1,561 research outputs found
Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data
Biomarkers which predict patient’s survival can play an important role in medical diagnosis and
treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in
survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers ofsurvival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce
dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were
located based on the position of optimized features. Kaplan-Meier curve and Cox regression model 2 were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to significantly associate with survival time
Gene Expression based Survival Prediction for Cancer Patients: A Topic Modeling Approach
Cancer is one of the leading cause of death, worldwide. Many believe that
genomic data will enable us to better predict the survival time of these
patients, which will lead to better, more personalized treatment options and
patient care. As standard survival prediction models have a hard time coping
with the high-dimensionality of such gene expression (GE) data, many projects
use some dimensionality reduction techniques to overcome this hurdle. We
introduce a novel methodology, inspired by topic modeling from the natural
language domain, to derive expressive features from the high-dimensional GE
data. There, a document is represented as a mixture over a relatively small
number of topics, where each topic corresponds to a distribution over the
words; here, to accommodate the heterogeneity of a patient's cancer, we
represent each patient (~document) as a mixture over cancer-topics, where each
cancer-topic is a mixture over GE values (~words). This required some
extensions to the standard LDA model eg: to accommodate the "real-valued"
expression values - leading to our novel "discretized" Latent Dirichlet
Allocation (dLDA) procedure. We initially focus on the METABRIC dataset, which
describes breast cancer patients using the r=49,576 GE values, from
microarrays. Our results show that our approach provides survival estimates
that are more accurate than standard models, in terms of the standard
Concordance measure. We then validate this approach by running it on the
Pan-kidney (KIPAN) dataset, over r=15,529 GE values - here using the mRNAseq
modality - and find that it again achieves excellent results. In both cases, we
also show that the resulting model is calibrated, using the recent
"D-calibrated" measure. These successes, in two different cancer types and
expression modalities, demonstrates the generality, and the effectiveness, of
this approach
A cDNA Microarray Gene Expression Data Classifier for Clinical Diagnostics Based on Graph Theory
Despite great advances in discovering cancer molecular profiles, the proper application of microarray technology to routine clinical diagnostics is still a challenge. Current practices in the classification of microarrays' data show two main limitations: the reliability of the training data sets used to build the classifiers, and the classifiers' performances, especially when the sample to be classified does not belong to any of the available classes. In this case, state-of-the-art algorithms usually produce a high rate of false positives that, in real diagnostic applications, are unacceptable. To address this problem, this paper presents a new cDNA microarray data classification algorithm based on graph theory and is able to overcome most of the limitations of known classification methodologies. The classifier works by analyzing gene expression data organized in an innovative data structure based on graphs, where vertices correspond to genes and edges to gene expression relationships. To demonstrate the novelty of the proposed approach, the authors present an experimental performance comparison between the proposed classifier and several state-of-the-art classification algorithm
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
Identification of an Efficient Gene Expression Panel for Glioblastoma Classification.
We present here a novel genetic algorithm-based random forest (GARF) modeling technique that enables a reduction in the complexity of large gene disease signatures to highly accurate, greatly simplified gene panels. When applied to 803 glioblastoma multiforme samples, this method allowed the 840-gene Verhaak et al. gene panel (the standard in the field) to be reduced to a 48-gene classifier, while retaining 90.91% classification accuracy, and outperforming the best available alternative methods. Additionally, using this approach we produced a 32-gene panel which allows for better consistency between RNA-seq and microarray-based classifications, improving cross-platform classification retention from 69.67% to 86.07%. A webpage producing these classifications is available at http://simplegbm.semel.ucla.edu
Effect Size Estimation and Misclassification Rate Based Variable Selection in Linear Discriminant Analysis
Supervised classifying of biological samples based on genetic information,
(e.g. gene expression profiles) is an important problem in biostatistics. In
order to find both accurate and interpretable classification rules variable
selection is indispensable. This article explores how an assessment of the
individual importance of variables (effect size estimation) can be used to
perform variable selection. I review recent effect size estimation approaches
in the context of linear discriminant analysis (LDA) and propose a new
conceptually simple effect size estimation method which is at the same time
computationally efficient. I then show how to use effect sizes to perform
variable selection based on the misclassification rate which is the data
independent expectation of the prediction error. Simulation studies and real
data analyses illustrate that the proposed effect size estimation and variable
selection methods are competitive. Particularly, they lead to both compact and
interpretable feature sets.Comment: 21 pages, 2 figure
Partial Least Squares: A Versatile Tool for the Analysis of High-Dimensional Genomic Data
Partial Least Squares (PLS) is a highly efficient statistical regression technique that is well suited for the analysis of high-dimensional genomic data. In this paper we review the theory and applications of PLS both under methodological and biological points of view. Focusing on microarray expression data we provide a systematic comparison of the PLS approaches currently employed, and discuss problems as different as tumor classification, identification of relevant genes, survival analysis and modeling of gene networks
Deducing corticotropin-releasing hormone receptor type 1 signaling networks from gene expression data by usage of genetic algorithms and graphical Gaussian models
<p>Abstract</p> <p>Background</p> <p>Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis is a hallmark of complex and multifactorial psychiatric diseases such as anxiety and mood disorders. About 50-60% of patients with major depression show HPA axis dysfunction, i.e. hyperactivity and impaired negative feedback regulation. The neuropeptide corticotropin-releasing hormone (CRH) and its receptor type 1 (CRHR1) are key regulators of this neuroendocrine stress axis. Therefore, we analyzed CRH/CRHR1-dependent gene expression data obtained from the pituitary corticotrope cell line AtT-20, a well-established <it>in vitro </it>model for CRHR1-mediated signal transduction. To extract significantly regulated genes from a genome-wide microarray data set and to deduce underlying CRHR1-dependent signaling networks, we combined supervised and unsupervised algorithms.</p> <p>Results</p> <p>We present an efficient variable selection strategy by consecutively applying univariate as well as multivariate methods followed by graphical models. First, feature preselection was used to exclude genes not differentially regulated over time from the dataset. For multivariate variable selection a maximum likelihood (MLHD) discriminant function within GALGO, an R package based on a genetic algorithm (GA), was chosen. The topmost genes representing major nodes in the expression network were ranked to find highly separating candidate genes. By using groups of five genes (chromosome size) in the discriminant function and repeating the genetic algorithm separately four times we found eleven genes occurring at least in three of the top ranked result lists of the four repetitions. In addition, we compared the results of GA/MLHD with the alternative optimization algorithms greedy selection and simulated annealing as well as with the state-of-the-art method random forest. In every case we obtained a clear overlap of the selected genes independently confirming the results of MLHD in combination with a genetic algorithm.</p> <p>With two unsupervised algorithms, principal component analysis and graphical Gaussian models, putative interactions of the candidate genes were determined and reconstructed by literature mining. Differential regulation of six candidate genes was validated by qRT-PCR.</p> <p>Conclusions</p> <p>The combination of supervised and unsupervised algorithms in this study allowed extracting a small subset of meaningful candidate genes from the genome-wide expression data set. Thereby, variable selection using different optimization algorithms based on linear classifiers as well as the nonlinear random forest method resulted in congruent candidate genes. The calculated interacting network connecting these new target genes was bioinformatically mapped to known CRHR1-dependent signaling pathways. Additionally, the differential expression of the identified target genes was confirmed experimentally.</p
Leukemia and small round blue-cell tumor cancer detection using microarray gene expression data set: Combining data dimension reduction and variable selection technique
Using gene expression data in cancer classification plays an important role for solving the fundamental problems
relating to cancer diagnosis. Because of high throughput of gene expression data for healthy and patient samples,
a variable selection method can be applied to reduce complexity of the model and improve the classification
performance. Since variable selection procedures pose a risk of over-fitting, when a large number of variables
with respect to sample are used,we have proposed a method for coupling data dimension reduction and variable
selection in the present study. This approach uses the concept of variable clustering for the original data set.
Significant components of local principal component analysis models have just been retained from all clusters.
Then, the variable selection algorithm is performed on these locally derived principal component variables.
The proposed algorithm has been evaluated on two gene expression data sets; namely, acute Leukemia and
small round blue-cell tumor (SRBCT). Our results confirmed that the classification models achieved on the
reduced data were better than those obtained on the entire microarray gene expression profile
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