Cheng Chen

Uncovering genetic architecture of the heart via genetic association studies of unsupervised deep learning derived endophenotypes

This study uses deep learning to derive image phenotypes from cardiac MRI and performs GWAS, identifying 323 SNPs and 628 genes associated with cardiac traits and diseases.

The computational models of AlphaFold2 and RoseTTAfold carry protein foldability information

This study shows that AlphaFold2 and RoseTTAfold models show structural variations correlated with protein foldability, independent of secondary structure and unrelated to function.

NIMAA: an R/CRAN package to accomplish NomInal data Mining AnAlysis

NIMAA is an R package for nominal data mining that provides tools for graph construction, clustering, and visualization, applied to a biological dataset.

DNN-DTIs: improved drug-target interactions prediction using XGBoost feature selection and deep neural network

DNN-DTIs predicts drug-target interactions using XGBoost feature selection and deep neural networks, achieving high accuracy on multiple datasets.

Prediction of protein-protein interactions based on elastic net and deep forest

This paper introduces a deep forest-based method for predicting protein-protein interactions, achieving high accuracy in yeast and Helicobacter pylori and improving drug discovery.

Prediction of Protein-Protein Interactions Based on L1-Regularized Logistic Regression and Gradient Tree Boosting

This paper introduces a PPI prediction method using gradient tree boosting and L1-regularized logistic regression, achieving high accuracy on multiple organisms.