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Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset

COMPUTERS & OPERATIONS RESEARCH 2010年第8期37卷 1395-1405页

作者： Archetti, Francesco Giordani, Ilaria Vanneschi, Leonardo Univ Milano Bicocca DISCo I-20126 Milan Italy Consorzio MilanoRic I-20126 Milan Italy

Statistical methods, and in particular machine learning, have been increasingly used in the drug development workflow. Among the existing machine learning methods, we have been specifically concerned with genetic programming. We present a genetic programming-based framework for predicting anticancer therapeutic response. We use the NCI-60 microarray dataset and we look for a relationship between gene expressions and responses to oncology drugs Fluorouracil, Fludarabine, Floxuridine and Cytarabine. We aim at identifying, from genomic measurements of biopsies, the likelihood to develop drug resistance. Experimental results. and their comparison with the ones obtained by Linear Regression and Least Square Regression, hint that genetic programming is a promising technique for this kind of application. Moreover, genetic programming output may potentially highlight some relations between genes which could support the identification of biological meaningful pathways. The structures that appear more frequently in the "best" solutions found by genetic programming are presented. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Genetic programming Machine learning Regression microarray data Anticancer therapy NCI-60

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A Robust Method for Large-Scale Multiple Hypotheses Testing

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BIOMETRICAL JOURNAL 2010年第2期52卷 222-232页

作者： Han, Seungbong Andrei, Adin-Cristian Tsui, Kam-Wah Univ Wisconsin Dept Biostat & Med Informat Madison WI 53792 USA Univ Wisconsin Dept Stat Med Sci Ctr Madison WI 53706 USA

When drawing large-scale simultaneous inference, such as in genomics and imaging problems, multiplicity adjustments should be made, since, otherwise, one would be faced with an inflated type I error. Numerous methods are available to estimate the proportion of true null hypotheses no, among a large number of hypotheses tested. Many methods implicitly assume that the no is large, that is, close to 1. However, in practice, mid-range no values are frequently encountered and many of the widely used methods tend to produce highly variable or biased estimates of no. As a remedy in such situations, we propose a hierarchical Bayesian model that produces an estimator of no that exhibits considerably less bias and is more stable. Simulation studies seem indicative of good method performance even when low-to-moderate correlation exists among test statistics. Method performance is assessed in simulated settings and its practical usefulness is illustrated in an application to a type II diabetes study.

关键词： Bayesian framework microarray data Mixture model Multiple testing Robustness

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An elastic network model to identify characteristic stress response genes

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2010年第3期34卷 193-202页

作者： Schneckener, Sebastian Goerlitz, Linus Ellinger-Ziegelbauer, Heid Run Ahr, Hans-Juergen Schuppert, Andreas Bayer Technol Serv PT AS SBCS D-51368 Leverkusen Germany Bayer Schering Pharma AG Dept Special Toxicol D-42096 Wuppertal Germany Rhein Westfal TH Aachen D-52056 Aachen Germany

Exposing eukaryotic cells to a toxic compound and subsequent gene expression profiling may allow the prediction of selected toxic effects based on changes in gene expression. This objective is complicated by the observation that compounds with different modes of toxicity cause similar changes in gene expression and that a global stress response affects many genes. We developed an elastic network model of global stress response with nodes representing genes which are connected by edges of graded coexpression. The expression of only few genes have to be known to model the global stress response of all but a few atypical responder genes. Those required genes and the atypical response genes are shown to be good biomarker for fox predictions. In total, 138 experiments and 13 different compounds were used to train models for different toxicity classes. The deduced biomarkers were shown to be biologically plausible. A neural network was trained to predict the toxic effects of compounds from profiling experiments. On a validation data set of 189 experiments with 16 different compounds the accuracy of the predictions was assessed: 14 out of 16 compounds have been classified correctly. Derivation of model based biomarkers through the elastic network approach can naturally be extended to other areas beyond toxicology since subtle signals against a broad response background are common in biological studies. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Gene expression microarray data Toxicology prediction Stress response Pharmacology Drug development

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Ensemble gene selection for cancer classification

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PATTERN RECOGNITION 2010年第8期43卷 2763-2772页

作者： Liu, Huawen Liu, Lei Zhang, Huijie Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China NE Normal Univ Dept Comp Sci Changchun 130021 Peoples R China

Cancer diagnosis is an important emerging clinical application of microarray data. Its accurate prediction to the type or size of tumors relies on adopting powerful and reliable classification models, so as to patients can be provided with better treatment or response to therapy. However, the high dimensionality of microarray data may bring some disadvantages, such as over-fitting, poor performance and low efficiency, to traditional classification models. Thus, one of the challenging tasks in cancer diagnosis is how to identify salient expression genes from thousands of genes in microarray data that can directly contribute to the phenotype or symptom of disease. In this paper, we propose a new ensemble gene selection method (EGS) to choose multiple gene subsets for classification purpose, where the significant degree of gene is measured by conditional mutual information or its normalized form. After different gene subsets have been obtained by setting different starting points of the search procedure, they will be used to train multiple base classifiers and then aggregated into a consensus classifier by the manner of majority voting. The proposed method is compared with five popular gene selection methods on six public microarray datasets and the comparison results show that our method works well. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Gene selection Ensemble learning Cancer classification Mutual information microarray data

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A GMM-IG framework for selecting genes as expression panel biomarkers

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2010年第2-3期48卷 75-82页

作者： Wang, Mingyi Chen, Jake Y. Purdue Univ Sch Sci Dept Comp & Informat Sci Indianapolis IN 46202 USA Indiana Univ Sch Informat Indianapolis IN 46202 USA Indiana Ctr Syst Biol & Personalized Med Indianapolis IN 46202 USA

Objective: The limitation of small sample size of functional genomics experiments has made it necessary to integrate DNA microarray experimental data from different sources. However, experimentation noises and biases of different microarray platforms have made integrated data analysis challenging. In this work, we propose an integrative computational framework to identify candidate biomarker genes from publicly available functional genomics studies. Methods: We developed a new framework, Gaussian Mixture Modeling-Coupled Information Gain (GMM-IG). In this framework, we first apply a two-component Gaussian mixture model (GMM) to estimate the conditional probability distributions of gene expression data between two different types of samples, for example, normal versus cancer. An expectation-maximization algorithm is then used to estimate the maximum likelihood parameters of a mixture of two Gaussian models in the feature space and determine the underlying expression levels of genes. Gene expression results from different studies are discretized, based on GMM estimations and then unified. Significantly differentially-expressed genes are filtered and assessed with information gain (IG) measures. Results: DNA microarray experimental data for lung cancers from three different prior studies was processed using the new GMM-IG method. Target gene markers from a gene expression panel were selected and compared with several conventional computational biomarker data analysis methods. GMM-IG showed consistently high accuracy for several classification assessments. A high reproducibility of gene selection results was also determined from statistical validations. Our study shows that the GMM-IG framework can overcome poor reliability issues from single-study DNA microarray experiment while maintaining high accuracies by combining true signals from multiple studies. Conclusions: We present a conceptually simple framework that enables reliable integration of true differential gene

关键词： Gene selection data integration microarray data Lung cancer Gaussian mixture model Information gain

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THE NEW HYBRID METHOD FOR CLASSIFICATION OF PATIENTS BY GENE EXPRESSION PROFILING

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JOURNAL OF INTEGRATED DESIGN & PROCESS SCIENCE 2010年第2期14卷 27-42页

作者： Cosgun, Erdal Karaagaoglu, Ergun Hacettepe Univ Fac Med Dept Biostat Ankara Turkey

Genetic researches have gradually become an area which is intensively studied on in recent years. The reason of that is the fact that a lot of diseases and features are transferred to the other generations by genes. These transfers are generally at the base of diseases. The evaluation of the input which is reached as the result of the researches is also accepted as a separate field. The aim of this study is to develop a model which enables the best classification of the patients by DNA microarray expression inputs. For this purpose, the classification which is based on Unsupervised Learning has mainly been used, by bringing together various methods. The Independent Components Analysis is used for dimension reduction, Kohonen Map Method is used for clustering and Random Forest Method is used for classification purposes. The model which is formed by combining these methods and very popular classification method Support Vector Machines (SVMs) has been studied and their classification performance is compared by True Classification Rate (TCR) on two real publicity data sets. The highest value that TCR can take on is one. The aim is to close this value to one. By the help of the model proposed in this study, we expect a reduction in the cost of these researches and aim to prevent wrong diagnoses as much as possible.

关键词： data mining random forest independent component analysis Kohonen map bootstrap classification clustering dimension reduction microarray data

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Projection-pursuit approach to robust linear discriminant analysis

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JOURNAL OF MULTIVARIATE ANALYSIS 2010年第10期101卷 2464-2485页

作者： Pires, Ana M. Branco, Joao A. Univ Tecn Lisboa Dept Math P-1049001 Lisbon Portugal Univ Tecn Lisboa CEMAT IST P-1049001 Lisbon Portugal

Discriminant analysis plays an important role in multivariate statistics as a prediction and classification method. It has been successfully applied in many fields of work and research. As it happens with other multivariate methods, discriminant analysis is highly vulnerable to the presence of outliers that commonly occur in many real world data sets. The lack of robustness of the classical estimators on which the linear discriminant function is based is a severe disadvantage and several authors have worked to find efficient ways to prevent the damage that outliers can cause. This paper focuses on the projection-pursuit approach to discriminant analysis. The projection-pursuit estimators are described and theoretical properties are deduced and their relevance is highlighted. These include Fisher consistency, affine equivariance, partial influence functions and asymptotic distributions. Application to real data and a simulation study reveal the robustness of the projection-pursuit approach. In both analyses the data relates to a large number of variables, a situation that is becoming common when new technology is applied to data gathering. (c) 2010 Elsevier Inc. All rights reserved.

关键词： Projection-pursuit Robustness Linear discriminant analysis Influence function Asymptotic variance High-dimensional data microarray data

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Survival analysis with high-dimensional covariates (vol 19, pg 29, 2010)

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STATISTICAL METHODS IN MEDICAL RESEARCH 2010年第2期19卷 200-200页

作者： Witten, Daniela M. Tibshirani, Robert Department of Statistics Stanford University Stanford CA 94305 USA dwitten{at}stanford.edu Departments of Health Research and Policy & Statistics Stanford University Stanford CA 94305 USA

Daniela M. Witten and Robert Tibshirani. Survival analysis with high-dimensional covariates. Statistical Methods in Medical Research 2010;19: 29-51 (DOI: 10.1177/0962280209105024). On page 36, Figure 1 is incorrect. Please note the following correction in the figure: The 'Modified Cox' and 'LPC' legends in the artwork were reversed. The correct figure is given below.

关键词： GENE-EXPRESSION data GENOME-WIDE ASSOCIATION B-CELL LYMPHOMA microarray data PRINCIPAL COMPONENTS COX REGRESSION VARIABLE SELECTION STATISTICAL TESTS PREDICT SURVIVAL BREAST-CANCER

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Infinite Latent Process Decomposition

Infinite Latent Process Decomposition

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Masada, Tomonari Shibata, Yuichiro Oguri, Kiyoshi Nagasaki Univ Dept Comp & Informat Sci Nagasaki Japan

ISBN: (纸本)9781424483044

This paper presents infinite latent process decomposition (iLPD), a new microarray analysis method, as an extension of latent process decomposition in Our method assumes an infinite number of latent processes. Further, our new collapsed variational Bayesian inference improves the inference proposed in [2] in the treatment of Dirichlet hyperparameters. We also give the results of the comparison experiment.

关键词： microarray data Bayesian nonparametrics

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Analyzing Genetic Factors Involved in Recombinant Protein Expression Enhancement

Analyzing Genetic Factors Involved in Recombinant Protein Ex...

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Johnson, Daniel Teoh, Keat Ashby, Cody Hood, Elizabeth Huang, Xiuzhen Arkansas State Univ Jonesboro AR 72467 USA

ISBN: (纸本)9781424483044

Understanding the genetic factors that promote recombinant protein accumulation in transgenic plants will provide insightful strategies for protein biofactory efficiency. Through combining biological and bioinformatics analysis, our work is to determine genetic and biological factors affecting increased protein accumulation of a bacterial cellulase enzyme in transgenic maize. microarray experiments were performed on maize near-isogenic lines that exhibit high and low accumulation of the enzyme expressed from a transgene in seeds. Through microarray data analysis, two thousand three hundred thirteen genes were identified which exhibited at least a 1.5-fold change in expression level. One hundred sixty-one genes from the data set are shown to be statistically valid. Of these, eighty-two genes are upregulated while seventy-nine genes are down-regulated. Preliminary functional analysis of the genes was conducted and several pathways of biological importance were tentatively identified. These genes code for four categories of proteins: known proteins, zein proteins, putative proteins and unknown proteins. Further functional clustering and annotation analysis for these genes will help construct and define networks of interaction as well as predict important metabolic pathways to understand the controlling mechanisms that lead to the hyper-accumulation phenomenon.

关键词： gene expression microarray data recombinant protein accumulation

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