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A set of protein pairs predicted to be interacting with high ratio of true positive is valuable for target selection in experiments like protein structure determination. Our goal in this paper is to investigate the problem of finding such a set of protein pairs in an organism by machine learning methods. Yeast genome was taken for this study and support vector machine was adopted as the classification...
The use of two powerful classification techniques (boosting and SVM) is explored for the segmentation of white-matter lesions in the MRI scans of human brain. Simple features are generated from proton density (PD) scans. Radial basis function (RBF) based Adaboost technique and support vector machines (SVM) are employed for this task. The classifiers are trained on severe, moderate and mild cases....
Biological markers are useful tools for the diagnosis and prognosis of disease. Many different methods are currently used to extract markers from multiple data sources, including gene expression microarrays. This paper investigates the effect of outlier removal on the performance of one such biomarker selection method, support vector machines (SVM). A simple method of outlier removal is employed as...
The performances of support vector regression estimation were analyzed. It was found that the insensitive factor epsiv can affect the performance of support vector regression estimation significantly. The noise inside the sample data should be considered in determining the insensitive factor epsiv when support vector regression was employed. A novel support vector regression based on non-uniform lost...
We have recently developed a sagittal laser optical tomographic (SLOT) imaging system for the diagnosis and monitoring of inflammatory processes in proximal interphalangeal (PIP) joints of patients with rheumatoid arthritis (RA). While cross sectional images of distribution of optical properties can now be generated easily, clinical interpretation of these images remains a challenge. In this paper,...
Support vector machine (SVM) can be seen as a new machine learning way which is based on the idea of VC dimensions and the principle of structural risk minimization rather than empirical risk minimization. SVM can be used for classification and regression. Support vector regression (SVR) is a very important branch of Support vector machine. Partial differential equations (PDEs) have been successfully...
The aim of this paper was to investigate the usefulness of multiscale morphological analysis in the assessment of atherosclerotic carotid plagues. Ultrasound images were recorded from 137 asymptomatic and 137 symptomatic plaques and were converted to binary images at low, middle and high intensity intervals based on structural morphology . Low images represent low intensity regions corresponding to...
The classification of the uterine myoma and adenomyosis from their ultrasound images mainly depends on doctors' experience and lacks objective criterions. Here a novel classification method is proposed using the multiresolution analysis and the orientational fractal analysis. Firstly, texture features under various resolutions and orientational fractal features are obtained from ultrasound images...
Using imitating-natural-reading induced potentials as communication carriers, we are constructing a brain-computer interface based mental speller which enable users to interaction with computers. The potentials were induced in this way: In a trial, strings consisted of target and non-target symbols were moving smoothly from right to left through a little visual window at the center of computer screen...
This paper reports on the use of electroencephalogram (EEG)-based phase desynchronization networks for the recognition of imagined movements. Features derived solely from these networks are classified using linear support vector machine. An average accuracy of 73% is achieved for the single-trial imagined hand versus foot movements. The results demonstrate that phase desynchronizations provide relevant...
Support vector machine (SVM) is a new learning technique based on statistical learning theory (SLT). In this paper, a medical diagnosis decision system (MDDSS) based on SVM has been established to intellectively diagnose 4 types of acid-base disturbance. SVM was originally developed for two-class classification. It is extended to solve multi-class classification problem named hierarchical SVM with...
A new method based on support vector regression (SVR) has been introduced to predict the relative solvent accessibility (RSA) of residues from protein primary sequences, which uses the local information of protein primary sequences as input. Different to most previous methods which are designed to predict the exposure state (exposed/buried, exposed/intermediate/buried, etc) of a particular residue...
Annotation of the functional sites on the surface of a protein has been the subject of many studies. In this regard, the search for attributes and features characterizing these sites is of prime consequence. Here, we present an implementation of a kernel-based machine learning protocol for identifying residues on a DNA-binding protein form the interface with the DNA. Sequence and structural features...
Frequent arousals during sleep degrade the quality of sleep and result in sleep fragmentation. Visual inspection of physiological signals to detect the arousal events is inconvenient and time-consuming work. The purpose of this study was to develop an automatic algorithm to detect the arousal events. We proposed the automatic method to detect arousals based on time-frequency analysis and the support...
A visualization and steering application, GAVis, has been developed to aid in understanding the behavior of and guiding the convergence of genetic algorithms running in parallel over long time periods. When classification techniques such as support vector machines (SVMs) paired with complete leave-one-out validation are used as a fitness function for identification of markers in -omic data, the time...
We investigated differences in corpus callosum shape at the midsagittal plane using MRI for different subjects: normal males, normal females, and subjects with gender identity disorder (GID). We first represented callosal shapes with Fourier descriptors of callosal contours. Using linear support vector machines with soft-margin, we next determined a hyperplane that separates normal males and females...
The detection and classification of leukocytes in blood smear images is a routine task in medical diagnosis. In this paper we present a fully automated approach to leukocyte segmentation that is robust with respect to cell appearance and image quality. A set of features is used to describe cytoplasm and nucleus properties. Pairwise SVM classification is used to discriminate between different cell...
Holter electrocardiogram data is analyzed by a computer, however, there is a detection of non-heartbeat as a heartbeat. This study dealt with reduction of the incorrect detection using support vector machine (SVM). By exploiting the power of SVM and human like information processing, the data was classified to heartbeat class or non-heartbeat class. The performance of the proposed method was verified...
Brain computer interface (BCI) algorithms are used to predict the torque generation in the direction of shoulder abduction or elbow flexion using scalp EEG signals from 163 electrodes. Based on features extracted from both frequency and time domains, three classifiers are employed including support vector classifier, classification trees and K nearest neighbor. Support vector classifier achieves the...
Traditional voice activity detection algorithms are mostly threshold-based or statistical model-based. All those methods are absent of the ability to react quickly to variations of environments. This paper describes an incremental SVM (support vector machine) method for speech activity detection. The proposed incremental procedure makes it adaptive to variation of environments and the special construction...
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