5,252 research outputs found
Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition
Sparse representation based classification (SRC) methods have achieved
remarkable results. SRC, however, still suffer from requiring enough training
samples, insufficient use of test samples and instability of representation. In
this paper, a stable inverse projection representation based classification
(IPRC) is presented to tackle these problems by effectively using test samples.
An IPR is firstly proposed and its feasibility and stability are analyzed. A
classification criterion named category contribution rate is constructed to
match the IPR and complete classification. Moreover, a statistical measure is
introduced to quantify the stability of representation-based classification
methods. Based on the IPRC technique, a robust tumor recognition framework is
presented by interpreting microarray gene expression data, where a two-stage
hybrid gene selection method is introduced to select informative genes.
Finally, the functional analysis of candidate's pathogenicity-related genes is
given. Extensive experiments on six public tumor microarray gene expression
datasets demonstrate the proposed technique is competitive with
state-of-the-art methods.Comment: 14 pages, 19 figures, 10 table
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
Elephant Search with Deep Learning for Microarray Data Analysis
Even though there is a plethora of research in Microarray gene expression
data analysis, still, it poses challenges for researchers to effectively and
efficiently analyze the large yet complex expression of genes. The feature
(gene) selection method is of paramount importance for understanding the
differences in biological and non-biological variation between samples. In
order to address this problem, a novel elephant search (ES) based optimization
is proposed to select best gene expressions from the large volume of microarray
data. Further, a promising machine learning method is envisioned to leverage
such high dimensional and complex microarray dataset for extracting hidden
patterns inside to make a meaningful prediction and most accurate
classification. In particular, stochastic gradient descent based Deep learning
(DL) with softmax activation function is then used on the reduced features
(genes) for better classification of different samples according to their gene
expression levels. The experiments are carried out on nine most popular Cancer
microarray gene selection datasets, obtained from UCI machine learning
repository. The empirical results obtained by the proposed elephant search
based deep learning (ESDL) approach are compared with most recent published
article for its suitability in future Bioinformatics research.Comment: 12 pages, 5 Tabl
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
Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types.
Isoforms of human miRNAs (isomiRs) are constitutively expressed with tissue- and disease-subtype-dependencies. We studied 10 271 tumor datasets from The Cancer Genome Atlas (TCGA) to evaluate whether isomiRs can distinguish amongst 32 TCGA cancers. Unlike previous approaches, we built a classifier that relied solely on \u27binarized\u27 isomiR profiles: each isomiR is simply labeled as \u27present\u27 or \u27absent\u27. The resulting classifier successfully labeled tumor datasets with an average sensitivity of 90% and a false discovery rate (FDR) of 3%, surpassing the performance of expression-based classification. The classifier maintained its power even after a 15Ć reduction in the number of isomiRs that were used for training. Notably, the classifier could correctly predict the cancer type in non-TCGA datasets from diverse platforms. Our analysis revealed that the most discriminatory isomiRs happen to also be differentially expressed between normal tissue and cancer. Even so, we find that these highly discriminating isomiRs have not been attracting the most research attention in the literature. Given their ability to successfully classify datasets from 32 cancers, isomiRs and our resulting \u27Pan-cancer Atlas\u27 of isomiR expression could serve as a suitable framework to explore novel cancer biomarkers
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