Bayesian automatic relevance determination algorithms for classifying gene
expression data

Li, Yi; Campbell, Colin; Tipping, Michael E.

doi:10.1093/bioinformatics/18.10.1332

Cited by 156 publications

(103 citation statements)

References 14 publications

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“…Most recently, a Bayesian technique of automatic relevance determination, the use of support vector machines, and the use of ensembles of classifiers, all these either alone or in combination, have proved particularly promising. For further details see [Li et al (2002), Lu et al (2007), Chrysostomou et al (2008)] and the literature there. In the context of feature selection the last developments by the late Leo Breiman deserve special attention.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Feature Selection and Interdependency Discovery in Supervised Classification

Dramiński

Kierczak

Koronacki

et al. 2010

Advances in Machine Learning II

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Applications of machine learning techniques in Life Sciences are the main applications forcing a paradigm shift in the way these techniques are used. Rather than obtaining the best possible supervised classifier, the Life Scientist needs to know which features contribute best to classifying observations into distinct classes and what are the interdependencies between the features. To this end we significantly extend our earlier work [Dramiński et al. (2008)] that introduced an effective and reliable method for ranking features according to their importance for classification. We begin with adding a method for finding a cut-off between informative and non-informative features and then continue with a development of a methodology and an implementation of a procedure for determining interdependencies between informative features. The reliability of our approach rests on multiple construction of tree classifiers. Essentially, each classifier is trained on a randomly chosen subset of the original data using only a fraction of all of the observed features. This approach is conceptually simple yet computer-intensive. The methodology is validated on a large and difficult task of modelling HIV-1 reverse transcriptase resistance to drugs which is a good example of the aforementioned paradigm shift. We construct a classifier but of the main interest is the identification of mutation points (i.e. features) and their combinations that model drug resistance.

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Section: Introductionmentioning

confidence: 99%

Monte Carlo Feature Selection and Interdependency Discovery in Supervised Classification

Dramiński

Kierczak

Koronacki

et al. 2010

Advances in Machine Learning II

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show abstract

“…Unlike correlation-based approaches, which consider the significance of individual features, the RVM considers the significance of a feature in the context of the features already selected, which may be useful in considering the effects of combinations of features on gene expression. This approach has been successfully used to find a small number of genes whose expression is diagnostic for certain cancer types (Li et al 2002). The discriminatory features selected by the RVM classifier included promoter motifs that had known functions in both glucose-and ABA-activated gene expression and revealed that light-responsive promoter motifs were powerful features for classifying promoters controlling glucose down-regulated gene expression.…”

mentioning

confidence: 99%

Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine

Lee²,

Walsh³

et al. 2006

Genome Res.

233

253

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Establishing transcriptional regulatory networks by analysis of gene expression data and promoter sequences shows great promise. We developed a novel promoter classification method using a Relevance Vector Machine (RVM) and Bayesian statistical principles to identify discriminatory features in the promoter sequences of genes that can correctly classify transcriptional responses. The method was applied to microarray data obtained from Arabidopsis seedlings treated with glucose or abscisic acid (ABA). Of those genes showing >2.5-fold changes in expression level, ~70% were correctly predicted as being up- or down-regulated (under 10-fold cross-validation), based on the presence or absence of a small set of discriminative promoter motifs. Many of these motifs have known regulatory functions in sugar- and ABA-mediated gene expression. One promoter motif that was not known to be involved in glucose-responsive gene expression was identified as the strongest classifier of glucose-up-regulated gene expression. We show it confers glucose-responsive gene expression in conjunction with another promoter motif, thus validating the classification method. We were able to establish a detailed model of glucose and ABA transcriptional regulatory networks and their interactions, which will help us to understand the mechanisms linking metabolism with growth in Arabidopsis. This study shows that machine learning strategies coupled to Bayesian statistical methods hold significant promise for identifying functionally significant promoter sequences

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“…One of the issues we are addressing in this simplified context is the preprocessing (normalization) of gene expression data before the application of our classification procedures. Because of variabilities in gene expression measurements and uncertainties about the processing done by the tools used to generate the data, 7 we decided to include the effect of normalization as part of our studies. Specifically, for each data set we study, we attempt to learn via cross validation the most effective of a family of normalization parameters.…”

Section: Preprocessing the Data (Normalization)mentioning

confidence: 99%

A Bayesian Network Classification Methodology for Gene Expression Data

Helman¹,

Veroff²,

Atlas³

et al. 2004

J Comput Biol

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We present new techniques for the application of a Bayesian network learning framework to the problem of classifying gene expression data. The focus on classification permits us to develop techniques that address in several ways the complexities of learning Bayesian nets. Our classification model reduces the Bayesian network learning problem to the problem of learning multiple subnetworks, each consisting of a class label node and its set of parent genes. We argue that this classification model is more appropriate for the gene expression domain than are other structurally similar Bayesian network classification models, such as Naive Bayes and Tree Augmented Naive Bayes (TAN), because our model is consistent with prior domain experience suggesting that a relatively small number of genes, taken in different combinations, is required to predict most clinical classes of interest. Within this framework, we consider two different approaches to identifying parent sets which are supported by the gene expression observations and any other currently available evidence. One approach employs a simple greedy algorithm to search the universe of all genes; the second approach develops and applies a gene selection algorithm whose results are incorporated as a prior to enable an exhaustive search for parent sets over a restricted universe of genes. Two other significant contributions are the construction of classifiers from multiple, competing Bayesian network hypotheses and algorithmic methods for normalizing and binning gene expression data in the absence of prior expert knowledge. Our classifiers are developed under a cross validation regimen and then validated on corresponding out-of-sample test sets. The classifiers attain a classification rate in excess of 90% on out-of-sample test sets for two publicly available data sets. We present an extensive compilation of results reported in the literature for other classification methods run against these same two data sets. Our results are comparable to, or better than, any we have found reported for these two sets, when a train-test protocol as stringent as ours is followed.

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Bayesian automatic relevance determination algorithms for classifying gene expression data

Cited by 156 publications

References 14 publications

Monte Carlo Feature Selection and Interdependency Discovery in Supervised Classification

Monte Carlo Feature Selection and Interdependency Discovery in Supervised Classification

Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine

A Bayesian Network Classification Methodology for Gene Expression Data

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