RSIR: regularized sliced inverse regression for motif discovery

Zhong, Wenxuan; Zeng, Peng; Ma, Ping; Liu, Jun S.; Zhu, Yu

doi:10.1093/bioinformatics/bti680

Cited by 68 publications

(77 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, a few novel interactions are also detected. As ChIP-chip data tends to be noisy, integration of gene expression data to our analysis would be more comprehensive (Sun et al, 2006;Zhong et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

Statistical methods to infer cooperative binding among transcription factors in Saccharomyces cerevisiae

Datta

Zhao

2007

Bioinformatics

View full text Add to dashboard Cite

Motivation: Transcription factors regulate transcription in prokaryotes and eukaryotes by binding to specific DNA sequences in the regulatory regions of the genes. This regulation usually occurs in a coordinated manner involving multiple transcription factors. Genome-wide location data, also called ChIP-chip data, have enabled researchers to infer the binding sites for individual regulatory proteins. However, current methods to infer binding sites, such as simple thresholding based on p-values, are not optimal for a number of study objectives like combinatorial regulation, leading to potential loss of information. Hence, there is a need to develop more efficient statistical methods for analyzing such data. Results: We propose to use log-linear models to study cooperative binding among transcription factors and have developed an Expectation-Maximization algorithm for statistical inferences. Our method is advantageous over simple thresholding methods both based on simulation and real data studies. We apply our method to infer the cooperative network of 204 regulators in Rich Medium and a subset of them in four different environmental conditions. Our results indicate that the cooperative network is condition specific; for a set of regulators, the network structure changes under different environmental conditions.

show abstract

Section: Discussionmentioning

confidence: 99%

Statistical methods to infer cooperative binding among transcription factors in Saccharomyces cerevisiae

Datta

Zhao

2007

Bioinformatics

View full text Add to dashboard Cite

show abstract

“…In the literature, there are many different approaches to regularize SIR [6][7][8]. In this work a very simple and intuitive approach is used.…”

Section: Regularized Sliced Inverse Regressionmentioning

confidence: 99%

“…Although results obtained in Reference [4] were satisfactory, in this paper the authors further study other multivariate calibration methods in order to obtain a better prediction performance. A new calibration model based on regularized slice inverse regression (RSIR) [5][6][7][8] is proposed. This model improved the results obtained from both PLS and the popular Support vector regression (SVR) [9], commonly used nowadays.…”

Section: Introductionmentioning

confidence: 99%

Regularized sliced inverse regression for determination of the percentage of crystallinity in FCC catalysts

Hernández

Talavera

Porro

et al. 2010

Journal of Chemometrics

View full text Add to dashboard Cite

This paper proposes a new calibration model based on regularized sliced inverse regression (RSIR) for predicting the percentage of crystallinity of fluidized catalytic cracking catalysts (FCC) using Fourier transform mid-infrared spectroscopy (FT-MIR). RSIR is an effective dimension-reduction tool that looks for a proper dimension-reduction subspace without requiring a pre-specified functional form for the relation between independent and dependent variables. Combinations of RSIR with linear and nonlinear learning algorithms like multiple linear regression (MLR) and Support vector regression (SVR) were applied to the preprocessed data set. A comparison of performance among the different approaches, including previous results reached using PLS, was done. RSIR-MLR achieved the highest prediction accuracy, leading to a simple calibration model.

show abstract

“…Tadesse et al (2004) later presented a Bayesian version of a similar approach. To alleviate the dependence of the linearity and Gaussian assumptions, Das et al (2004) suggested an approach using MARS, and Zhong et al (2005) designed a modified slice inverse regression approach, which also effectively reduces the dimensionality without assuming linearity.…”

Section: Joint Analysis Of Sequence Motifs and Expression Microarraysmentioning

confidence: 99%

“…In the past two decades, we have witnessed the development of the likelihood approach to pairwise sequence alignments (Bishop and Thompson, 1986;Thorne et al, 1991); probabilistic models for RNA secondary structure predictions (Zuker, 1989;Lowe and Eddy, 1997;Ding and Lawrence, 2001;Pedersen et al, 2004); the expectation-maximization (EM) algorithm for finding regulatory binding motifs (Lawrence and Reilly, 1990;Cardon and Stormo, 1992), the Gibbs sampling strategies for detecting subtle sequence similarities (Lawrence et al, 1993;Liu, 1994;Neuwald et al, 1997); the hidden Markov models (HMMs) for DNA composition analysis, multiple sequence alignments, gene prediction, and protein secondary structure prediction (Churchill, 1989;Krogh et al, 1994a;Baldi et al, 1994;Burge and Karlin, 1997;Schmidler et al, 2000; Chapters 4 and 5); regression and Bayesian network approaches to gene regulation networks (Bussemaker et al, 2001;Segal et al, 2003;Conlon et al, 2003;Beer and Tavazoie, 2004;Zhong et al, 2005); and many statistical-model based approaches to gene expression microarray analyses (Li and Wong, 2001;Lu et al, 2004;Speed, 2003). All these developments show that algorithms resulting from statistical modeling efforts constitute a significant portion of today's bioinformatics toolbox.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Methods in Biological Sequence Analysis

Liu

Logvinenko

2007

Handbook of Statistical Genetics

View full text Add to dashboard Cite

Hidden Markov models, the expectation-maximization algorithm, and the Gibbs sampler were introduced for biological sequence analysis in early 1990s. Since then the use of formal statistical models and inference procedures has revolutionized the field of computational biology. This chapter reviews the hidden Markov and related models, as well as their Bayesian inference procedures and algorithms, for sequence alignments and gene regulatory binding motif discoveries. We emphasize that the combination of Markov chain Monte Carlo and dynamic-programming techniques often results in effective algorithms for nondeterministic polynomial (NP)-hard problems in sequence analysis. We will also discuss some recent approaches to infer regulatory modules and to combine expression data with sequence data.

show abstract

RSIR: regularized sliced inverse regression for motif discovery

Abstract: Matlab programs are available upon request from the authors.

Cited by 68 publications

References 26 publications

Statistical methods to infer cooperative binding among transcription factors in Saccharomyces cerevisiae

Statistical methods to infer cooperative binding among transcription factors in Saccharomyces cerevisiae

Regularized sliced inverse regression for determination of the percentage of crystallinity in FCC catalysts

Bayesian Methods in Biological Sequence Analysis

Contact Info

Product

Resources

About