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Selecting informative genes from microarray gene expression data is the most important task while performing data analysis on the large amount of data. Mining genes having regulatory relations within thousands of genes is essential. To fit this need, a number of methods were proposed from various points of view. However, most existing methods solely focus on gene expression values themselves without...
Bio-inspired computer vision is an emerging field. It aims to reproduce the capabilities of biological vision systems, eventually to simulate the visual functions for various purposes. In this paper, we propose a bio-inspired computer visual system using Graphical Processing Unit (GPU), and its application on breast cancer prognosis. The system extracts visual features from an input image using a...
Repetitive substructures within a protein play an important role in understanding protein folding and stability, biological function, and genome evolution. About 25% of all proteins contain repeat structures for eukaryote species and most of them do not have the resolved structural information yet. Therefore, this study aimed to design a comprehensive system for identifying internal repeats either...
In the post-genome era, disease-relevant gene finding and prioritization have focused on genome-wide association studies and molecular interaction networks, due to their power in characterizing the functions of genes/proteins in genomics and network biology contexts. In this paper, we describe a simple yet generic computational framework based on protein interaction networks to perform and evaluate...
Microarray technology provides an opportunity for scientists to analyze thousands of gene expression profiles simultaneously. Time-series microarray data are gene expression values generated from microarray experiments within certain time intervals. Scientists can infer gene regulations in a biological system by judging whether two genes present similar gene expression values in microarray time-series...
Using X-ray crystallography to determine the 3D structure of a protein is a costly and time-consuming process. One of the major reasons is that the protein needs to be purified and crystallized first, and the failure rate of protein crystallization is quite high. Thus it is desired to use a computational method to predict protein crystallizability based on the primary structure information before...
This paper uses the SVM to predict the protein disordered region. Nevertheless, the number of features used in this paper is 440. Both time and space complexity is high while performing the support vector machine (SVM) training and testing. So this paper proposes a hybrid feature selection mechanism to reduce the dimensionality of the feature space. The filter and wrapper feature selection methods...
In proteomics, a proteinpsilas function is always strongly related to its structure. But, while some parts of a protein have a fixed definite structure, such as alpha-helix, beta-sheet, or coil, other parts are not associated with well-defined conformations. Previously, these so-called disordered regions were not thought to have a specific function of their own. But, recent studies suggest that some...
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