9,530 research outputs found
Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction
<b>Background</b>
The widely used k top scoring pair (k-TSP) algorithm is a simple yet powerful parameter-free classifier. It owes its success in many cancer microarray datasets to an effective feature selection algorithm that is based on relative expression ordering of gene pairs. However, its general robustness does not extend to some difficult datasets, such as those involving cancer outcome prediction, which may be due to the relatively simple voting scheme used by the classifier. We believe that the performance can be enhanced by separating its effective feature selection component and combining it with a powerful classifier such as the support vector machine (SVM). More generally the top scoring pairs generated by the k-TSP ranking algorithm can be used as a dimensionally reduced subspace for other machine learning classifiers.<p></p>
<b>Results</b>
We developed an approach integrating the k-TSP ranking algorithm (TSP) with other machine learning methods, allowing combination of the computationally efficient, multivariate feature ranking of k-TSP with multivariate classifiers such as SVM. We evaluated this hybrid scheme (k-TSP+SVM) in a range of simulated datasets with known data structures. As compared with other feature selection methods, such as a univariate method similar to Fisher's discriminant criterion (Fisher), or a recursive feature elimination embedded in SVM (RFE), TSP is increasingly more effective than the other two methods as the informative genes become progressively more correlated, which is demonstrated both in terms of the classification performance and the ability to recover true informative genes. We also applied this hybrid scheme to four cancer prognosis datasets, in which k-TSP+SVM outperforms k-TSP classifier in all datasets, and achieves either comparable or superior performance to that using SVM alone. In concurrence with what is observed in simulation, TSP appears to be a better feature selector than Fisher and RFE in some of the cancer datasets.<p></p>
<b>Conclusions</b>
The k-TSP ranking algorithm can be used as a computationally efficient, multivariate filter method for feature selection in machine learning. SVM in combination with k-TSP ranking algorithm outperforms k-TSP and SVM alone in simulated datasets and in some cancer prognosis datasets. Simulation studies suggest that as a feature selector, it is better tuned to certain data characteristics, i.e. correlations among informative genes, which is potentially interesting as an alternative feature ranking method in pathway analysis
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
rfTSP: A Non-parametric predictive model with order-based feature selection for transcriptomic data
Genomic data has strong potential to predict biologic classifications using gene expression data. For example, tumor subtype can be determined using machine learning models and gene expression profiles. We propose the use of Top Scoring Pairs in combination with machine learning to improve inter-study prediction of genomic profiles. Inter-study prediction refers to two studies that are completely independent either in terms of platform or tissue. Top Scoring Pairs (TSPs) rank pairs of genes according to how well they are expressed between different groups of subjects. For example, gene A will be lowly expressed in cases, and gene B will be highly expressed in controls, while gene A will be highly expressed in controls, and gene B will be lowly expressed in cases. The pairs demonstrate an inverse relationship with respect to one and another. Using TSPs act not only as a feature selection step, but also allows for a non parametric method that transforms the continuous expression data to 0,1, which is based on the rank of the pairs. Due to the robust nature of the transformed data, our methods demonstrate that the use of TSP binary data is much more effective in prediction than continuous data, particularly in cross study prediction. Furthermore, we extend the use of TSPs to not only binary and multi-class label prediction, but also continuous classification. The objective of this paper is to demonstrate how using dichotomized data from TSPs as the feature space for machine learning methods, particularly random forest, returns stronger prediction accuracy across independent studies than traditional machine learning techniques with log2 and quantile normalization of data. This work has significant public health impact as accurate genomic prediction is crucial for early detection of many serious illnesses such as cancer
A Pairwise Feature Selection Method For Gene Data Using Information Gain
The current technical practice for doing classification has limitations when using gene expression microarray data. For example, the robustness of top scoring pairs does not extend to some datasets involving small data size and the gene set with best discrimination power may not be involve a combination of genes. Hence, it is necessary to construct a discriminative and stable classifier that generates highly informative gene sets. As we know, not all the features will be active in a biological process. So a good feature selector should be robust with respect to noise and outliers; the challenge is to select the most informative genes. In this study, the top discriminating pair (TDP) approach is motivated by this issue and aims to reveal which features are highly ranked according to their discrimination power. To identify TDPS, each pair of genes is assigned a score based on their relative probability distribution. Our experiment combines the TDP methodology with information gain (ig) to achieve an effective feature set. To illustrate the effectiveness of TDP with ig, we applied this method to two breast cancer datasets (Wang et al., 2005 and Van\u27t Veer et al., 2002). The result from these experimental datasets using the TDP method is competitive with the baseline method using random forests. Information gain combined with the TDP algorithm used in this study provides a new effective method for feature selection for machine learning
The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures
Motivation: Biomarker discovery from high-dimensional data is a crucial
problem with enormous applications in biology and medicine. It is also
extremely challenging from a statistical viewpoint, but surprisingly few
studies have investigated the relative strengths and weaknesses of the plethora
of existing feature selection methods. Methods: We compare 32 feature selection
methods on 4 public gene expression datasets for breast cancer prognosis, in
terms of predictive performance, stability and functional interpretability of
the signatures they produce. Results: We observe that the feature selection
method has a significant influence on the accuracy, stability and
interpretability of signatures. Simple filter methods generally outperform more
complex embedded or wrapper methods, and ensemble feature selection has
generally no positive effect. Overall a simple Student's t-test seems to
provide the best results. Availability: Code and data are publicly available at
http://cbio.ensmp.fr/~ahaury/
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An Overview of the Use of Neural Networks for Data Mining Tasks
In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks
Statistical methods for tissue array images - algorithmic scoring and co-training
Recent advances in tissue microarray technology have allowed
immunohistochemistry to become a powerful medium-to-high throughput analysis
tool, particularly for the validation of diagnostic and prognostic biomarkers.
However, as study size grows, the manual evaluation of these assays becomes a
prohibitive limitation; it vastly reduces throughput and greatly increases
variability and expense. We propose an algorithm - Tissue Array Co-Occurrence
Matrix Analysis (TACOMA) - for quantifying cellular phenotypes based on
textural regularity summarized by local inter-pixel relationships. The
algorithm can be easily trained for any staining pattern, is absent of
sensitive tuning parameters and has the ability to report salient pixels in an
image that contribute to its score. Pathologists' input via informative
training patches is an important aspect of the algorithm that allows the
training for any specific marker or cell type. With co-training, the error rate
of TACOMA can be reduced substantially for a very small training sample (e.g.,
with size 30). We give theoretical insights into the success of co-training via
thinning of the feature set in a high-dimensional setting when there is
"sufficient" redundancy among the features. TACOMA is flexible, transparent and
provides a scoring process that can be evaluated with clarity and confidence.
In a study based on an estrogen receptor (ER) marker, we show that TACOMA is
comparable to, or outperforms, pathologists' performance in terms of accuracy
and repeatability.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS543 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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