39 citations · 115 across the 12 of their papers we have counts for
12 papers
From Cancer Drivers to Cancer Keepers: Paradigm Shift and Clinical Implications
Xizhe Zhang, Weixiong Zhang
Cancer research has traditionally focused on identifying driver genes, those with mutations that initiate tumorigenesis. The Cancer Driver Gene (CDG) paradigm, further supported by…
NetMoST: A network-based machine learning approach for subtyping schizophrenia using polygenic SNP allele biomarkers
Xinru Wei, Shuai Dong, Zhao Su +7
Subtyping neuropsychiatric disorders like schizophrenia is essential for improving the diagnosis and treatment of complex diseases. Subtyping schizophrenia is challenging because i…
RAW-GNN: RAndom Walk Aggregation based Graph Neural Network
Di Jin, Rui Wang, Meng Ge +4
Graph-Convolution-based methods have been successfully applied to representation learning on homophily graphs where nodes with the same label or similar attributes tend to connect…
Identification of cancer-keeping genes as therapeutic targets by finding network control hubs
Xizhe Zhang, Chunyu Pan, Xinru Wei +9
Finding cancer driver genes has been a focal theme of cancer research and clinical studies. One of the recent approaches is based on network structural controllability that focuses…
Response to: Significance and stability of deep learning-based identification of subtypes within major psychiatric disorders. Molecular Psychiatry (2022)
Xizhe Zhang, Fei Wang, Weixiong Zhang
Recently, Winter and Hahn [1] commented on our work on identifying subtypes of major psychiatry disorders (MPDs) based on neurobiological features using machine learning [2]. They…
Total controllability analysis discovers explainable drugs for Covid-19 treatment
Xinru Wei, Chunyu Pan, Xizhe Zhang +1
Network medicine has been pursued for Covid-19 drug repurposing. One such approach adopts structural controllability, a theory for controlling a network (the cell). Motivated to pr…
Control hubs of complex networks and a polynomial-time identification algorithm
Xizhe Zhang, Chunyu Pan, Weixiong Zhang
Unveiling the underlying control principles of complex networks is one of the ultimate goals of network science. We introduce a novel concept, control hub, to reveal a cornerstone…
Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning
Cuiying Huo, Dongxiao He, Yawen Li +5
Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. Most existing HGNN-based approaches are supervised or s…
Weaving Attention U-net: A Novel Hybrid CNN and Attention-based Method for Organs-at-risk Segmentation in Head and Neck CT Images
Zhuangzhuang Zhang, Tianyu Zhao, Hiram Gay +2
In radiotherapy planning, manual contouring is labor-intensive and time-consuming. Accurate and robust automated segmentation models improve the efficiency and treatment outcome. W…
Pyramid Medical Transformer for Medical Image Segmentation
Zhuangzhuang Zhang, Weixiong Zhang
Deep neural networks have been a prevailing technique in the field of medical image processing. However, the most popular convolutional neural networks (CNNs) based methods for med…
ARPM-net: A novel CNN-based adversarial method with Markov Random Field enhancement for prostate and organs at risk segmentation in pelvic CT images
Zhuangzhuang Zhang, Tianyu Zhao, Hiram Gay +2
Purpose: The research is to develop a novel CNN-based adversarial deep learning method to improve and expedite the multi-organ semantic segmentation of CT images, and to generate a…
Semi-supervised Semantic Segmentation of Prostate and Organs-at-Risk on 3D Pelvic CT Images
Zhuangzhuang Zhang, Tianyu Zhao, Hiram Gay +2
Automated segmentation can assist radiotherapy treatment planning by saving manual contouring efforts and reducing intra-observer and inter-observer variations. The recent developm…