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Missing value estimation methods for DNA microarrays
, 2001
"... Motivation: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clu ..."
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Cited by 477 (24 self)
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Motivation: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data.
Block-relaxation Algorithms in Statistics
, 1994
"... this paper we discuss four such classes of algorithms. Or, more precisely, we discuss a single class of algorithms, and we show how some well-known classes of statistical algorithms fit in this common class. The subclasses are, in logical order, block-relaxation methods augmentation methods majoriza ..."
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Cited by 41 (2 self)
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this paper we discuss four such classes of algorithms. Or, more precisely, we discuss a single class of algorithms, and we show how some well-known classes of statistical algorithms fit in this common class. The subclasses are, in logical order, block-relaxation methods augmentation methods majorization methods Expectation-Maximization Alternating Least Squares Alternating Conditional Expectations
An Evaluation of Some Approximate F Statistics and Their Small Sample Distributions for the Mixed Model with . . .
, 1987
"... The purpose of this work was to extend results from the General Linear Univariate Model and the General Linear Multivariate Model to special cases of the mixed model with linear covariance structure. These extensions were then used to motivate approximate F statistics for the mixed model. Three appr ..."
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Cited by 3 (1 self)
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The purpose of this work was to extend results from the General Linear Univariate Model and the General Linear Multivariate Model to special cases of the mixed model with linear covariance structure. These extensions were then used to motivate approximate F statistics for the mixed model. Three approximate F statistics were proposed; one was based on the canonical form of the mixed model (FREML) and two were based on weighted least squares (F WLS ' F
A New Data Imputing Algorithm
"... DNA microarray analysis has become the most widely used functional genomics approach in the bioinformatics field. Microarray gene expression data often contains missing values due to various reasons. Clustering gene expression data algorithms requires having complete information. This means that the ..."
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DNA microarray analysis has become the most widely used functional genomics approach in the bioinformatics field. Microarray gene expression data often contains missing values due to various reasons. Clustering gene expression data algorithms requires having complete information. This means that there shouldn't be any missing values. In this paper, a clustering method is proposed, called "Clustering Local Least Square Imputation method (ClustLLsimpute)", to estimate the missing values. In ClustLLsimpute, a complete dataset is obtained by removing each row with missing values. K clusters and their centroids are obtained by applying a non-parametric clustering technique on the complete dataset. Similar genes to the target gene (with missing values) are chosen as the smallest Euclidian distance to the centroids of each cluster. The target gene is represented as a linear combination of similar genes. Undertaken experiments proved that this algorithm is more accurate than the other algorithms, which have been introduced in the literature.
Michael L. MillerPerformance Monitoring of Run-to-Run Control Systems Used in Semiconductor Manufacturing
"... First of all, I wish to thank Dr. Edgar for providing me an opportunity to pursue my PhD degree at the University of Texas. He has been very patient with me and has kept me on track through the last four years. ..."
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First of all, I wish to thank Dr. Edgar for providing me an opportunity to pursue my PhD degree at the University of Texas. He has been very patient with me and has kept me on track through the last four years.
Linear Models with Generalized AR(1) Covariance Structure for . . .
, 1990
"... This work focuses on the study and development of a model for longitudinal data which accommodates irregularly-timed, inconsistently-timed, and randomly-missing data while taking into account the correlation between observations on the same individual with an AR(l) type of covariance structure. This ..."
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This work focuses on the study and development of a model for longitudinal data which accommodates irregularly-timed, inconsistently-timed, and randomly-missing data while taking into account the correlation between observations on the same individual with an AR(l) type of covariance structure. This model will be referred to as the model with generalized AR(l) covariance or as the model with exponentially decreasing correlation. Standard methods of analysis for these types of data, such as the General Linear Multivariate Model, require assuming that the data are consistently-timed between individuals. Further, observations with missing data would need to be discarded in order to use such a model. Another useful model for this type of data is the General Linear Model with ARMA covariance structure, as described by Rochon (1989). However, this model does not allow for unequally-spaced observations within individuals, although missing observations can be accommodated. Maximum Likelihood and Restricted Maximum Likelihood Estimators are derived for the parameters of the model with exponentially decreasing correlation and
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"... Identification of diagnostic biomarkers for infection in premature neonates ..."
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Identification of diagnostic biomarkers for infection in premature neonates
Identification of Diagnostic Biomarkers for Infection in Premature Neonates*
"... Infection is a leading cause of neonatal morbidity and mortality worldwide. Premature neonates are particu-larly susceptible to infection because of physiologic im-maturity, comorbidity, and extraneous medical interven-tions. Additionally premature infants are at higher risk of progression to sepsis ..."
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Infection is a leading cause of neonatal morbidity and mortality worldwide. Premature neonates are particu-larly susceptible to infection because of physiologic im-maturity, comorbidity, and extraneous medical interven-tions. Additionally premature infants are at higher risk of progression to sepsis or severe sepsis, adverse out-comes, and antimicrobial toxicity. Currently initial diag-nosis is based upon clinical suspicion accompanied by nonspecific clinical signs and is confirmed upon positive microbiologic culture results several days after institu-tion of empiric therapy. There exists a significant need for rapid, objective, in vitro tests for diagnosis of infec-tion in neonates who are experiencing clinical instabil-ity. We used immunoassays multiplexed on microarrays to identify differentially expressed serum proteins in
