Honghuang Lin, PhD
Adjunct Associate Professor, Medicine
Biography
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.
Other Positions
- Member, BU-BMC Cancer Center, Boston University
- Investigator, Framingham Heart Study
- Member, Evans Center for Interdisciplinary Biomedical Research, Boston University
Education
- National University of Singapore (NUS), PhD
- Peking University, BA
- Peking University, BS
Publications
- Published on 3/21/2024
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 the Alzheimer's Disease Sequencing Project whole genome sequence data. Alzheimers Dement. 2024 Mar 21. PMID: 38511601.
Read at: PubMed - Published on 3/21/2024
Ding H, Wang B, Hamel AP, Karjadi C, Ang TFA, Au R, Lin H. Exploring cognitive progression subtypes in the Framingham Heart Study. Alzheimers Dement (Amst). 2024; 16(1):e12574. PMID: 38515438.
Read at: PubMed - Published on 2/15/2024
Ding H, Kim M, Searls E, Sunderaraman P, De Anda-Duran I, Low S, Popp Z, Hwang PH, Li Z, Goyal K, Hathaway L, Monteverde J, Rahman S, Igwe A, Kolachalama VB, Au R, Lin H. Digital neuropsychological measures by defense automated neurocognitive assessment: reference values and clinical correlates. Front Neurol. 2024; 15:1340710. PMID: 38426173.
Read at: PubMed - Published on 1/23/2024
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. PMID: 38263370.
Read at: PubMed - Published on 1/16/2024
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. PMID: 38226510.
Read at: PubMed - Published on 1/16/2024
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. PMID: 38226518.
Read at: PubMed - Published on 1/16/2024
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. PMID: 38226519.
Read at: PubMed - Published on 12/7/2023
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. PMID: 38062110.
Read at: PubMed - Published on 12/6/2023
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.
Read at: PubMed - Published on 11/24/2023
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.
Read at: Custom
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