World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
66
Citations
19853
World Ranking
1387
National Ranking
459

Biology and Biochemistry

D-Index
74
Citations
23149
World Ranking
5521
National Ranking
2624

Overview

Feixiong Cheng is affiliated with Case Western Reserve University in the United States and has contributed extensively to the fields of biochemistry, genetics, and molecular biology as well as medicine. Their research portfolio includes a substantial number of publications, with a focus on molecular biology, computational theory and mathematics, neurology, genetics, and cardiology and cardiovascular medicine.

Their scientific work addresses a range of topics, including:

  • Bioinformatics and genomic networks
  • Computational drug discovery methods
  • Neuroinflammation and neurodegeneration mechanisms
  • Alzheimer's disease research and treatments
  • Immune cells in cancer
  • Single-cell and spatial transcriptomics
  • SARS-CoV-2 and COVID-19 research

Feixiong Cheng has collaborated frequently with coauthors such as Yadi Zhou, Andrew A. Pieper, Yuan Hou, Jeffrey L. Cummings, and Jielin Xu.

Their recent impactful publications include:

  • "Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2" (2020), published in Cell Discovery
  • "Artificial intelligence in COVID-19 drug repurposing" (2020), published in The Lancet Digital Health
  • "New insights into genetic susceptibility of COVID-19: an ACE2 and TMPRSS2 polymorphism analysis" (2020), published in BMC Medicine
  • "Alzheimer's disease drug development pipeline: 2023" (2023), published in Alzheimer's & Dementia Translational Research & Clinical Interventions
  • "Target identification among known drugs by deep learning from heterogeneous networks" (2020), published in Chemical Science

The primary venues for their publications have included bioRxiv (Cold Spring Harbor Laboratory), Alzheimer's & Dementia, Circulation, SSRN Electronic Journal, and Alzheimer's & Dementia Translational Research & Clinical Interventions.

Best Publications

  • The Immune Landscape of Cancer

    Vésteinn Thorsson;David L Gibbs;Scott D Brown;Denise Wolf

  • Oncogenic Signaling Pathways in The Cancer Genome Atlas

    Francisco Sanchez-Vega;Marco Mina;Joshua Armenia;Walid K. Chatila

  • Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer.

    Katherine A. Hoadley;Christina Yau;Christina Yau;Toshinori Hinoue;Denise M. Wolf

  • Comprehensive Characterization of Cancer Driver Genes and Mutations.

    Matthew H Bailey;Collin Tokheim;Eduard Porta-Pardo;Sohini Sengupta

  • admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties.

    Feixiong Cheng;Weihua Li;Yadi Zhou;Jie Shen

  • Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

    Yadi Zhou;Yuan Hou;Jiayu Shen;Yin Huang

  • Genomic and Functional Approaches to Understanding Cancer Aneuploidy

    Alison M. Taylor;Alison M. Taylor;Juliann Shih;Gavin Ha;Gavin Ha;Galen F. Gao

  • Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

    Feixiong Cheng;Chuang Liu;Jing Jiang;Weiqiang Lu

  • Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients.

    André Kahles;Kjong-Van Lehmann;Nora C Toussaint;Matthias Hüser

  • Pathogenic Germline Variants in 10,389 Adult Cancers

    Kuan-Lin Huang;R Jay Mashl;Yige Wu;Deborah I Ritter

  • Network-based prediction of drug combinations.

    Feixiong Cheng;István A. Kovács;István A. Kovács;Albert László Barabási

  • Erratum: Comprehensive Characterization of Cancer Driver Genes and Mutations (ARTICLE (2018) 173(2) (371–385), (S009286741830237X), (10.1016/j.cell.2018.02.060))

    Matthew H. Bailey;Collin Tokheim;Eduard Porta-Pardo;Sohini Sengupta

  • Artificial intelligence in COVID-19 drug repurposing

    Yadi Zhou;Fei Wang;Jian Tang;Ruth Nussinov

  • Artificial intelligence in COVID-19 drug repurposing.

    Yadi Zhou;Fei Wang;Jian Tang;Ruth Nussinov;Ruth Nussinov

  • Network-based approach to prediction and population-based validation of in silico drug repurposing

    Feixiong Cheng;Feixiong Cheng;Rishi J. Desai;Diane E. Handy;Ruisheng Wang

  • deepDR: a network-based deep learning approach to in silico drug repositioning.

    Xiangxiang Zeng;Siyi Zhu;Xiangrong Liu;Yadi Zhou

  • Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

    Franz X. Schaub;Varsha Dhankani;Ashton C. Berger;Mihir Trivedi

  • SoNar, a Highly Responsive NAD+/NADH Sensor, Allows High-Throughput Metabolic Screening of Anti-tumor Agents

    Yuzheng Zhao;Qingxun Hu;Feixiong Cheng;Ni Su

  • New insights into genetic susceptibility of COVID-19: an ACE2 and TMPRSS2 polymorphism analysis.

    Yuan Hou;Junfei Zhao;William Martin;Asha Kallianpur;Asha Kallianpur

  • Estimation of ADME properties with substructure pattern recognition.

    Jie Shen;Feixiong Cheng;You Xu;Weihua Li

  • Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties.

    Feixiong Cheng;Zhongming Zhao

  • Suppression of the SLC7A11/glutathione axis causes synthetic lethality in KRAS-mutant lung adenocarcinoma

    Kewen Hu;Kun Li;Jing Lv;Jie Feng

  • Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers.

    Xinxin Peng;Zhongyuan Chen;Farshad Farshidfar;Xiaoyan Xu

  • Target identification among known drugs by deep learning from heterogeneous networks.

    Xiangxiang Zeng;Siyi Zhu;Weiqiang Lu;Zehui Liu

  • Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework

    Unknown

  • In silico ADMET prediction: recent advances, current challenges and future trends.

    Feixiong Cheng;Weihua Li;Guixia Liu;Yun Tang

  • Deep generative molecular design reshapes drug discovery

    Unknown

  • In silico Prediction of Chemical Ames Mutagenicity

    Congying Xu;Feixiong Cheng;Lei Chen;Zheng Du

  • Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer’s disease

    Unknown

Frequent Co-Authors

Yun Tang
Yun Tang East China University of Science and Technology
Ruth Nussinov
Ruth Nussinov National Institutes of Health
Zhongming Zhao
Zhongming Zhao The University of Texas Health Science Center at Houston
Weihua Li
Weihua Li East China University of Science and Technology
Justin D. Lathia
Justin D. Lathia Cleveland Clinic Lerner College of Medicine
Charis Eng
Charis Eng Cleveland Clinic Lerner College of Medicine
Lara Jehi
Lara Jehi Cleveland Clinic
Mingyao Liu
Mingyao Liu University Health Network
Andrew A. Pieper
Andrew A. Pieper University of Iowa
Joseph Loscalzo
Joseph Loscalzo Harvard Medical School

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