SHrinkage Covariance Estimation Incorporating Prior Biological Knowledge with Applications to High-Dimensional Data

In ``-omic data'' analysis, information on the structure of covariates are broadly available either from public databases describing gene regulation processes and functional groups such as the Kyoto encyclopedia of genes and genomes (KEGG), or from statistical analyses -- for example in form of partial correlation estimators. The analysis of transcriptomic data might benefit from the incorporation of such prior knowledge. In this paper we focus on the integration of structured information into statistical analyses in which at least one major step involves the estimation of a (high-dimensional) covariance matrix. More precisely, we revisit the recently proposed ``SHrinkage Incorporating Prior'' (SHIP) covariance estimation method which takes into account the group structure of the covariates, and suggest to integrate the SHIP covariance estimator into various multivariate methods such as linear discriminant analysis (LDA), global analysis of covariance (GlobalANCOVA), and regularized generalized canonical correlation analysis (RGCCA). We demonstrate the use of the resulting new methods based on simulations and discuss the benefit of the integration of prior information through the SHIP estimator. Reproducible R codes are available at http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/shipproject/index.html

SHrinkage Covariance Estimation Incorporating Prior Biological Knowledge with Applications to High-Dimensional Data

In ``-omic data'' analysis, information on the structure of covariates are broadly available either from public databases describing gene regulation processes and functional groups such as the Kyoto encyclopedia of genes and genomes (KEGG), or from statistical analyses -- for example in form of partial correlation estimators. The analysis of transcriptomic data might benefit from the incorporation of such prior knowledge. In this paper we focus on the integration of structured information into statistical analyses in which at least one major step involves the estimation of a (high-dimensional) covariance matrix. More precisely, we revisit the recently proposed ``SHrinkage Incorporating Prior'' (SHIP) covariance estimation method which takes into account the group structure of the covariates, and suggest to integrate the SHIP covariance estimator into various multivariate methods such as linear discriminant analysis (LDA), global analysis of covariance (GlobalANCOVA), and regularized generalized canonical correlation analysis (RGCCA). We demonstrate the use of the resulting new methods based on simulations and discuss the benefit of the integration of prior information through the SHIP estimator. Reproducible R codes are available at http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/shipproject/index.html

Alternating direction method of multipliers for penalized zero-variance discriminant analysis

We consider the task of classification in the high dimensional setting where the number of features of the given data is significantly greater than the number of observations. To accomplish this task, we propose a heuristic, called sparse zero-variance discriminant analysis (SZVD), for simultaneously performing linear discriminant analysis and feature selection on high dimensional data. This method combines classical zero-variance discriminant analysis, where discriminant vectors are identified in the null space of the sample within-class covariance matrix, with penalization applied to induce sparse structures in the resulting vectors. To approximately solve the resulting nonconvex problem, we develop a simple algorithm based on the alternating direction method of multipliers. Further, we show that this algorithm is applicable to a larger class of penalized generalized eigenvalue problems, including a particular relaxation of the sparse principal component analysis problem. Finally, we establish theoretical guarantees for convergence of our algorithm to stationary points of the original nonconvex problem, and empirically demonstrate the effectiveness of our heuristic for classifying simulated data and data drawn from applications in time-series classification

Gradient-orientation-based PCA subspace for novel face recognition

This article has been made available through the Brunel Open Access Publishing Fund.Face recognition is an interesting and a challenging problem that has been widely studied in the field of pattern recognition and computer vision. It has many applications such as biometric authentication, video surveillance, and others. In the past decade, several methods for face recognition were proposed. However, these methods suffer from pose and illumination variations. In order to address these problems, this paper proposes a novel methodology to recognize the face images. Since image gradients are invariant to illumination and pose variations, the proposed approach uses gradient orientation to handle these effects. The Schur decomposition is used for matrix decomposition and then Schurvalues and Schurvectors are extracted for subspace projection. We call this subspace projection of face features as Schurfaces, which is numerically stable and have the ability of handling defective matrices. The Hausdorff distance is used with the nearest neighbor classifier to measure the similarity between different faces. Experiments are conducted with Yale face database and ORL face database. The results show that the proposed approach is highly discriminant and achieves a promising accuracy for face recognition than the state-of-the-art approaches

Evaluation of linear classifiers on articles containing pharmacokinetic evidence of drug-drug interactions

Background. Drug-drug interaction (DDI) is a major cause of morbidity and mortality. [...] Biomedical literature mining can aid DDI research by extracting relevant DDI signals from either the published literature or large clinical databases. However, though drug interaction is an ideal area for translational research, the inclusion of literature mining methodologies in DDI workflows is still very preliminary. One area that can benefit from literature mining is the automatic identification of a large number of potential DDIs, whose pharmacological mechanisms and clinical significance can then be studied via in vitro pharmacology and in populo pharmaco-epidemiology. Experiments. We implemented a set of classifiers for identifying published articles relevant to experimental pharmacokinetic DDI evidence. These documents are important for identifying causal mechanisms behind putative drug-drug interactions, an important step in the extraction of large numbers of potential DDIs. We evaluate performance of several linear classifiers on PubMed abstracts, under different feature transformation and dimensionality reduction methods. In addition, we investigate the performance benefits of including various publicly-available named entity recognition features, as well as a set of internally-developed pharmacokinetic dictionaries. Results. We found that several classifiers performed well in distinguishing relevant and irrelevant abstracts. We found that the combination of unigram and bigram textual features gave better performance than unigram features alone, and also that normalization transforms that adjusted for feature frequency and document length improved classification. For some classifiers, such as linear discriminant analysis (LDA), proper dimensionality reduction had a large impact on performance. Finally, the inclusion of NER features and dictionaries was found not to help classification.Comment: Pacific Symposium on Biocomputing, 201