Feixiong  Cheng,  PhD

Feixiong Cheng, PhD

Assistant Staff

Lerner Research Institute, 9500 Euclid Avenue, Cleveland, Ohio 44195


The primary goal of Dr. Cheng’s lab is to combine tools from network medicine, Artificial Intelligence (AI), genomics, bioinformatics, computational biology, chemical biology, and experimental pharmacology and systems biology assays (e.g., single cell sequencing and patient iPSC-derived models) to address the challenging questions of understanding of various human complex diseases, in particular for Alzheimer's disease, which could have a major impact in identifying novel real-world data-driven diagnostic biomarkers and therapeutic approaches for precision medicine.

Dr. Cheng’s lab at Cleveland Clinic’s Genomic Medicine Institute has several major focus areas:

1. Network Medcine Tools for Precision Medicine Drug Discovery

Traditional drug discovery and development pipelines involve complex, expensive, and time-consuming processes. Many drug candidates with idealin vitroactivities are failed in phases II and III because of low efficacyin vivoor safety problems. One possible reason for this high clinical attrition rate might be the shortcoming if the classical hypothesis of ‘one drug, one gene, one disease’ in the traditional drug discovery paradigm. Supported by NIH grants (R01 and R56), our team have invested a huge amount of efforts for the development of several integrated, network-based methodologies (Nature Aging 2021,Nature Genetics 2021; Nature Communications 2018 and 2019a) for drug repurposing. Those state-of-the-art network medicine tools (https://alzgps.lerner.ccf.org and https://taca.lerner.ccf.org/) present new and important methodologies fordeveloping broadly active therapeutics for various complex diseases (including COVID-19 and Alzheimer's disease)

2. Translation of Multi-Omics Discoveries to Disease Biology and Therapeutic Development

High-throughput omics (including genomics, transcriptomics (single-cell), proteomics, and metabolomics) offer power tools for human disease studies. However, how to translate multi-omics findings to disease pathobiology and therapeutic development remains a great challenge. Supported by NIH/NIA U01, our team are actively developing and applying developed multiple network-based methodologies and AI tools (Genome Research 2021; PLOS Biology 2020; Nature Communications2019b) to illustrate the pathobiology of disease and therapeutic discovery in Alzheimer’s disease.

3. Artificial Intelligence (AI) Methodologies for Target Identification and Therapeutic Discovery

Without foreknowledge of the complete drug target information, development of promising and affordable approaches for effective treatment of human diseases is challenging. Our team has developed multiple network-based, artificial Intelligence methodologies (Lancet Digital Health 2020 [Cover]; Chemical Science 2020 [Cover]), for drug target identification and precision medicine drug discovery, by unique integration of big biomedical data, including genomics, transcriptomics, proteomics, metabolomics, radiomics, interactomics (e.g., protein-protein interactions [https://mutanome.lerner.ccf.org], Genome Biology 2021), and electronic health records (EHRs).

4. Building an Individualized Network Medicine Infrastructure for Precision Cardio-Oncology

The growing awareness of cardiac dysfunction by cancer treatment has led to the emerging field of Cardio-Oncology. However, there are no guidelines in terms of how to prevent and treat the new cardiotoxicity in cancer survivors due to the limited experimental assays. Network medicine – a discipline that seeks to redefine disease and therapeutics from an integrated perspective using systems biology and network science – offers a non-invasive way to identify actionable biomarkers for Cardio-Oncology. Supported by NIH Career Development Award (K99/R00), Dr. Cheng’s lab is working on developing state-of-the-art systems biology and network medicine approaches in Cardio-Oncology that focuses on screening, monitoring and treating cancer survivors with cardiac dysfunction resulting from cancer treatments. The central, unifying hypothesis is that an integrated, network-based, systems biology approach that incorporates not only genetic variations, but also gene expression, metabolomic, proteomic, the human protein-protein interactome, exposomic data, and real-world data (e.g., patient longitudinal data), along with careful deep phenotypic data, will prove to be the most effective way to identify clinical actionable biomarkers and mechanisms responsible for Cardio-Oncology, thereby achieving the goal of coordinated, patient-centered strategies for treatment and long-term heart and vascular care (e.g., heart failure and pulmonary vascular disease) for cancer survivors.

Lay Summary

Cheng Lab is developing and applying systems biology technologies and network medicine methodologies to predict drug targets and to identify mechanisms of disease, thereby approaching the goal of coordinated, patient-centered strategies to innovative diagnostics and therapeutics development.

We have 2-3 postdoc positions and multiple graduate student positions available for Network Medicine and Artificial Intelligence (AI) projects. If you have PhD or MD in the field of systems biology, bioinformatics, machine learning, AI, natural language processing, mathematics, computational biology, and network science, please send your cover letter (describing your interest in and qualifications for this position), curriculum vitae (including publications list), one research statement that outlines both your research achievements and agenda, and your service and outreach activities and plans, and the names and contact information of three letter writers. Please apply before September 30, 2022.

Peer-reviewed Papers

Zhang P, Hou Y, Tu W, Campbell N, Pieper AA, Leverenz JB, Gao S, Cummings J, Cheng F (2022) Population-based Discovery and Mendelian Randomization Analysis Identify Telmisartan as a Candidate Medicine for Alzheimer's Disease in African Americans.  Alzheimer's & Dementia: The Journal of the Alzheimer's Association. in press. DOI: 10.1002/alz.12819.

Fang J, Zhang P, Zhou Y, Chiang WC, Tan J, Hou Y, Stauffer S, Li L, Pieper AA, Cummings J, Cheng F (2021) Endophenotype-based in-silico network medicine discovery combined with insurance records data mining identifies sildenafil as a candidate drug for Alzheimer’s diseaseNature Aging, 1, 1175–1188. (Highlighted by NIH Research News and 50+ major news outlets such as Newsweek, US News, BBC News, Fox News, UK Daily Mail)

Zhou Y, Xu J, Hou Y, Bekris L, Leverenz JB, Pieper AA, Cummings J, Cheng F (2022) The Alzheimer's Cell Atlas (TACA): A single-cell molecular map for translational therapeutics accelerator in Alzheimer's disease. Alzheimer and Dementia, accepted. https://taca.lerner.ccf.org/, doi: 10.1002/trc2.12350

Zeng X, Xiang H, Yu L, Wang J, Li K, Nussinov R, Cheng F (2022) Accurate prediction of molecular targets using a self-supervised image representation learning framework. Nature Machine Intelligence, accepted in principle. doi: 10.21203/rs.3.rs-1477870/v1.

Zeng X, Wang F, Luo Y, Kang S, Tang J, Lightstone F.C., Fang F.E., Cornell W., Nussinov R, Cheng F (2022) Deep Generative Molecular Design Reshapes Drug Discovery, Cell Reports Medicine, accepted in principle.

Lal J.C., Mao C, Zhou Y, Gore-Panter S.R., Rennison H.J., Lovano B.S., Castel L., Shin J., Gillinov M.A., Smith J, Barnard J., Van Wagoner R.D., Luo Y#Cheng F# (Co-corresponding author), Chung K.M. # (2022) Transcriptomics-based Network Medicine Discovery and Population-based Validation Identifies Metformin as a Candidate Drug for Atrial Fibrillation. Cell Reports Medicinein press.

Zhou Y, Liu Y, Gupta S, Paramo M, Hou Y, Mao C, Luo Y, Judd J, Wierbowski S, Bertolotti M, Nerkar M, Jehi L, Drayman N, Nicolaescu V, Gula H, Tay S, Randall G, Lis TJ, Feschotte C, Erzurum CS, Cheng F# (Co-corresponding author), Yu H#. A comprehensive SARS-CoV-2-human protein-protein interactome network identifies pathobiology and host-targeting therapies for COVID-19Nature Biotechnology, 2022, in press.

Hou Y, Zhou Y, Jehi L, Luo Y, Gack UM, Chan T, Yu H, Eng C, Pieper AA, Cheng F (2022) Aging-related cell type-specific pathophysiologic immune responses that exacerbate disease severity in aged COVID-19 patients. Aging Cell, 21(2):e13544. doi: 10.1111/acel.13544.

Fang J, Zhang P, Wang Q, Chiang C, Zhou Y, Hou Y, Xu J, Chen R, Zhang B, Lewis JS, Leverenz B.J., Pieper A.A., Li B,
Li L, Cummings J, Cheng F (2022) Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer’s disease, Alzheimer's Research & Therapy, 14(1):7.

Martin W, Sheynkman G, Lightstone FC, Nussinov R, Cheng F (2021) Interpretable Artificial Intelligence and Exascale Molecular Dynamics Simulations to Reveal Kinetics: Applications to Alzheimer’s Disease, Current Opinion in Structural Biology, 72:103-113.

Xu J, Zhang P, Huang Y, Zhou Y, Hou Y, Bekris L, Lathia JD, Chiang WC, Li L, Pieper AA, Leverenz BJ, Cummings J, Cheng F (2021) Multimodal single-cell/nucleus RNA-sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer’s diseaseGenome Research, 31(10):1900-1912.

Hou Y, Zhou Y, Hussain M, Budd GT, Tang WHW, Abraham J, Xu B, Shah C, Moudgil R, Popovic Z, Watson C, Cho L, Chung M, Kanj M, Kapadia S, Griffin B, Svensson L, Collier P, Cheng F (2021) Cardiac risk stratification in cancer patients: A longitudinal patient-patient network analysisPLOS Medicine18(8): e1003736.

Hou Y, Zhou Y, Gack UM, Lathia DJ, Kallianpur A, Mehra R, Chan T, Jung UJ, Jehi L, Eng C, Cheng F (2021) Multimodal single-cell omics analysis identifies epithelium-immune cell interactions and immune vulnerability associated with sex differences in COVID-19Signal Transduction and Targeted Therapy, 6(1):292.

Zhou Y, Xu J, Hou Y, Leverenz B.J., Kallianpur A, Mehra MR, Liu Y, Yu H, Pieper AA, Jehi, L. & Cheng F (2021) Network medicine links SARS-CoV-2/COVID-19 infection to brain microvascular injury and neuroinflammation in dementia-like cognitive impairmentAlzheimer's Research & Therapy, 13:110.

Zhou Y, Fang J, Bekris L, Young H.K., Pieper AA, Leverenz J, Cummings J, Cheng F (2021) AlzGPS: A Genome-wide Positioning Systems Platform to Catalyze Multi-omics Findings for Alzheimer's Drug Discovery. Alzheimer's Research & Therapy, 13(1):24. AlzGPS website: https://alzgps.lerner.ccf.org

Shin KM, Vázquez-Rosa E, Koh YG, Dhar M, Chaubey K, Cintrón-Pérez JC, Barker S, Miller E, Franke K, Noterman M, Seth D, Allen SR, Motz TC, Rao R, Skelton AL, Pardue TM, Fliesler JS, Wang C, Tracy ET, Gan L, Liebl JD, Savarraj J, Torres LG, Ahnstedt H, McCullough DL, Kitagawa SR, Choi AH, Zhang P, Hou Y, Chiang WC, Li L, Ortiz F, Kilgore AJ, Williams SN, Whitehair CV, Gefen T, Flanagan EM, Stamler SJ, Jain KM, Kraus A, Cheng F, Reynolds DJ, Pieper AA (2021) Reducing tau acetylation is neuroprotective in brain injuryCell184(10):2715-2732.e23.

Zhou Y, Zhao J, Fang J, Martin W, Li L, Nussinov R, Chan TA, Eng C, Cheng F (2021) My Personal Mutanome: A Computational Genomic Medicine Platform for Searching Network Perturbing Alleles Linking Genotype to Phenotype. Genome Biology. 22: 53. Website: https://mutanome.lerner.ccf.org

Cheng FZhao J, Wang Y, Lu W, Liu Z, Zhou Y, Martin W, Wang R, Hao T, Yue H, Ma J, Hou Y, Castrillon AJ, Fang J, Lathia DJ, Keri AR, Lightstone C.F., Antmam ME, Rabadan R, David EH, Eng C, Vidal M, Loscalzo J (2021) Comprehensive characterization of protein-protein interactions perturbed by disease mutationsNature Genetics53(3):342-353.

Nussinov R, Jang H, Nir G, Tsai CJ, Cheng F (2021) A new precision medicine initiative at the dawn of exascale computing. Signal Transduction and Targeted Therapy, 6(1):3.

Fang J, Pieper AA, Lee G, Bekris L, Nussinov R, Leverenz BJ, Cummings J, Cheng F (2020) Harnessing endophenotypes and network medicine for Alzheimer’s drug repurposingMedicinal Research Reviews, 40:2386–2426.

Zhou Y, Wang F, Tang J, Nussinov R, Cheng F (2020) Artificial Intelligence in COVID-19 Drug Repurposing. Lancet Digital Health. 2(12), E667-E676. Cover Paper.
Zhou Y, Hou Y, Shen J, Kallianpur A1, Zein J, Culver AD, Farha S, Comhair S, Fiocchi C, Gack UM, Mehra R, Stappenbeck T, Chan T, Eng C, Jung UJ, Jehi L, Erzurum S, Cheng F (2020) A network medicine approach to prediction and population-based validation of disease manifestations and drug repurposing for COVID-19PLOS Biology, 18(11): e3000970. PMCID: PMC7350981

Zhou Y, Hou Y, Shen J, Huang Y, Martin W, Cheng F (2020) Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2Cell Discovery, 6, 14.

Hou Y, Zhao J, Martin W, Kallianpur A, Chung KM, Jehi L, Sharifi N, Erzurum S, Eng C, Cheng F (2020) New insights into genetic susceptibility of COVID-19: an ACE2 and TMPRSS2 polymorphism analysisBMC Medicine, 18, 216.

Zeng X, Song X, Ma T, Pan X, Zhou Y, Hou Y, Zhang Z, Karypis G, Cheng F (2020) Repurpose open data to discover therapeutics for COVID-19 using deep learning. Journal of Proteome Research, 19(11), 4624–4636 (Cover paper)

Martin W, Cheng F (2020) Repurposing of FDA-approved toremifene to treat COVID-19 by blocking the spike glycoprotein and NSP14 of SARS-CoV-2Journal of Proteome Research, 19(11), 4670–4677. 

Martin W, Cheng F (2021) A rational design of a multi-epitope vaccine against SARS-CoV-2 which accounts for the glycan shield of the Spike glycoprotein. Journal of Biomolecular Structure and Dynamics. in press. 10.1080/07391102.2021.1894986.

Zeng X, Zhu S, Lu W, Liu Z, Huang J, Zhou Y, Fnag J, Huang Y, Guo H, Li L, Trapp B, Nussinov R, Eng C, Loscalzo J, Cheng F (2020) Target identification among known drugs by deep learning from heterogeneous networksChemical Science, 11, 1775-1797. (Journal Cover Paper)

Liu C, Ma Y, Zhao J, Nussinov R, Zhang Y.C., Cheng F (co-corresponding author), Zhang Z (2020) Computational network biology: data, model, and applicationPhysics Reports 846 (3): 1-66.

Liu C, Zhao J, Lu W, Dai Y, Zhou Y, Hockings J, Nussinov R, Eng C, Cheng F (2020) Individualized genetic network analysis reveals new therapeutic vulnerabilities in 6,700 cancer genomesPLoS Computational Biology, 16(2): e1007701.

Zhou Y, Hou Y, Hussain M, Watson C, Moudgil R, Shah C, Abraham J, Budd G.T., Tang W.H.W, Finet J.E., James, K; Estep J.D., Xu B, Hu B, Cremer P, Jellis C, Grimm R.A., Greenberg N, Popovic Z.B., Cho L, Desai M.Y., Nissen S.E., Kapadia S.R., Svensson L.G., Griffin B.P, Collier P, Cheng F (2020) Machine Learning Approaches to Cancer Therapy-related Cardiac Dysfunction Risk Stratification in 4,300 Longitudinal Cancer PatientsJournal of the American Heart Association (JAHA). 9(23):e019628.

Castrillon JA, Eng C, Cheng F (2020) Pharmacogenomics for immunotherapy and immune-related cardiotoxicityHuman Molecular Genetics. 29(R2):R186-R196.

Xu B, Kocyigit D, Griffin PB, Cheng F (2020) Applications of artificial intelligence in multimodality cardiovascular imaging: A state-of-the-art reviewProgress in Cardiovascular Diseases. 63(3):367-376.

Bayik D, Zhou Y, Park C, Hong C, Vail D, Silver JD, Lauko JA, Roversi AG, Watson CD, Lo A, Alban JT, McGraw M, Sorensen DM, Grabowski MM, Otvos B, Vogelbaum AM, Horbinski MC, Kristensen WB, Khalil MA, Hwang HT, Ahluwalia SM, Cheng F, Lathia DJ (2020) Myeloid-derived suppressor cell subsets drive glioblastoma growth in a sex-specific mannerCancer Discovery. 10(8):1210-1225.

Hu K, Li K, Lv J, Feng J, Chen J, Wu H, Cheng F, Jiang W, Wang J, Pei H, Chiao PJ, Cai Z, Chen Y, Liu M, Pang X (2020) Suppression of the SLC7A11/glutathione axis causes synthetic lethality in KRAS-mutant lung adenocarcinomaJournal of Clinical Investigation. 130(4):1752-1766.

Akhavanfard S, Padmanabhan R, Yehia L, Cheng F, Eng C (2020) Comprehensive germline genomic profiles of children, adolescents and young adults with solid tumorsNature Communications, 11: 2206.

Jin S, Zeng X, Lin J, Chan YS, Erzurum CS, Cheng F (2019) A network-based approach to infer microRNA-mediated disease comorbidities and potential pathobiological implications. npj Systems Biology and Applications, 5, 41.

Huang Y, Fang J, Wang Z, Lu W, Wang Q, Hou Y, Jiang X, Reizes O, Lathia J, Nussinov R, Eng C, Cheng F (2019) A systems pharmacology approach uncovers wogonoside as a novel angiogenesis inhibitor of triple-negative breast cancer by targeting Hedgehog signalingCell Chemical Biology. 26: 1143-1158.

Peng H, Zeng X, Zhang D, Nussinov R, Cheng F (2019) A components attribute clustering (COAC) algorithm for single cell RNA sequencing data analysis and potential pathobiological implicationsPLoS Computational Biology, 15(2): e1006772

Cheng F, Liang H, Butte AJ, Eng C, Nussinov R (2019) Personal mutanomes meet modern oncology drug discovery and precision healthPharmacological Reviews71:1-19. (Journal Cover)

Wang Q, Chen R, Cheng F, Wei Q, Ji Y, Yang H, Zhong X, Tao R, Wen Z, Sutcliffe SJ, Liu C, Cook HE, Cox JN, Li B (2019) A Bayesian framework that integrates multi-omics data and gene networks predictes risk genes from Schizophreniz GWAS dataNature Neuroscience. 22(5): 691-699.

Cheng F, Liu C, Lu W, Fang J, Hou Y, Handy ED, Wang R, Zhao Y, Yang Y, Huang J, Hill ED, Vidal M, Eng C, Loscalzo J (2019) A genome-wide positioning systems network algorithm for in silico drug repurposingNature Communications. 10: 3476.

Cheng F, Kovacs I, Barabasi AL (2019) Network-based prediction of drug combinationsNature Communications, 10: 1197.

Cheng F, Desai JR, Handy ED, Wang R, Schneeweiss S, Barabasi AL, Loscalzo J (2018) Network-based approach to prediction and population-based validation of in silicodrug repurposingNature Communications. 9: 2691.

01/13/2022 |  

Investigating Age Differences in COVID-19 Immune Response

Dr. Cheng’s team identified several differences in immune and inflammatory responses that may help explain the elevated risk for severe illness and death observed in older COVID-19 patients.

12/06/2021 |  

Harnessing Endophenotypes for Alzheimer’s Disease Drug Repurposing

Dr. Cheng’s team developed an endophenotype-based drug repurposing methodology that identified the FDA-approved drug sildenafil as a candidate for the prevention and treatment of Alzheimer’s disease.

09/15/2021 |  

NIH Supplement Award for Alzheimer’s Disease Drug Repurposing

With this award, Dr. Cheng’s team will develop artificial intelligence and machine learning tools capable of identifying novel endophenotypes and actionable targets for drug repurposing in Alzheimer’s disease.

08/30/2021 |  

Developing Artificial Intelligence Tools for Alzheimer’s Disease Drug Discovery

With a new $4 million grant, Drs. Cheng, Bekris and Leverenz will develop and utilize artificial intelligence tools to identify novel drug targets and repurposable drugs for Alzheimer’s disease.

08/05/2021 |  

Artificial Intelligence Predicts Cancer Therapy-Related Cardiac Risk

A research team led by Drs. Cheng and Collier developed an artificial intelligence methodology to help identify cancer patients at risk for cancer therapy-related cardiac dysfunction.

08/04/2021 |  

Investigating Sex Differences in COVID-19 Immune Response

Utilizing large-scale patient data and samples from the Cleveland Clinic COVID-19 registry, Dr. Cheng’s team identified clinical characteristics and immune-related mechanisms associated with sex differences in COVID-19 outcomes.

07/20/2021 |  

Artificial Intelligence Methodology for Alzheimer’s Disease Drug Repurposing

Dr. Cheng’s team developed an artificial intelligence methodology to uncover molecular targets involved in neuroinflammation and identify candidate therapeutics for Alzheimer’s disease.

07/22/2021 |  

GMI Researchers Receive Awards Promoting Diversity in Science

Dr. Cheng and Ms. Castrillon Lal received the Gilliam Fellowship for Advanced Study, and Dr. Smith received the MOSAIC Postdoctoral Career Transition Award to Promote Diversity.

06/11/2021 |  

Network Medicine Links COVID-19 and Alzheimer’s Disease-like Cognitive Impairment

Utilizing network medicine methodologies, a research team led by Dr. Cheng linked COVID-19 to neuroinflammation and brain microvascular injury in Alzheimer’s disease-like cognitive impairment.

02/08/2021 |  

Researchers Develop Interactive Platform to Identify Druggable Cancer Mutations

Dr. Cheng and team developed a personalized genomic medicine platform to identify clinically actionable mutations and accelerate the development of cancer precision medicine protocols.

01/06/2021 |  

Study Shows Machine Learning’s Potential to Predict Cancer Therapy-Related Cardiac Risk

An interdisciplinary team led by Drs. Cheng and Collier developed machine learning models that predict with promising accuracy the risk of cardiac dysfunction in cancer survivors and may be generalizable to clinical practice.

11/09/2020 |  

Cleveland Clinic Researchers Use “Big Data” Approach to Identify Melatonin as Possible COVID-19 Treatment

Dr. Cheng and colleagues developed a network medicine strategy to predict disease manifestations associated with COVID-19 and find existing drugs with the potential to be effective COVID-19 treatments.

09/02/2020 |  

NIH Supplement Award for COVID-19 Drug Repurposing

Dr. Cheng and his team aim to identify repurposable drugs and combination regimens to treat COVID-19 in older adults

07/15/2020 |  

Genetic Factors May Influence COVID-19 Susceptibility

Dr. Cheng's team found a possible association between ACE2 and TMPRSS2 polymorphisms and COVID-19 susceptibility.

04/08/2020 |  

$3.3M NIH Grant for Alzheimer’s Drug Repurposing

Dr. Cheng will develop and implement computational tools to identify and test novel repurposable drugs and drug combinations for Alzheimer’s disease.

03/16/2020 |  

Network-Based Drug Discovery for the Emerging COVID-19 Epidemic

By harnessing the powers of systems pharmacology and predictive modeling, Dr. Cheng identified 16 drugs and three drug combinations that may be candidates for repurposing as potential COVID-19 treatments.

02/12/2020 |  

Harnessing AI for Drug Repurposing

A systems biology and network medicine expert, Dr. Cheng developed a deep learning methodology to more accurately predict drug-target interactions, which will help accelerate drug repurposing efforts.