Zhuoran Xu

Zhuoran Xu

Cornell University

H-index: 10

North America-United States

About Zhuoran Xu

Zhuoran Xu, With an exceptional h-index of 10 and a recent h-index of 10 (since 2020), a distinguished researcher at Cornell University, specializes in the field of Genetics, Biostatistics, Machine Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Semi-Supervised, Attention-Based Deep Learning for Predicting TMPRSS2: ERG Fusion Status in Prostate Cancer Using Whole Slide Images

Single-Cell Omics for Transcriptome CHaracterization (SCOTCH): isoform-level characterization of gene expression through long-read single-cell RNA sequencing

PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants

MultiNEP: a multi-omics network enhancement framework for prioritizing disease genes and metabolites simultaneously

Detection of ERG: TMPRSS2 gene fusion in prostate cancer from histopathology slides using attention-based deep learning

Machine learning can aid in prediction of IDH mutation from H&E-stained histology slides in infiltrating gliomas

Using Attention-based Deep Learning to Predict ERG: TMPRSS2 Fusion Status in Prostate Cancer from Whole Slide Images

Multi-omics biomarkers aid prostate cancer prognostication

Zhuoran Xu Information

University

Position

Weill Cornell Medicine

Citations(all)

508

Citations(since 2020)

507

Cited By

120

hIndex(all)

10

hIndex(since 2020)

10

i10Index(all)

11

i10Index(since 2020)

11

Email

University Profile Page

Google Scholar

Zhuoran Xu Skills & Research Interests

Genetics

Biostatistics

Machine Learning

Top articles of Zhuoran Xu

Semi-Supervised, Attention-Based Deep Learning for Predicting TMPRSS2: ERG Fusion Status in Prostate Cancer Using Whole Slide Images

Molecular Cancer Research

2024/2/14

Single-Cell Omics for Transcriptome CHaracterization (SCOTCH): isoform-level characterization of gene expression through long-read single-cell RNA sequencing

bioRxiv

2024

PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants

Nature Communications

2023/11/28

MultiNEP: a multi-omics network enhancement framework for prioritizing disease genes and metabolites simultaneously

Bioinformatics

2023/6/1

Detection of ERG: TMPRSS2 gene fusion in prostate cancer from histopathology slides using attention-based deep learning

Cancer Research

2023/4/4

Machine learning can aid in prediction of IDH mutation from H&E-stained histology slides in infiltrating gliomas

Scientific Reports

2022/12/31

Using Attention-based Deep Learning to Predict ERG: TMPRSS2 Fusion Status in Prostate Cancer from Whole Slide Images

bioRxiv

2022/11/20

A multi-omics signature for patients’ risk classification in prostate cancer

Cancer Research

2022/6/15

Using attention-based deep multiple instance learning to identify key genetic alterations in prostate cancer from whole slide images

Cancer Research

2022/6/15

Deep learning predicts chromosomal instability from histopathology images

iScience

2021/5/21

Data from: Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning

2021/5/19

Comparing a novel machine learning method to the Friedewald formula and Martin-Hopkins equation for low-density lipoprotein estimation

PLoS One

2020/9/30

Using histopathology images to predict chromosomal instability in breast cancer: a deep learning approach

medRxiv

2020/1/1

Utilizing electronic health data and machine learning for the prediction of 30-day unplanned readmission or all-cause mortality in heart failure

Cardiovascular Digital Health Journal

2020/9/1

Extraction of radiographic findings from unstructured thoracoabdominal computed tomography reports using convolutional neural network based natural language processing

PLoS One

2020/7/30

Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory …

PLoS One

2020/6/25

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning

PloS one

2020/5/6

Identification and quantification of cardiovascular structures from CCTA: an end-to-end, rapid, pixel-wise, deep-learning method

Cardiovascular Imaging

2020/5/1

Automatic segmentation of cardiovascular structures imaged on cardiac computed tomography angiography using deep learning

Journal of the American College of Cardiology

2020/3/24

See List of Professors in Zhuoran Xu University(Cornell University)

Co-Authors

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