Jiajie Peng

VISTA infers spatial gene expression from H&E images, predicting gene expression and clinical outcomes in cancer samples.

This paper introduces Vir3D, a structure-aware deep learning model that predicts influenza antigenicity and improves vaccine strain selection by incorporating 3D structural information from amino acid sequences.

DiffuST denoises spatial transcriptomics data by integrating spatial features and pathology images, improving downstream analysis and biological insights.

FluNexus provides a web platform for antigenic prediction and visualization of influenza A viruses, offering tools for data preprocessing, prediction, and visualization of antigenic evolution.

This paper introduces AAVDiffusion, a viability-guided diffusion model for designing viable AAV capsid proteins, showing improved performance in generating functional sequences.

This paper introduces stMSA, a deep learning model for integrating spatial omics data, improving domain detection, matching, and alignment across tissue slices.

This paper introduces MPOGAN, a generative adversarial network for designing antimicrobial peptides with multiple desired properties, showing improved performance in antimicrobial activity and reduced cytotoxicity.

This study integrates multi-omics data from Drosophila and human Alzheimer's disease models to identify pathways involved in neurodegeneration.

This paper introduces RL-GenRisk, a deep reinforcement learning method for identifying ccRCC risk genes, which outperforms existing methods and identifies ten novel risk genes, including validated EGFR.

DisConST improves spatial domain detection in spatial transcriptomics by using distribution-aware contrastive learning, achieving better accuracy than existing methods.

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.

CellAgent uses an LLM-driven multi-agent framework to automate scRNA-seq and spatial transcriptomics analysis through natural language interactions, achieving 60% efficiency improvement over manual methods.

This paper introduces ALG-DDI, a multi-scale feature fusion model for drug-drug interaction prediction that outperforms existing methods by integrating attribute, local biological, and global semantic information.

The paper introduces a flexibility-rigidity index (FRI) model to quantify chromosome flexibility using Hi-C data, showing improved accuracy and efficiency compared to Gaussian network model (GNM).

This paper introduces DeepPRS, a deep learning-based polygenic risk score model that outperforms existing methods in predicting risk for common diseases using genome-wide genotype data.

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

This paper introduces ScHiC-Rep, a framework for single-cell Hi-C clustering that improves imputation and feature extraction, outperforming existing methods on human and mouse datasets.

The paper proposes DeepMNE-CNN, a method that integrates multiple biological networks using semi-supervised autoencoders and convolutional neural networks to improve gene function prediction.

xQTLImp imputes missing xQTL summary statistics using multivariate Gaussian approximation, rediscovering novel significant eQTL associations in a single-cell dataset.

This paper introduces TEP-Finder, an algorithm to identify emerging phenomena in plant phenotyping data, showing it finds more functionally specific and biologically significant patterns than existing methods.

The paper proposes two methods integrating Gene Ontology with neural networks to improve scRNA-seq clustering, showing better performance than existing approaches.

The paper introduces a neural network framework, MDA-CNN, for predicting miRNA-disease and miRNA-phenotype associations, showing improved performance over existing methods.

This paper introduces FSM, an algorithm for network motif discovery that improves efficiency and shows that large, frequent motifs are better for inferring higher-order biological network structures.