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. Herbert C, Shi Q, Baek J, Wang B, Kheterpal V, Nowak C, Suvarna T, Singh A, Hartin P, Durnam B, Schrader S, Harman E, Gerber B, Barton B, Zai A, Cohen-Wolkowiez M, Corbie-Smith G, Kibbe W, Marquez J, Hafer N, Broach J, Lin H, Heetderks W, McManus DD, Soni A. Association of neighborhood-level sociodemographic factors with Direct-to-Consumer (DTC) distribution of COVID-19 rapid antigen tests in 5 US communities. BMC Public Health. 2023 Sep 22; 23(1):1848. PMID: 37735647; PMCID: PMC10515232; DOI: 10.1186/s12889-023-16642-3;
  2. Blusztajn JK, Aytan N, Rajendiran T, Mellott TJ, Soni T, Burant CF, Serrano GE, Beach TG, Lin H, Stein TD. Cerebral Gray and White Matter Monogalactosyl Diglyceride Levels Rise with the Progression of Alzheimer's Disease. J Alzheimers Dis. 2023 Sep 14. PMID: 37718815; DOI: 10.3233/JAD-230543;
  3. Lee WP, Choi SH, Shea MG, Cheng PL, Dombroski BA, Pitsillides AN, Heard-Costa NL, Wang H, Bulekova K, Kuzma AB, Leung YY, Farrell JJ, Lin H, Naj A, Blue EE, Nusetor F, Wang D, Boerwinkle E, Bush WS, Zhang X, De Jager PL, Dupuis J, Farrer LA, Fornage M, Martin E, Pericak-Vance M, Seshadri S, Wijsman EM, Wang LS, Schellenberg GD, Destefano AL, Haines JL, Peloso GM. Association of Common and Rare Variants with Alzheimer's Disease in over 13,000 Diverse Individuals with Whole-Genome Sequencing from the Alzheimer's Disease Sequencing Project. medRxiv. 2023 Sep 02. PMID: 37693521; PMCID: PMC10491367; DOI: 10.1101/2023.09.01.23294953;
  4. Wang Y, Sarnowski C, Lin H, Pitsillides AN, Heard-Costa NL, Choi SH, Wang D, Bis JC, Blue EE, Boerwinkle E, De Jager PL, Fornage M, Wijsman EM, Seshadri S, Dupuis J, Peloso GM, DeStefano AL. Key variants via Alzheimer's Disease Sequencing Project whole genome sequence data. medRxiv. 2023 Aug 29. PMID: 37693453; PMCID: PMC10491364; DOI: 10.1101/2023.08.28.23294631;
  5. Pirruccello JP, Khurshid S, Lin H, Weng LC, Zamirpour S, Kany S, Raghavan A, Koyama S, Vasan RS, Benjamin EJ, Lindsay ME, Ellinor PT. AORTA Gene: Polygenic prediction improves detection of thoracic aortic aneurysm. medRxiv. 2023 Aug 25. PMID: 37662232; PMCID: PMC10473783; DOI: 10.1101/2023.08.23.23294513;
  6. Keshawarz A, Bui H, Joehanes R, Ma J, Liu C, Huan T, Hwang SJ, Tejada B, Sooda M, Courchesne P, Munson PJ, Demirkale CY, Yao C, Heard-Costa NL, Pitsillides AN, Lin H, Liu CT, Wang Y, Peloso GM, Lundin J, Haessler J, Du Z, Cho M, Hersh CP, Castaldi P, Raffield LM, Wen J, Li Y, Reiner AP, Feolo M, Sharopova N, Vasan RS, DeMeo DL, Carson AP, Kooperberg C, Levy D. Expression quantitative trait methylation analysis elucidates gene regulatory effects of DNA methylation: the Framingham Heart Study. Sci Rep. 2023 Aug 10; 13(1):12952. PMID: 37563237; PMCID: PMC10415314; DOI: 10.1038/s41598-023-39936-3;
  7. Wang X, Pathiravasan CH, Zhang Y, Trinquart L, Borrelli B, Spartano NL, Lin H, Nowak C, Kheterpal V, Benjamin EJ, McManus DD, Murabito JM, Liu C. Association of Depressive Symptom Trajectory With Physical Activity Collected by mHealth Devices in the Electronic Framingham Heart Study: Cohort Study. JMIR Ment Health. 2023 Jul 14; 10:e44529.View Related Profiles. PMID: 37450333; PMCID: PMC10382951; DOI: 10.2196/44529;
  8. Soni A, Herbert C, Lin H, Yan Y, Pretz C, Stamegna P, Wang B, Orwig T, Wright C, Tarrant S, Behar S, Suvarna T, Schrader S, Harman E, Nowak C, Kheterpal V, Rao LV, Cashman L, Orvek E, Ayturk D, Gibson L, Zai A, Wong S, Lazar P, Wang Z, Filippaios A, Barton B, Achenbach CJ, Murphy RL, Robinson ML, Manabe YC, Pandey S, Colubri A, O'Connor L, Lemon SC, Fahey N, Luzuriaga KL, Hafer N, Roth K, Lowe T, Stenzel T, Heetderks W, Broach J, McManus DD. Performance of Rapid Antigen Tests to Detect Symptomatic and Asymptomatic SARS-CoV-2 Infection : A Prospective Cohort Study. Ann Intern Med. 2023 Jul; 176(7):975-982. PMID: 37399548; PMCID: PMC10321467; DOI: 10.7326/M23-0385;
  9. Brant LCC, Ribeiro AH, Pinto-Filho MM, Kornej J, Preis SR, Fetterman JL, Eromosele OB, Magnani JW, Murabito JM, Larson MG, Benjamin EJ, Ribeiro ALP, Lin H. Association Between Electrocardiographic Age and Cardiovascular Events in Community Settings: The Framingham Heart Study. Circ Cardiovasc Qual Outcomes. 2023 Jul; 16(7):e009821.View Related Profiles. PMID: 37381910
  10. Zhang C, Miao X, Wang B, Thomas RJ, Ribeiro AH, Brant LCC, Ribeiro ALP, Lin H. Association of lifestyle with deep learning predicted electrocardiographic age. Front Cardiovasc Med. 2023; 10:1160091. PMID: 37168659; PMCID: PMC10165078; DOI: 10.3389/fcvm.2023.1160091;
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This graph shows the total number of publications by year, by first, middle/unknown, or last author.

Bar chart showing 199 publications over 19 distinct years, with a maximum of 23 publications in 2022

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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