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While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well. To overcome this limitation, we present hash graph kernels, a general framework to derive kernels for graphs with continuous attributes from discrete ones. The idea is to iteratively turn continuous...
Epigenetics is the study of heritable changesin gene expression that does not involve changes to theunderlying DNA sequence, i.e. a change in phenotype notinvolved by a change in genotype. At least three mainfactor seems responsible for epigenetic change including DNAmethylation, histone modification and non-coding RNA, eachone sharing having the same property to affect the dynamicof the chromatin...
The biological processes are widely studied by genome analysis leading to a large number of genes, thus making necessary the use of automated evaluation methods. In this study, we examine the influence of algorithmic parameters in the prediction power of a gene signature and in the selection process of the signature itself. We focus on one gene selection approach applied on a dataset of the budding...
Graph classification is important for different scientific applications; it can be exploited in various problems related to bioinformatics and cheminformatics. Given their graphs, there is increasing need for classifying small molecules to predict their properties such as activity, toxicity or mutagenicity. Using subtrees as feature set for graph classification in kernel methods has been shown to...
O-glycosylation is one of the main types of the mammalian protein glycosylation, it occurs on the particular site of serine and threonine. It's important to predict the O-glycosylation site. In this paper, we propose a new method of kernel principal component analysis (KPCA) to predict the O-glycosylation site with window size w=9. The samples for experiment are encoded by the sparse coding and projected...
Predicting gene functions is a challenge for biologists in the postgenomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene...
Conventional support vector machine (SVM) utilizes the sign function to classify test data into different classes, which has demonstrated some limitations that hinder its performance. This paper explores the feasibility of using Bayesian statistics to support decision making in the SVM and demonstrated its application in Bioinformatics. The proposed methodology was tested on two real biological problems:...
The clinical symptoms of metabolic disorders during neonatal period are often not apparent, if not treated early irreversible damages such as mental retardation may occur, even death. Therefore, practicing newborn screening is very important to prevent neonatal from these damages. In this paper, the newborn screening system used support vector machines (SVM) classification technique is proposed in...
The analysis of microarray data is a challenging task for statistical and machine learning methods, since the datasets usually contain a very large number of features (genes) and only a small number of examples (subjects). In this work, we describe a technique for gene selection and classification of microarray data based on the recently proposed potential support vector machine (P-SVM) for feature...
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