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One or more keywords matched the following properties of Lin, Honghuang
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keywords Multi-omics Data Integration
overview

I am a bioinformatician 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. The ultimate goal is to develop new computational strategies for the disease prevention and intervention.

  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 the 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.
One or more keywords matched the following items that are connected to Lin, Honghuang
Item TypeName
Concept Databases, Factual
Concept Database Management Systems
Concept Databases, Genetic
Concept Data Compression
Concept Data Mining
Concept Databases as Topic
Concept Databases, Protein
Concept Molecular Sequence Data
Academic Article Relations between circulating microRNAs and atrial fibrillation: data from the Framingham Offspring Study.
Academic Article Relations between plasma microRNAs, echocardiographic markers of atrial remodeling, and atrial fibrillation: Data from the Framingham Offspring study.
Academic Article Shared Genetic and Environmental Architecture of Cardiac Phenotypes Assessed via Echocardiography: The Framingham Heart Study.
Academic Article Using data science to diagnose and characterize heterogeneity of Alzheimer's disease.
Academic Article Assessment of the Mid-Life Demographic and Lifestyle Risk Factors of Dementia Using Data from the Framingham Heart Study Offspring Cohort.
Academic Article TANTIGEN: a comprehensive database of tumor T cell antigens.
Academic Article Dana-Farber repository for machine learning in immunology.
Academic Article Microarray data analysis of gene expression evolution.
Academic Article Prediction of antibiotic resistance proteins from sequence-derived properties irrespective of sequence similarity.
Academic Article Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research.
Academic Article Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector machine approach.
Academic Article Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties.
Academic Article Prediction of the functional class of lipid binding proteins from sequence-derived properties irrespective of sequence similarity.
Academic Article Integrative Omics Approach to Identifying Genes Associated With Atrial Fibrillation.
Academic Article Quality control and integration of genotypes from two calling pipelines for whole genome sequence data in the Alzheimer's disease sequencing project.
Academic Article A global characterization and identification of multifunctional enzymes.
Academic Article Homology-free prediction of functional class of proteins and peptides by support vector machines.
Academic Article Automatic synchronization and distribution of biological databases and software over low-bandwidth networks among developing countries.
Academic Article Efficacy of different protein descriptors in predicting protein functional families.
Academic Article Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness.
Academic Article PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence.
Academic Article MHC-BPS: MHC-binder prediction server for identifying peptides of flexible lengths from sequence-derived physicochemical properties.
Academic Article Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity.
Academic Article Prediction of transporter family from protein sequence by support vector machine approach.
Academic Article Strategies to design and analyze targeted sequencing data: cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study.
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