Cen Wan

Improving cell-type identification with generative adversarial networks-enhanced augmentation-free single-cell RNA-Seq contrastive learning

This paper introduces GAN-RCL, a contrastive learning method for cell-type identification in single-cell RNA-Seq that outperforms existing methods.

An extensive evaluation of single-cell RNA-Seq contrastivelearning generative networks for intrinsic cell-typesdistribution estimation

This study evaluates contrastive learning generative networks for estimating cell-type distributions in single-cell RNA-Seq data, finding supervised methods outperform self-supervised ones.

Functional Yeast Promoter Sequence Design Using Autoregressive Generative Models

The paper introduces Gen-DNA-TCN, a method for generating functional yeast promoter sequences using deep learning, and shows it produces diverse and functional sequences with similar transcription factor binding sites.

Evaluation and optimization of sequence-based gene regulatory deep learning models

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.

Improving cell-type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning

This paper introduces a contrastive learning framework with Gaussian noise augmentation for improved cell-type prediction in scRNA-seq data.

Elucidating human ageing-related phenotypic abnormalities with a novel hierarchical feature selection-based knowledge discovery framework

This paper introduces a hierarchical feature selection method, HIP+, to predict human phenotypic abnormalities and identifies key biological process associations.

Improving protein function prediction with synthetic feature samples created by generative adversarial networks

This work introduces FFPred-GAN, a GAN-based method to generate synthetic protein feature samples, improving function prediction accuracy across all three Gene Ontology domains.

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

The paper reports results from the CAFA3 challenge, showing improved computational protein function prediction and new annotations for over 1000 genes through experimental screens in C. albicans, P. aureginosa, and Drosophila.

Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks

STRING2GO uses deep maxout neural networks to improve protein function prediction from interaction networks, outperforming existing methods in accuracy.

Predicting Human Protein Function with Multi-task Deep Neural Networks

This paper introduces a multi-task deep neural network for protein function prediction, showing improved accuracy over baseline methods and single-task predictors.