolution approach.

The paper introduces LTMG, a model for capturing transcriptional regulatory heterogeneities in scRNA-seq data, showing improved fitting and validation on multiple datasets.

This paper introduces a bi-clustering framework to characterize colorectal cancer subtypes and identify prognostic markers and drug resistance mechanisms beyond CMS classification.

QUBIC2 improves biclustering for bulk and single-cell RNA-Seq by addressing sparsity and transcriptional regulation, outperforming existing tools in functional module detection and cell type classification.

The paper investigates the role of extracellular matrix (ECM) and immune components in Oral Squamous Cell Carcinoma (OSCC), finding that ECM upregulation and immune cell infiltration are key factors in tumor invasion.

The paper introduces a probabilistic bi-clustering method for single-cell transcriptomic data to detect sub-populations with shared or distinct expression patterns.