Zhenjiang Fan

DeepCEF: A Deep Causal Estimation Framework for Complex Biological Systems Integrating Local Scores, Independence Tests, and Relation Attributes

This paper introduces a deep learning framework for causal estimation in complex biological systems, combining local scores, independence tests, and variable attributes to improve accuracy and adaptability.

Unveiling the Gut Microbiome Network Fingerprint: A Deep Learning Approach to Predict Immunotherapy Response in Melanoma Patients.

This study uses DeepMicroNET, a deep learning model, to identify gut bacteria consistently predicting immunotherapy response in melanoma patients across multiple cohorts.

Causal inference using deep-learning variable selection identifies and incorporates direct and indirect causalities in complex biological systems

This paper introduces causalDeepVASE, a deep-learning method for causal inference that identifies both direct and indirect causal relationships in biological systems, outperforming existing methods in simulated and biological data.

Cell-type-specific alternative polyadenylation (APA) genes reveal the function of dynamic APA in complex tissues

The paper introduces scMAPA, a tool to identify cell-type-specific APA genes in scRNA-Seq data, showing improved power and interpretability in human and mouse datasets.

Alternative Polyadenylation Regulates Patient-specific Tumor Growth by Individualizing the MicroRNA Target Site Landscape.

The paper identifies microRNAs whose target sites are modified by global APA events in breast cancer, showing their role in tumor progression and patient-specific heterogeneity.