Honghuang Lin, PhD

Assistant ProfessorHonghuang Lin


PhD, National University of Singapore, Singapore

General Field of Work:

Bioinformatics; Cardiovascular genetics

Affiliations other than medicine:

Framingham Heart Study

Contact information:

Office/Lab: Robinson, B616

Phone: (617)-638-7649

Fax: (617)-638-8086



Next generation sequencing; Systems biology; Machine learning; computer-aided drug design; Cardiovascular diseases

Summary of academic interest:

The advance of high-throughput technologies such as microarray and next generation sequencing offers a great opportunity to study biological systems from an unprecedented genome-wide approach. My research focuses on the design of statistical and computational tools to discover genetic or epigenetic causes of cancer and cardiovascular diseases. In particular, I am interested in the discovery of various pathogenesis-related variations, such as single nucleotide polymorphisms (SNPs), copy number variations (CNVs), insertions/deletions, and methylation. I am also interested in the modeling/simulation of biological pathways, and investigating the effects of genetic heterogeneity to disease phenotypes. Ultimately, the goal is to develop novel computational tools and to identify potential therapeutic targets for drug discovery.

Recent Publications:

Lin HH, Han LY, Cai CZ, Ji ZL, and Chen YZ. Classification of Transporter Families from Primary Sequence by Support Vector Machine Approach. Proteins, 2006, 62 (1): 218-31

Ong AK, Lin HH, Chen YZ, Li ZR, and Cao ZW. Efficacy of different protein descriptors in predicting protein functional families. BMC Bioinformatics, 2007, 8 (1): 300

Tang ZQ, Han LY, Lin HH, Cui J, Jia J, Low BC, Li BW, Chen YZ. Derivation of Stable Microarray Cancer-differentiating Signatures by a Feature-selection Method Incorporating Consensus Scoring of Multiple Random Sampling and Gene-Ranking Consistency Evaluation. Cancer Research, 2007, 67(20):9996-10003

Lin HH, Han LY, Yap CW, Xue Y, Liu XH, Zhu F, and Chen YZ. Prediction of Factor Xa Inhibitors by Machine Learning Methods. Journal of Molecular Graphics and Modelling, 2007, 26 (2): 505-518

Han LY, Ma XH, Lin HH, Jia J, Zhu F, Xue Y, Li ZR, Cao ZW, Ji ZL, and Chen YZ. A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor. J Mol Graph Model, 2008, 26(8):1276-86

Lin HH, Ray S, Tongchusak S, Reinherz EL and Brusic V. Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research. BMC Immunology, 2008, 9(1):8

Zhang HL, Lin HH, Tao L, Ma XH, Dai JL, Jia J and Cao ZW. Prediction of Antibiotic Resistance Proteins from Sequence Derived Properties Irrespective of Sequence Similarity. International Journal of Antimicrobial Agents, 2008, 32: 221-226

Lin HH. Microarray Data Analysis of Gene Expression Evolution. Gene Regulation and Systems Biology,2009, 3:211-214