Curriculum Vitae

1451 E 55th Street

Apt 717N

Chicago, IL 60615

(646) 300-5036

firstname.lastname @ dbmi . columbia . edu

Educational Background

08/05-present Columbia University , New York , NY

Department of Biomedical Informatics, PhD Candidate

Research topic: drug target analysis and prediction by computational methods (under supervision of Dr. Andrey Rzhetsky)

07/02-07/04 National University of Singapore , Singapore

Department of Computational Science, Masters of Science

Thesis: Inhibitor prediction by machine learning approaches

09/98-07/02 Dalian University of Technology, P.R. China

Department of Chemical Engineering, Bachelor of Engineering

Thesis: Morphological Control of Nano Particles of Alumina

Research/Professional Experience

01.2006-present: Dr. Andrey Rzhetsky's lab, Columbia University

drug target analysis and prediction by computational methods

Current drug targets are analyzed in the context of a literature-based molecular network in order to identify some characteristics or patterns of drug targets. Specifically five tasks are achieved: (1) to identify macroscopical properties of drug targets; (2) to study topological properties of drug targets in a biological network; (3) to examine tissue selectivity of drug targets; (4) to verify the clusterability of drug targets for different disease categories; (5) to check the gene expression levels of drug targets in different tissues.

09.2005-12.2005: Dr. Andrea Califano's lab, Columbia Univeristy

Evidence integration of GeneWays and Aracne

When reconstructing a genome-wide biological network, there are many supporting evidences, such as microarray data, colocalization information, and literature reports. It is found that many predictions which yield complete and accurate cellular networks are based on moderate and consistent evidence from multiple sources rather than strong evidence from a single source. I aim to use Bayesian Network to integrating GeneWays (literature evidence) and Aracne (microarray prediction) and investigate its capability at reconstructing genetic regulatory network.

The fundamental idea is to assess each source of evidence for interactions by comparing it against samples of known positives and negatives ("gold standards"), yielding a statistical reliability. Then, extrapolating genome-wide, I predict the chance of possible interactions for every gene pair by combining each independent evidence source according to its reliability.

09.2004-12.2004: Department of Chemistry, Rensselaer Polytechnic Institute

Comparative study of docking/scoring functions based on Trypsin inhibitor

Scoring function is one of the most important elements of docking programs. In this project, three docking/scoring softwares, namely SYBYL, MOE, and GRAMM, are evaluated in terms of their sampling and scoring algorithms. The benchmark protein-ligand complex used is a Trypsin protein and its inhibitor Benzamidine.

07.2002-06.2004: Department of Computational Science, National University of Singapore

Inhibitor Prediction by Machine Learning Approaches

Three widely used algorithms from machine learning community were explored to facilitate inhibitor prediction for three pharmacologically important proteins. The aim was to evaluate the feasibility of introducing these machine learning approaches to lead identification and its ADME/toxicity properties analysis. Specifically, I worked on the inhibitor/antagonist prediction for a therapeutic target (5-HT2), an adverse reaction target (cholinesterase) and an ADME associated protein (CYP3A4). The machine learning approaches used include decision tree, k-nearest neighbor and support vector machine, and preprocessing techniques such as normalization and principal component analysis. Quantitative Structure Activity Relationship (QSAR) methods were used to extract features from 3D structures of small molecules.

Teaching Experience

01/03-12/03 National University of Singapore , Singapore

• Mentor of Undergraduate Research Opportunities Programme ( UROPS ) for 2 nd year college students

• Mentor of Science Research Programme ( SRP ) for senior high school students

• Teaching Assistant of CZ1102 Problem Solving and Computation ( C Language programming)

08/04-05/05 Rensselaer Polytechnic Institute

Teaching Assistant of Organic Chemistry Laboratory I & II

01/07-05/07 Columbia University

• Teaching Assistant of Computational Biology and Bioinformatics II

• Teaching Assistant of Biological Sequence Analysis

Other Experience

05/07-08/07 GlacoSmithKline, Collegeville, PA

Summer internship, translational medicine analysis based on biomarker capturing. I compare and evaluate nested ANOVA and t-test et al on microarray data obtained from in-house cell line samples and clinical samples (patient biopsy).

Publications

• Quantitative Systems-level determinants of human genes targeted by successful drugs. L. Yao and A. Rzhetsky (submitted 2007)
• Internet Resources for Proteins Associated with Drug Therapeutic Effects, Adverse Reactions, and ADME. Z. L. Ji, L. Z. Sun, X. Chen, C. J. Zheng, L. X. Yao , L. Y. Han, Z.W. Cao,oJ. F. Wang, W. K. Yeo, C.Z. Cai, and Y. Z. Chen. Drug Discovery Today , 8(12),526-529. (2003).
• KDBI:Kinetic Data of Bio-molecular Interactions Database. Z. L. Ji, X. Chen, C. J. Zheng, L.X. Yao , L. Y. Han , W. K. Yeo, P. C. Chung, H. S. Puy, Y. T. Tay, A. Muhammad, and Y. Z. Chen. Nucleic. Acids. Res ., 31(1), 255-257. (2003).

Computer Skills

Familiar with molecular modeling/bioinformatics techniques, e.g. QSAR, docking, sequence alignment, homology modeling and machine learning. Proficient at Matlab, Perl, mySQL, HTML, and UNIX OS

Affiliations

American Medical Informatics Association

New York Academy of Sciences