keyword
MENU ▼
Read by QxMD icon Read
search

svm

keyword
https://www.readbyqxmd.com/read/28092582/automatic-prediction-of-health-status-using-smartphone-derived-behaviour-profiles
#1
Daniel Kelly, Kevin Curran, Brian Caulfield
OBJECTIVE: Current methods of assessing the affect a patients' health has on their daily life are extremely limited. The aim of this work is to develop a sensor based approach to health status measurement in order to objectively measure health status. METHODS: Techniques to generate human behaviour profiles, derived from smartphone accelerometer and gyroscope sensors, are proposed. Experiments, using SVM regression models, are then conducted in order to evaluate the use of the proposed behaviour profiles as a predictor of health status...
January 9, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28092572/pspel-in-silico-prediction-of-self-interacting-proteins-from-amino-acids-sequences-using-ensemble-learning
#2
Jian-Qiang Li, Zhu-Hong You, Xiao Li, Ming Zhong, Xing Chen
Self interacting proteins (SIPs) play an important role in various aspects of the structural and functional organization of the cell. Detecting SIPs is one of the most important issues in current molecular biology. Although a large number of SIPs data has been generated by experimental methods, wet laboratory approaches are both time-consuming and costly. In addition, they yield high false negative and positive rates. Thus, there is a great need for in silico methods to predict SIPs accurately and efficiently...
January 10, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28088356/advancing-the-prediction-accuracy-of-protein-protein-interactions-by-utilizing-evolutionary-information-from-position-specific-scoring-matrix-and-ensemble-classifier
#3
Lei Wang, Zhu-Hong You, Shi-Xiong Xia, Feng Liu, Xing Chen, Xin Yan, Yong Zhou
Protein-Protein Interactions (PPIs) are essential to most biological processes and play a critical role in most cellular functions. With the development of high-throughput biological techniques and in silico methods, a large number of PPI data have been generated for various organisms, but many problems remain unsolved. These factors promoted the development of the in silico methods based on machine learning to predict PPIs. In this study, we propose a novel method by combining ensemble Rotation Forest (RF) classifier and Discrete Cosine Transform (DCT) algorithm to predict the interactions among proteins...
January 11, 2017: Journal of Theoretical Biology
https://www.readbyqxmd.com/read/28088191/predicting-probable-alzheimer-s-disease-using-linguistic-deficits-and-biomarkers
#4
Sylvester O Orimaye, Jojo S-M Wong, Karen J Golden, Chee P Wong, Ireneous N Soyiri
BACKGROUND: The manual diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population...
January 14, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28075375/an-approach-to-biometric-verification-based-on-human-body-communication-in-wearable-devices
#5
Jingzhen Li, Yuhang Liu, Zedong Nie, Wenjian Qin, Zengyao Pang, Lei Wang
In this paper, an approach to biometric verification based on human body communication (HBC) is presented for wearable devices. For this purpose, the transmission gain S21 of volunteer's forearm is measured by vector network analyzer (VNA). Specifically, in order to determine the chosen frequency for biometric verification, 1800 groups of data are acquired from 10 volunteers in the frequency range 0.3 MHz to 1500 MHz, and each group includes 1601 sample data. In addition, to achieve the rapid verification, 30 groups of data for each volunteer are acquired at the chosen frequency, and each group contains only 21 sample data...
January 10, 2017: Sensors
https://www.readbyqxmd.com/read/28072829/predicting-the-metabolic-sites-by-flavin-containing-monooxygenase-on-drug-molecules-using-svm-classification-on-computed-quantum-mechanics-and-circular-fingerprints-molecular-descriptors
#6
Chien-Wei Fu, Thy-Hou Lin
As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO) also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM) on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D) are computed and classified using the support vector machine (SVM) for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes...
2017: PloS One
https://www.readbyqxmd.com/read/28068348/optical-determination-of-lead-chrome-green-in-green-tea-by-fourier-transform-infrared-ft-ir-transmission-spectroscopy
#7
Xiaoli Li, Kaiwen Xu, Yuying Zhang, Chanjun Sun, Yong He
The potential of Fourier transform infrared (FT-IR) transmission spectroscopy for determination of lead chrome green in green tea was investigated based on chemometric methods. Firstly, the qualitative analysis of lead chrome green in tea was performed based on partial least squares discriminant analysis (PLS-DA), and the correct rate of classification was 100%. And then, a hybrid method of interval partial least squares (iPLS) regression and successive projections algorithm (SPA) was proposed to select characteristic wavenumbers for the quantitative analysis of lead chrome green in green tea, and 19 wavenumbers were obtained finally...
2017: PloS One
https://www.readbyqxmd.com/read/28068295/support-vector-machines-to-detect-physiological-patterns-for-eeg-and-emg-based-human-computer-interaction-a-review
#8
L R Quitadamo, F Cavrini, L Sbernini, F Riillo, L Bianchi, S Seri, G Saggio
Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM...
January 9, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28068089/food-targeting-geographical-origin-determination-of-hazelnuts-corylus-avellana-by-lc-qqq-ms-ms-based-targeted-metabolomics-application
#9
Sven Klockmann, Eva Reiner, Nicolas Cain, Markus Fischer
A targeted metabolomics LC-ESI-QqQ-MS application for geographical origin discrimination based on 20 non-polar key metabolites was developed, validated according to accepted guidelines and used for quantitation via stable isotope labeled internal standards in 202 raw authentic hazelnut samples from six countries (Turkey, Italy, Georgia, Spain, France and Germany) out of harvest years 2014 and 2015. Multivariate statistics were used for detection of significant variations in metabolite levels between countries and moreover, a prediction model using support vector machine classification (SVM) was calculated yielding 100% training accuracy and 97% cross-validation accuracy which was subsequently applied to 55 hazelnut samples for confectionary industry gaining up to 80% correct classifications compared to declared origin...
January 9, 2017: Journal of Agricultural and Food Chemistry
https://www.readbyqxmd.com/read/28067767/depth-errors-analysis-and-correction-for-time-of-flight-tof-cameras
#10
Ying He, Bin Liang, Yu Zou, Jin He, Jun Yang
Time-of-Flight (ToF) cameras, a technology which has developed rapidly in recent years, are 3D imaging sensors providing a depth image as well as an amplitude image with a high frame rate. As a ToF camera is limited by the imaging conditions and external environment, its captured data are always subject to certain errors. This paper analyzes the influence of typical external distractions including material, color, distance, lighting, etc. on the depth error of ToF cameras. Our experiments indicated that factors such as lighting, color, material, and distance could cause different influences on the depth error of ToF cameras...
January 5, 2017: Sensors
https://www.readbyqxmd.com/read/28065840/mace-prediction-of-acute-coronary-syndrome-via-boosted-resampling-classification-using-electronic-medical-records
#11
Zhengxing Huang, Tak-Ming Chan, Wei Dong
OBJECTIVES: Major adverse cardiac events (MACE) of acute coronary syndrome (ACS) often occur suddenly resulting in high mortality and morbidity. Recently, the rapid development of electronic medical records (EMR) provides the opportunity to utilize the potential of EMR to improve the performance of MACE prediction. In this study, we present a novel data-mining based approach specialized for MACE prediction from a large volume of EMR data. METHODS: The proposed approach presents a new classification algorithm by applying both over-sampling and under-sampling on minority-class and majority-class samples, respectively, and integrating the resampling strategy into a boosting framework so that it can effectively handle imbalance of MACE of ACS patients analogous to domain practice...
January 5, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28060807/svm-and-svm-ensembles-in-breast-cancer-prediction
#12
Min-Wei Huang, Chih-Wen Chen, Wei-Chao Lin, Shih-Wen Ke, Chih-Fong Tsai
Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions...
2017: PloS One
https://www.readbyqxmd.com/read/28059133/prediction-of-n-methyl-d-aspartate-receptor-glun1-ligand-binding-affinity-by-a-novel-svm-pose-svm-score-combinatorial-ensemble-docking-scheme
#13
Max K Leong, Ren-Guei Syu, Yi-Lung Ding, Ching-Feng Weng
The glycine-binding site of the N-methyl-D-aspartate receptor (NMDAR) subunit GluN1 is a potential pharmacological target for neurodegenerative disorders. A novel combinatorial ensemble docking scheme using ligand and protein conformation ensembles and customized support vector machine (SVM)-based models to select the docked pose and to predict the docking score was generated for predicting the NMDAR GluN1-ligand binding affinity. The predicted root mean square deviation (RMSD) values in pose by SVM-Pose models were found to be in good agreement with the observed values (nā€‰=ā€‰30, r(2)ā€‰=ā€‰0...
January 6, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28055987/heart-beat-classification-from-single-lead-ecg-using-the-synchrosqueezing-transform
#14
Christophe L Herry, Martin Frasch, Andrew Je Seely, Hau-Tieng Wu
The processing of ECG signal provides a wealth of information on cardiac function and overall cardiovascular health. While multi-lead ECG recordings are often necessary for a proper assessment of cardiac rhythms, they are not always available or practical, for example in fetal ECG applications. Moreover, a wide range of small non-obtrusive single-lead ECG ambulatory monitoring devices are now available, from which heart rate variability (HRV) and other health-related metrics are derived. Proper beat detection and classification of abnormal rhythms is important for reliable HRV assessment and can be challenging in single-lead ECG monitoring devices...
January 5, 2017: Physiological Measurement
https://www.readbyqxmd.com/read/28055924/robust-c-loss-kernel-classifiers
#15
Guibiao Xu, Bao-Gang Hu, Jose C Principe
The correntropy-induced loss (C-loss) function has the nice property of being robust to outliers. In this paper, we study the C-loss kernel classifier with the Tikhonov regularization term, which is used to avoid overfitting. After using the half-quadratic optimization algorithm, which converges much faster than the gradient optimization algorithm, we find out that the resulting C-loss kernel classifier is equivalent to an iterative weighted least square support vector machine (LS-SVM). This relationship helps explain the robustness of iterative weighted LS-SVM from the correntropy and density estimation perspectives...
December 29, 2016: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28048907/su-f-r-46-predicting-distant-failure-in-lung-sbrt-using-multi-objective-radiomics-model
#16
Z Zhou, M Folkert, P Iyengar, Y Zhang, J Wang
PURPOSE: To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. METHODS: Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048828/su-g-bra-09-estimation-of-motion-tracking-uncertainty-for-real-time-adaptive-imaging
#17
H Yan, Z Chen, R Nath, W Liu
PURPOSE: kV fluoroscopic imaging combined with MV treatment beam imaging has been investigated for intrafractional motion monitoring and correction. It is, however, subject to additional kV imaging dose to normal tissue. To balance tracking accuracy and imaging dose, we previously proposed an adaptive imaging strategy to dynamically decide future imaging type and moments based on motion tracking uncertainty. kV imaging may be used continuously for maximal accuracy or only when the position uncertainty (probability of out of threshold) is high if a preset imaging dose limit is considered...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048816/mo-de-207b-05-predicting-gene-mutations-in-renal-cell-carcinoma-based-on-ct-imaging-features-validation-using-tcga-tcia-datasets
#18
X Chen, Z Zhou, K Thomas, J Wang
PURPOSE: The goal of this work is to investigate the use of contrast enhanced computed tomographic (CT) features for the prediction of mutations of BAP1, PBRM1, and VHL genes in renal cell carcinoma (RCC). METHODS: For this study, we used two patient databases with renal cell carcinoma (RCC). The first one consisted of 33 patients from our institution (UT Southwestern Medical Center, UTSW). The second one consisted of 24 patients from the Cancer Imaging Archive (TCIA), where each patient is connected by a unique identi?er to the tissue samples from the Cancer Genome Atlas (TCGA)...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048644/tu-h-campus-iep3-05-computer-aided-diagnosis-employing-automatically-segmented-color-specific-regions-in-ultrasound-shear-wave-elastography-for-the-assessment-of-chronic-liver-disease
#19
I Gatos, S Tsantis, G C Kagadis
PURPOSE: To assess the role of an automated Computer Aided Diagnosis (CAD) system in differentiation of Healthy Subjects to Chronic Liver Disease (CLD) Patients in terms of liver fibrosis (F0-F4), using Ultrasound Shear Wave Elastography (SWE). METHODS: Clinical Dataset consisted of 125 subjects, 55 Healthy (F0) whom condition was validated with Normal Biochemical Markers and clear Clinical History, and 70 with CLD (F1-F4) whom condition was validated with Liver Biopsy...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048384/su-d-207b-06-predicting-breast-cancer-malignancy-on-dce-mri-data-using-pre-trained-convolutional-neural-networks
#20
N Antropova, B Huynh, M Giger
PURPOSE: We investigate deep learning in the task of distinguishing between malignant and benign breast lesions on dynamic contrast-enhanced MR images (DCE-MRIs), eliminating the need for lesion segmentation and extraction of tumor features. We evaluate convolutional neural network (CNN) after transfer learning with ImageNet, a database of thousands of non-medical images. METHODS: Under a HIPAA-compliant IRB protocol, a database of 551 (357 malignant and 194 benign) breast MRI cases was collected...
June 2016: Medical Physics
keyword
keyword
116917
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"