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“Bioinformatics” 특집을 내면서

Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques.

In this article, we make the interested reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, and discussing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.

Contact: yvan.saeys@psb.ugent.be

Supplementary information: http://bioinformatics.psb.ugent.be/supplementary_data/yvsae/fsreview

A review of feature selection techniques in bioinformatics

Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of genes that can still achieve good predictive performance (for instance, for future use with diagnostic purposes in clinical practice). Many gene selection approaches use univariate (gene-by-gene) rankings of gene relevance and arbitrary thresholds to select the number of genes, can only be applied to two-class problems, and use gene selection ranking criteria unrelated to the classification algorithm. In contrast, random forest is a classification algorithm well suited for microarray data: it shows excellent performance even when most predictive variables are noise, can be used when the number of variables is much larger than the number of observations and in problems involving more than two classes, and returns measures of variable importance. Thus, it is important to understand the performance of random forest with microarray data and its possible use for gene selection. We investigate the use of random forest for classification of microarray data (including multi-class problems) and propose a new method of gene selection in classification problems based on random forest. Using simulated and nine microarray data sets we show that random forest has comparable performance to other classification methods, including DLDA, KNN, and SVM, and that the new gene selection procedure yields very small sets of genes (often smaller than alternative methods) while preserving predictive accuracy. Because of its performance and features, random forest and gene selection using random forest should probably become part of the "standard tool-box" of methods for class prediction and gene selection with microarray data.

/pdf/gene-selection-and-classification-of-microarray-data-using-3zpkuyc1ok.pdf

Gene selection and classification of microarray data using random forest

Regional homogeneity approach to fMRI data analysis

Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statis- tics are used to compare methodological trade-offs as well as determine an adequate number of clus- ters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed func- tional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homol- ogy, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/. Hum Brain Mapp

A whole brain fMRI atlas generated via spatially constrained spectral clustering

Purpose Chronic inflammation is believed to contribute to the development and progression of breast cancer. Systemic C-reactive protein (CRP) and serum amyloid A (SAA) are measures of low-grade chronic inflammation and potential predictors of cancer survival. Patients and Methods We evaluated the relationship between circulating markers of inflammation and breast cancer survival using data from the Health, Eating, Activity, and Lifestyle (HEAL) Study (a multiethnic prospective cohort study of women diagnosed with stage 0 to IIIA breast cancer). Circulating concentrations of CRP and SAA were measured approximately 31 months after diagnosis and tested for associations with disease-free survival (approximately 4.1 years of follow-up) and overall survival (approximately 6.9 years of follow-up) in 734 disease-free breast cancer survivors. Cox proportional hazards models were used with adjustment for potential confounding factors to generate hazard ratios (HRs) and 95% CIs. Results Elevated SAA and CRP were ass...

/pdf/elevated-biomarkers-of-inflammation-are-associated-with-3aeesrt73c.pdf

Elevated Biomarkers of Inflammation Are Associated With Reduced Survival Among Breast Cancer Patients

Motivation: Tw op ractical realities constrain the analysis of microarray data, mass spectra from proteomics, and biomedical infrared or magnetic resonance spectra. One is the ‘curse of dimensionality’: the number of features characterizing these data is in the thousands or tens of thousands. The other is the ‘curse of dataset sparsity’: the number of samples is limited. The consequences of these two curses are far-reaching when such data are used to classify the presence or absence of disease. Results: Using very simple classifiers, we show for several publicly available microarray and proteomics datasets how these curses influence classification outcomes. In particular, even if the sample per feature ratio is increased to the recommended 5–10 by feature extraction/reduction methods, dataset sparsity can render any classification result statistically suspect. In addition, several ‘optimal’ feature sets are typically identifiable for sparse datasets, all producing perfect classification results, both for the training and independent validation sets. This non-uniqueness leads to interpretational difficulties and casts doubt on the biological relevance of any of these ‘optimal’ feature sets. We suggest an approach to assess the relative quality of apparently equally good classifiers.

/pdf/class-prediction-and-discovery-using-gene-microarray-and-3okaa78bb2.pdf

Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions.

Purpose Overweight or obese breast cancer patients have a worse prognosis compared with normal-weight patients. This may be attributed to hyperinsulinemia and dysregulation of adipokine levels associated with overweight and obesity. Here, we evaluate whether low levels of adiponectin and a greater level of insulin resistance are associated with breast cancer mortality and all-cause mortality. Patients and Methods We measured glucose, insulin, and adiponectin levels in fasting serum samples from 527 women enrolled in the Health, Eating, Activity, and Lifestyle (HEAL) Study, a multiethnic, prospective cohort study of women diagnosed with stage I-IIIA breast cancer. We evaluated the association between adiponectin and insulin and glucose levels (expressed as the Homeostatic Model Assessment [HOMA] score) represented as continuous measures and median split categories, along with breast cancer mortality and all-cause mortality, using Cox proportional hazards models. Results Increasing HOMA scores were associat...

/pdf/associations-of-insulin-resistance-and-adiponectin-with-5clh3dyl6w.pdf

Richard Baumgartner

Papers

Elevated Biomarkers of Inflammation Are Associated With Reduced Survival Among Breast Cancer Patients

Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions.

Associations of Insulin Resistance and Adiponectin With Mortality in Women With Breast Cancer

Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis

Quantification in functional magnetic resonance imaging : Fuzzy clustering vs. correlation analysis