of Statistical Science Academia Sinica
Taipei 11529, Taiwan, R.O.C.
Tel: 886-2-27835611 ext. 468
FAX : 886-2-27831523
- Bioinformatics, systems biology, lung cancer
High dimensional data analysis, Large
ensembles of time series, Medical image analysis,
Machine learning, Statistical graphics, Bayesian
computation, Regression, Censoring, Experimental design,
Li is best known for introducing sliced inverse
regression (SIR) and principal Hessian direction (PHD),
two fundamental dimension reduction methods for high
dimensional data analysis. Starting from 2000, his
research interest turned to the emerging field of
computation/mathematics/statistics in genome biology. In
2002, he published a paper in Proceedings of Academy of
Science, featuring the novel method of liquid
association (LA) for microarray gene expression
analysis. He is currently leading a research group in
UCLA and in Academia Sinica to continue the development
of methods for utilizing multiple sources of gene
expression profiling, genetic markers, complex disease
phenotypes and traits. A website
http://kiefer.stat2.sinica.edu.tw/LAP3 offers on-line
computation based on LA and related methods for gene
expression studies. He is also collaborating with Dr.
Pan Chyr Yang of National Taiwan University and his
colleagues on integrative cancer biology.
association. (a) Association between genes X and Y as
mediated by gene Z. When gene Z is expressed at the high
level (red), a positive correlation between X and Y is
observed. The association changes as the expression of Z
is lowered. It eventually becomes a negative trend
(green). There are two basic ways (shown in panels b and
c) to apply the liquid association (LA) scoring system
to guide a genome-wide search. (b) When two genes X and
Y are given, compute LA score LA(X, Y|Z) for every gene
Z first and then output a short list of high score genes
Z1, Z2, and so on. (c) When only one gene X is given,
compute LA score LA(X, Y|Z) for every pair of genes X,Y
first and then output a short list of high score gene
pairs Y1,Z1, Y2,Z2, and so on. Li et al. Genome Biology
2007 8:R205 doi:10.1186/gb-2007-8-10-r205
- Liquid association. Correlation is a simple
and powerful method for analyzing gene expression
data.Two genes with positively correlated expression
profiles are likely to be functionally associated. They
may participate in the same biological process. However,
functionally associated genes may not be correlated for
a variety of reasons. For instance, they may not be
regulated at the transcription level. Another common
situation is that most genes have multiple functions.
Depending on the cellular needs, co-expressed genes may
become uncorrelated or even turn into contra-expressed.
Liquid association (LA), as opposed to "steady"
association, is designed to quantify the change of
correlation between two genes as a relevant cellular
state variable changes. There is no need to specify the
state variable explicitly. A highlighted example is the
elucidation of gene expression for the urea cycle in
yeast. This pathway controls both the biosynthesis and
degradation of the amino acid, arginine. LA is able to
find the correct genes which have to be turned on, as
well as the correct genes which have to be turned off at
the same time, so that any wasteful immediate
degradation of newly synthesized arginine can be
- Sliced inverse regression (SIR). Many
statistical methods are known to suffer from urse of
dimensionality? they break down easily when dealing with
high dimensional data. How to reduce dimensionality is a
long-standing issue. Ad hoc methods such as principal
component analysis and partial least squares have been
advocated. Yet the associated issue about possible
nformation loss due to improper dimension reduction is
rarely addressed. The SIR methodology helps reshape this
area by presenting an effective dimension reduction
framework for theorizing both issues.
- Principal Hessian direction. Another effective
dimension reduction method.
||Ph.D., Statistics, University of California, Berkeley. (Advisor: Jack Kiefer)
||B.S., Mathematics, National Taiwan University
||Distinguished Research Fellow, Institute of Statistical Science, Academia Sinica
||Director, Institute of Statistical Science, Academia Sinica
||graduate vice chair, Statistics Department, UCLA
||Professor, Statistics Department, UCLA
|1989-present||Professor, Mathematics Department, UCLA
||Associate Professor, Mathematics Department, UCLA
||Assistant Professor, Statistics Department, Purdue University
||Co-Editor of Statistica Sinica
||Associate editor of Annals of Statistics
|| Associate editor of Statistica Sinica
||Associate editor of Computational Statistics
||Elected Member, The World Academy of Sciences (2014)
||Academician, Academia Sinica
||Medallion Lecturer, IMS; 1993 Guggenheim Fellow
||NSF/ASA/NIST fellow; 1990 JASA theory and methods Editor's invited speaker in Joint Statistical Meetings
||IMS Fellow; 1981 elected member of Phi Beta Kappa
||B. Friedman Memorial Prize in Applied Mathematics, U.C. Berkeley
 Sung-Liang Yu,Hsuan-Yu Chen, Gee-Chen
Chang,6Chih-Yi Chen,Huei-Wen Chen, Sher
Singh,Chiou-Ling Cheng, Chong-Jen Yu, Yung-Chie Lee,
Han-Shiang Chen,Te-Jen Su, Ching-Cheng Chiang,Han-Ni
Li,Qi-Sheng Hong, Hsin-Yuan Su, Chun-Chieh
Chen,Wan-Jiun Chen, Chun-Chi Liu,Wing-Kai Chan,Wei J.
Chen, Ker-Chau Li,Jeremy J.W. Chen, and Pan-Chyr Yang
(2008) MicroRNA Signature Predicts Survival and
Relapse in Lung Cancer. Cancer Cell 13, 48?7.
 Li, KC, Palotie A, Yuan, S, Bronnikov, D., Chen
D., Wei X., Choi, O., Saarela J., Peltonen L. (2007)
Finding disease candidate genes by liquid association.
Genome Biology, 8, R205. oi:10.1186/gb-2007-8-10-r205.
 Yuan, S., and Li. K.C. (2007) Context-dependent
Clustering for Dynamic Cellular State Modeling of
Microarray Gene Expression. Bioinformatics 2007;
 Wei Sun; Tianwei Yu; Ker-Chau Li
of eQTL modules mediated by activity levels of
transcription factors. Bioinformatics; 2007 Sep
 Chun-Chi Liu, Chin-Chung Lin, Ker-Chau Li,
Wen-Shyen E. Chen, Jiun-Ching Chen, Ming-Te Yang,
Pan-Chyr Yang, Pei-Chun Chang, and Jeremy J.W. Chen.
(2007) Genome-wide identification of the specific
oligonucleotides using artificial neural network and
computational genomic analysis. BMC Bioinformatics.
 Tianwei Yu, Hui Ye, Wei Sun, Ker-Chau Li, Zugen
Chen, Sharoni Jacobs, Dione K Bailey, David T Wong and
Xiaofeng Zhou (2007). A forward-backward fragment
assembling algorithm for the identification of genomic
amplification and deletion breakpoints using
high-density single nucleotide polymorphism (SNP)
array. BMC Bioinformatics, 8:145.
 Yu, T., and Li, K.C. (2005). Inference of
transcriptional regulatory network by two-stage
constrained space factor analysis. Bioinformatics 21,
 Yu , T., Sun, W., Yuan , S., and Li, K.C. (2005).
Study of coordinative gene expression at the
biological process level. Bioinformatics 21 3651-3657.
 Li, K.C.o, Ching-Ti Liu, Wei Sun, Shinsheng Yuan
and Tianwei Yu (2004). A system for enhancing
genome-wide co-expression dynamics study. Proceedings
of National Academy of Sciences . 101 , 15561-15566.
 Xie, J., Li, K.C., and Bina, M. (2004) A Bayesian
Insertion/Deletion Algorithm for Distant Protein Motif
Searching via Entropy Filtering. J. American
Statistical Association , 99, 409-420.
 Li, K.C., and Yuan, S. (2004) A functional genomic
study on NCI's anticancer drug screen. The
Pharmacogenomics Journal, 4, 127-135.
 Li, K.C., Aragon, Y, Shedden, K. and Thomos-Agan
C., C.(2003). Dimension reduction for multivariate
response data. Journal of American Statistical
Association. 98, 99-106.
 Li, K.C. (2002) Genome-wide co-expression
dynamics: theory and application. Proceedings of
National Academy of Science . 99, 16875-16880.
 Li, K.C., Yan, M. and Yuan, S. (2002) A simple
statistical model for depicting the cdc-15
synchronized yeast cell cycle-regulated gene
expression data. Statistica Sinica, 12, 141-158.
 Li, K.C. and Shedden. K (2002). Identification of
shared common components in large ensembles of time
series using dimension reduction. Journal of American
Statistical Association, 97, 759-765.
 Li, K.C. and Shedden, K. (2001). Monte Carlo
deconvolution of digital signals guided by the inverse
filter. Journal of Amer. Stat. Assoc. 96, 1014-1021.
 Li, K.C., Lue, H.H, and Chen, C.H. (2000)
Interactive tree-structured regression via principal
Hessian directions. Journal Amer. Statist. Assoc. 95,
 Li, K.C., J.L. Wang, and C.H. Chen (1999).
Dimension reduction for censored regression data. Ann.
Stat.. 27, 1-23.
 Li, K. C. (1992). On principal Hessian directions
for data visualization and dimension reduction :
another application of Stein's lemma. J. Ameri. Stat.
Assoc. 87, 1025-1039.
 Li, K. C. (1991). Sliced inverse regression for
dimension reduction, with discussions. J. Amer.
Statist. Assoc. 86, 316-342.