Honghuang Lin, PhD
Adjunct Associate Professor
Boston University Chobanian & Avedisian School of Medicine
Computational Biomedicine

PhD, National University of Singapore (NUS)
BA, Peking University
BS, Peking University

I am a bioinformatician/biostatistician with training in mathematics, machine learning, genetics, and digital medicine. Our lab is mainly focused on the development and application of computational tools to study complex diseases.

1. Identification of genetic causes of complex diseases. We have been involved in multiple large-scale genetic consortiums, such as the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, Trans-Omics for Precision Medicine (TOPMed) program, and Alzheimer's Disease Sequencing Project (ADSP). These studies have identified hundreds of genetic loci associated with atrial fibrillation, heart failure, hypertension, and Alzheimer’s disease.

2. Integration of multi-omics data to understand disease molecular mechanisms. Complex diseases are usually caused by the interplay of genetic and environmental factors. We have identified numerous molecular signatures from gene expression, protein expression, and DNA methylation that are related to aging and cardiovascular disease. We are also developing computational methods to integrate different molecular signatures and build gene interaction networks to study potential disease regulation networks.

3. Development of machine learning models for early disease diagnosis. We have built multiple machine learning models to predict dementia risk from midlife risk factors and neuropsychological tests. In combination with neuroimaging and blood-based measures, we are also developing multimodal machine learning methods to identify new biomarkers that are predictive of future cognitive impairment.

4. Exploration of digital and wearable devices for health monitoring. We have deployed thousands of wearable devices and mobile apps to monitor cardiovascular health and cognitive health. We are integrating active engagement with passive engagement technologies from the habitual environment to make sustained monitoring feasible. Novel analytic strategies are also being developed to analyze big unstructured data to identify potential digital biomarkers that are predictive of future health outcomes.

Boston University
BU-BMC Cancer Center

Framingham Heart Study

Boston University
Evans Center for Interdisciplinary Biomedical Research

Integrated Digital Technology Platform for Optimization of Precision Brain Health
07/01/2020 - 06/30/2024 (Multi-PI)
PI: Honghuang Lin, PhD
American Heart Association

Identification of digital biomarkers for Alzheimer's disease
03/01/2020 - 02/28/2023 (PI)
Alzheimer's Association

Assessing Alzheimer disease risk and heterogeneity using multimodal machine learning approaches
09/15/2021 - 12/31/2021 (Multi-PI)
PI: Honghuang Lin, PhD
NIH/National Institute on Aging


Biostatistical analysis of TOPMed genetics data
06/01/2019 - 05/31/2020 (PI)
University of Connecticut Health Center

Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.

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  1. Leung YY, Naj AC, Chou YF, Valladares O, Schmidt M, Hamilton-Nelson K, Wheeler N, Lin H, Gangadharan P, Qu L, Clark K, Kuzma AB, Lee WP, Cantwell L, Nicaretta H, Haines J, Farrer L, Seshadri S, Brkanac Z, Cruchaga C, Pericak-Vance M, Mayeux RP, Bush WS, Destefano A, Martin E, Schellenberg GD, Wang LS. Human whole-exome genotype data for Alzheimer's disease. Nat Commun. 2024 Jan 23; 15(1):684.View Related Profiles. PMID: 38263370; PMCID: PMC10805795; DOI: 10.1038/s41467-024-44781-7;
  2. De Anda-Duran I, Sunderaraman P, Searls E, Moukaled S, Jin X, Popp Z, Karjadi C, Hwang PH, Ding H, Devine S, Shih LC, Low S, Lin H, Kolachalama VB, Bazzano L, Libon DJ, Au R. Comparing Cognitive Tests and Smartphone-Based Assessment in 2 US Community-Based Cohorts. J Am Heart Assoc. 2024 Jan 16; 13(2):e032733.View Related Profiles. PMID: 38226519; DOI: 10.1161/JAHA.123.032733;
  3. Popp Z, Low S, Igwe A, Rahman MS, Kim M, Khan R, Oh E, Kumar A, De Anda-Duran I, Ding H, Hwang PH, Sunderaraman P, Shih LC, Lin H, Kolachalama VB, Au R. Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research. J Am Heart Assoc. 2024 Jan 16; 13(2):e031247.View Related Profiles. PMID: 38226518; DOI: 10.1161/JAHA.123.031247;
  4. Sunderaraman P, De Anda-Duran I, Karjadi C, Peterson J, Ding H, Devine SA, Shih LC, Popp Z, Low S, Hwang PH, Goyal K, Hathaway L, Monteverde J, Lin H, Kolachalama VB, Au R. Design and Feasibility Analysis of a Smartphone-Based Digital Cognitive Assessment Study in the Framingham Heart Study. J Am Heart Assoc. 2024 Jan 16; 13(2):e031348.View Related Profiles. PMID: 38226510; DOI: 10.1161/JAHA.123.031348;
  5. Møller AL, Vasan RS, Levy D, Andersson C, Lin H. Integrated omics analysis of coronary artery calcifications and myocardial infarction: the Framingham Heart Study. Sci Rep. 2023 Dec 07; 13(1):21581.View Related Profiles. PMID: 38062110; PMCID: PMC10703905; DOI: 10.1038/s41598-023-48848-1;
  6. Mensah Otabil E, Dai Q, Anzenberg P, Filippaios A, Ding E, Mehawej J, Mathew JE, Lessard D, Wang Z, Noorishirazi K, Hamel A, Paul T, DiMezza D, Han D, Mohagheghian F, Soni A, Lin H, Barton B, Saczynski J, Chon KH, Tran KV, McManus DD. Technology engagement is associated with higher perceived physical well-being in stroke patients prescribed smartwatches for atrial fibrillation detection. Front Digit Health. 2023; 5:1243959. PMID: 38125757; PMCID: PMC10731012; DOI: 10.3389/fdgth.2023.1243959;
  7. Ding H, Wang B, Hamel A, Melkonyan M, Ang TA, Au R, Lin H. Prediction of Progression from Mild Cognitive Impairment to Alzheimer's disease with Longitudinal and Multimodal Data. Front Dementia. 2023; DOI:10.3389/frdem.2023.127168. View Publication
  8. Ding H, Hamel A, Karjadi C, Ang TA, Lu S, Thomas RJ, Au R, Lin H. Association between acoustic features and brain volumes: the Framingham Heart Study. Front Dementia. 2023; DOI:10.3389/frdem.2023.1214940. View Publication
  9. Herbert C, Manabe YC, Filippaios A, Lin H, Wang B, Achenbach C, Kheterpal V, Hartin P, Suvarna T, Harman E, Stamegna P, Rao LV, Hafer N, Broach J, Luzuriaga K, Fitzgerald KA, McManus DD, Soni A. Differential Viral Dynamics by Sex and Body Mass Index During Acute SARS-CoV-2 Infection: Results from a Longitudinal Cohort Study. Clin Infect Dis. 2023 Nov 16. PMID: 37972270; DOI: 10.1093/cid/ciad701;
  10. Sarnowski C, Huan T, Ma Y, Joehanes R, Beiser A, DeCarli CS, Heard-Costa NL, Levy D, Lin H, Liu CT, Liu C, Meigs JB, Satizabal CL, Florez JC, Hivert MF, Dupuis J, De Jager PL, Bennett DA, Seshadri S, Morrison AC. Multi-tissue epigenetic analysis identifies distinct associations underlying insulin resistance and Alzheimer's disease at CPT1A locus. Clin Epigenetics. 2023 Oct 27; 15(1):173.View Related Profiles. PMID: 37891690; PMCID: PMC10612362; DOI: 10.1186/s13148-023-01589-4;
Showing 10 of 215 results. Show More

This graph shows the total number of publications by year, by first, middle/unknown, or last author.

Bar chart showing 215 publications over 20 distinct years, with a maximum of 29 publications in 2023

In addition to these self-described keywords below, a list of MeSH based concepts is available here.

Machine learning
Microarray data analysis
Next generation sequencing
Multi-omics data integration

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