15,492 research outputs found
Accurate molecular classification of cancer using simple rules
<p>Abstract</p> <p>Background</p> <p>One intractable problem with using microarray data analysis for cancer classification is how to reduce the extremely high-dimensionality gene feature data to remove the effects of noise. Feature selection is often used to address this problem by selecting informative genes from among thousands or tens of thousands of genes. However, most of the existing methods of microarray-based cancer classification utilize too many genes to achieve accurate classification, which often hampers the interpretability of the models. For a better understanding of the classification results, it is desirable to develop simpler rule-based models with as few marker genes as possible.</p> <p>Methods</p> <p>We screened a small number of informative single genes and gene pairs on the basis of their depended degrees proposed in rough sets. Applying the decision rules induced by the selected genes or gene pairs, we constructed cancer classifiers. We tested the efficacy of the classifiers by leave-one-out cross-validation (LOOCV) of training sets and classification of independent test sets.</p> <p>Results</p> <p>We applied our methods to five cancerous gene expression datasets: leukemia (acute lymphoblastic leukemia [ALL] vs. acute myeloid leukemia [AML]), lung cancer, prostate cancer, breast cancer, and leukemia (ALL vs. mixed-lineage leukemia [MLL] vs. AML). Accurate classification outcomes were obtained by utilizing just one or two genes. Some genes that correlated closely with the pathogenesis of relevant cancers were identified. In terms of both classification performance and algorithm simplicity, our approach outperformed or at least matched existing methods.</p> <p>Conclusion</p> <p>In cancerous gene expression datasets, a small number of genes, even one or two if selected correctly, is capable of achieving an ideal cancer classification effect. This finding also means that very simple rules may perform well for cancerous class prediction.</p
Extreme Value Distribution Based Gene Selection Criteria for Discriminant Microarray Data Analysis Using Logistic Regression
One important issue commonly encountered in the analysis of microarray data
is to decide which and how many genes should be selected for further studies.
For discriminant microarray data analyses based on statistical models, such as
the logistic regression models, gene selection can be accomplished by a
comparison of the maximum likelihood of the model given the real data,
, and the expected maximum likelihood of the model given an
ensemble of surrogate data with randomly permuted label, .
Typically, the computational burden for obtaining is immense,
often exceeding the limits of computing available resources by orders of
magnitude. Here, we propose an approach that circumvents such heavy
computations by mapping the simulation problem to an extreme-value problem. We
present the derivation of an asymptotic distribution of the extreme-value as
well as its mean, median, and variance. Using this distribution, we propose two
gene selection criteria, and we apply them to two microarray datasets and three
classification tasks for illustration.Comment: to be published in Journal of Computational Biology (2004
Optimal classifier selection and negative bias in error rate estimation: An empirical study on high-dimensional prediction
In biometric practice, researchers often apply a large number of different methods in a "trial-and-error" strategy to get as much as possible out of their data and, due to publication pressure or pressure from the consulting customer, present only the most favorable results. This strategy may induce a substantial optimistic bias in prediction error estimation, which is quantitatively assessed in the present manuscript. The focus of our work is on class prediction based on high-dimensional data (e.g. microarray data), since such analyses are particularly exposed to this kind of bias.
In our study we consider a total of 124 variants of classifiers (possibly including variable selection or tuning steps) within a cross-validation evaluation scheme. The classifiers are applied to original and modified real microarray data sets, some of which are obtained by randomly permuting the class labels to mimic non-informative predictors while preserving their correlation structure. We then assess the minimal misclassification rate over the different variants of classifiers in order to quantify the bias arising when the optimal classifier is selected a posteriori in a data-driven manner. The bias resulting from the parameter tuning (including gene selection parameters as a special case) and the bias resulting from the choice of the classification method are examined both separately and jointly.
We conclude that the strategy to present only the optimal result is not acceptable, and suggest alternative approaches for properly reporting classification accuracy
PLS dimension reduction for classification of microarray data
PLS dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, PLS is compared with some of the best state-of-the-art classification methods. In addition, a simple procedure to choose the number of components is suggested. The connection between PLS dimension reduction and gene selection is examined and a property of the first PLS component for binary classification is proven. PLS can also be used as a visualization tool for high-dimensional data in the classification framework. The whole study is based on 9 real microarray cancer data sets
maigesPack: A Computational Environment for Microarray Data Analysis
Microarray technology is still an important way to assess gene expression in
molecular biology, mainly because it measures expression profiles for thousands
of genes simultaneously, what makes this technology a good option for some
studies focused on systems biology. One of its main problem is complexity of
experimental procedure, presenting several sources of variability, hindering
statistical modeling. So far, there is no standard protocol for generation and
evaluation of microarray data. To mitigate the analysis process this paper
presents an R package, named maigesPack, that helps with data organization.
Besides that, it makes data analysis process more robust, reliable and
reproducible. Also, maigesPack aggregates several data analysis procedures
reported in literature, for instance: cluster analysis, differential
expression, supervised classifiers, relevance networks and functional
classification of gene groups or gene networks
Computational Models for Transplant Biomarker Discovery.
Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems
Rank discriminants for predicting phenotypes from RNA expression
Statistical methods for analyzing large-scale biomolecular data are
commonplace in computational biology. A notable example is phenotype prediction
from gene expression data, for instance, detecting human cancers,
differentiating subtypes and predicting clinical outcomes. Still, clinical
applications remain scarce. One reason is that the complexity of the decision
rules that emerge from standard statistical learning impedes biological
understanding, in particular, any mechanistic interpretation. Here we explore
decision rules for binary classification utilizing only the ordering of
expression among several genes; the basic building blocks are then two-gene
expression comparisons. The simplest example, just one comparison, is the TSP
classifier, which has appeared in a variety of cancer-related discovery
studies. Decision rules based on multiple comparisons can better accommodate
class heterogeneity, and thereby increase accuracy, and might provide a link
with biological mechanism. We consider a general framework ("rank-in-context")
for designing discriminant functions, including a data-driven selection of the
number and identity of the genes in the support ("context"). We then specialize
to two examples: voting among several pairs and comparing the median expression
in two groups of genes. Comprehensive experiments assess accuracy relative to
other, more complex, methods, and reinforce earlier observations that simple
classifiers are competitive.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS738 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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