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https://www.readbyqxmd.com/read/28453576/a-novel-framework-for-the-identification-of-drug-target-proteins-combining-stacked-auto-encoders-with-a-biased-support-vector-machine
#1
Qi Wang, YangHe Feng, JinCai Huang, TengJiao Wang, GuangQuan Cheng
The identification of drug target proteins (IDTP) plays a critical role in biometrics. The aim of this study was to retrieve potential drug target proteins (DTPs) from a collected protein dataset, which represents an overwhelming task of great significance. Previously reported methodologies for this task generally employ protein-protein interactive networks but neglect informative biochemical attributes. We formulated a novel framework utilizing biochemical attributes to address this problem. In the framework, a biased support vector machine (BSVM) was combined with the deep embedded representation extracted using a deep learning model, stacked auto-encoders (SAEs)...
2017: PloS One
https://www.readbyqxmd.com/read/28448817/automated-detection-of-pathologic-white-matter-alterations-in-alzheimer-s-disease-using-combined-diffusivity-and-kurtosis-method
#2
Yuanyuan Chen, Miao Sha, Xin Zhao, Jianguo Ma, Hongyan Ni, Wei Gao, Dong Ming
Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) are important diffusion MRI techniques for detecting microstructure abnormities in diseases such as Alzheimer's. The advantages of DKI over DTI have been reported generally; however, the indistinct relationship between diffusivity and kurtosis has not been clearly revealed in clinical settings. In this study, we hypothesize that the combination of diffusivity and kurtosis in DKI improves the capacity of DKI to detect Alzheimer's disease compared with diffusivity or kurtosis alone...
April 12, 2017: Psychiatry Research
https://www.readbyqxmd.com/read/28448272/automated-network-analysis-to-measure-brain-effective-connectivity-estimated-from-eeg-data-of-patients-with-alcoholism
#3
Youngoh Bae, Byeong Wook Yoo, Jung Chan Lee, Hee Chan Kim
OBJECTIVE: Detection and diagnosis based on extracting features and classification using electroencephalography (EEG) signals are being studied vigorously. A network analysis of time series EEG signal data is one of many techniques that could help study brain functions. In this study, we analyze EEG to diagnose alcoholism. APPROACH: We propose a novel methodology to estimate the differences in the status of the brain based on EEG data of normal subjects and data from alcoholics by computing many parameters stemming from effective network using Granger causality...
May 2017: Physiological Measurement
https://www.readbyqxmd.com/read/28445404/support-vector-data-description-model-to-map-specific-land-cover-with-optimal-parameters-determined-from-a-window-based-validation-set
#4
Jinshui Zhang, Zhoumiqi Yuan, Guanyuan Shuai, Yaozhong Pan, Xiufang Zhu
This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD), to determine optimal parameters for support vector data description (SVDD) model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of the compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space...
April 26, 2017: Sensors
https://www.readbyqxmd.com/read/28443027/personalized-medication-response-prediction-for-attention-deficit-hyperactivity-disorder-learning-in-the-model-space-vs-learning-in-the-data-space
#5
Hin K Wong, Paul A Tiffin, Michael J Chappell, Thomas E Nichols, Patrick R Welsh, Orla M Doyle, Boryana C Lopez-Kolkovska, Sarah K Inglis, David Coghill, Yuan Shen, Peter Tiño
Attention-Deficit Hyperactive Disorder (ADHD) is one of the most common mental health disorders amongst school-aged children with an estimated prevalence of 5% in the global population (American Psychiatric Association, 2013). Stimulants, particularly methylphenidate (MPH), are the first-line option in the treatment of ADHD (Reeves and Schweitzer, 2004; Dopheide and Pliszka, 2009) and are prescribed to an increasing number of children and adolescents in the US and the UK every year (Safer et al., 1996; McCarthy et al...
2017: Frontiers in Physiology
https://www.readbyqxmd.com/read/28443015/cross-subject-eeg-feature-selection-for-emotion-recognition-using-transfer-recursive-feature-elimination
#6
Zhong Yin, Yongxiong Wang, Li Liu, Wei Zhang, Jianhua Zhang
Using machine-learning methodologies to analyze EEG signals becomes increasingly attractive for recognizing human emotions because of the objectivity of physiological data and the capability of the learning principles on modeling emotion classifiers from heterogeneous features. However, the conventional subject-specific classifiers may induce additional burdens to each subject for preparing multiple-session EEG data as training sets. To this end, we developed a new EEG feature selection approach, transfer recursive feature elimination (T-RFE), to determine a set of the most robust EEG indicators with stable geometrical distribution across a group of training subjects and a specific testing subject...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28442997/high-spatiotemporal-resolution-ecog-recording-of-somatosensory-evoked-potentials-with-flexible-micro-electrode-arrays
#7
Taro Kaiju, Keiichi Doi, Masashi Yokota, Kei Watanabe, Masato Inoue, Hiroshi Ando, Kazutaka Takahashi, Fumiaki Yoshida, Masayuki Hirata, Takafumi Suzuki
Electrocorticogram (ECoG) has great potential as a source signal, especially for clinical BMI. Until recently, ECoG electrodes were commonly used for identifying epileptogenic foci in clinical situations, and such electrodes were low-density and large. Increasing the number and density of recording channels could enable the collection of richer motor/sensory information, and may enhance the precision of decoding and increase opportunities for controlling external devices. Several reports have aimed to increase the number and density of channels...
2017: Frontiers in Neural Circuits
https://www.readbyqxmd.com/read/28441231/patient-specific-classification-of-icu-sedation-levels-from-heart-rate-variability
#8
Sunil B Nagaraj, Siddharth Biswal, Emily J Boyle, David W Zhou, Lauren M McClain, Ednan K Bajwa, Sadeq A Quraishi, Akeju Oluwaseun, Riccardo Barbieri, Patrick L Purdon, M Brandon Westover
OBJECTIVE: To develop a personalizable algorithm to discriminate between sedation levels in ICU patients based on heart rate variability. DESIGN: Multicenter, pilot study. SETTING: Several ICUs at Massachusetts General Hospital, Boston, MA. PATIENTS: We gathered 21,912 hours of routine electrocardiogram recordings from a heterogenous group of 70 adult ICU patients. All patients included in the study were mechanically ventilated and were receiving sedatives...
April 22, 2017: Critical Care Medicine
https://www.readbyqxmd.com/read/28440291/identifying-n-6-methyladenosine-sites-using-multi-interval-nucleotide-pair-position-specificity-and-support-vector-machine
#9
Pengwei Xing, Ran Su, Fei Guo, Leyi Wei
N6-methyladenosine (m(6)A) refers to methylation of the adenosine nucleotide acid at the nitrogen-6 position. It plays an important role in a series of biological processes, such as splicing events, mRNA exporting, nascent mRNA synthesis, nuclear translocation and translation process. Numerous experiments have been done to successfully characterize m(6)A sites within sequences since high-resolution mapping of m(6)A sites was established. However, as the explosive growth of genomic sequences, using experimental methods to identify m(6)A sites are time-consuming and expensive...
April 25, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28436898/extended-polynomial-growth-transforms-for-design-and-training-of-generalized-support-vector-machines
#10
Ahana Gangopadhyay, Oindrila Chatterjee, Shantanu Chakrabartty
Growth transformations constitute a class of fixed-point multiplicative update algorithms that were originally proposed for optimizing polynomial and rational functions over a domain of probability measures. In this paper, we extend this framework to the domain of bounded real variables which can be applied towards optimizing the dual cost function of a generic support vector machine (SVM). The approach can, therefore, not only be used to train traditional soft-margin binary SVMs, one-class SVMs, and probabilistic SVMs but can also be used to design novel variants of SVMs with different types of convex and quasi-convex loss functions...
April 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28436405/a-comparison-of-methods-for-three-class-mammograms-classification
#11
Marina Milosevic, Zeljko Jovanovic, Dragan Jankovic
BACKGROUND: Mammography is considered the gold standard for early breast cancer detection but it is very difficult to interpret mammograms for many reason. Computer aided diagnosis (CAD) is an important development that may help to improve the performance in breast cancer detection. OBJECTIVE: We present a CAD system based on feature extraction techniques for detecting abnormal patterns in digital mammograms. METHODS: Computed features based on gray-level co-occurrence matrices (GLCM) are used to evaluate the effectiveness of textural information possessed by mass regions...
April 14, 2017: Technology and Health Care: Official Journal of the European Society for Engineering and Medicine
https://www.readbyqxmd.com/read/28435046/a-multilevel-roi-features-based-machine-learning-method-for-detection-of-morphometric-biomarkers-in-parkinson-s-disease
#12
Bo Peng, Suhong Wang, Zhiyong Zhou, Yan Liu, Baotong Tong, Tao Zhang, Yakang Dai
Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisted diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to make the multilevel ROI features...
April 20, 2017: Neuroscience Letters
https://www.readbyqxmd.com/read/28434670/real-time-eutrophication-status-evaluation-of-coastal-waters-using-support-vector-machine-with-grid-search-algorithm
#13
Xianyu Kong, Yuyan Sun, Rongguo Su, Xiaoyong Shi
The development of techniques for real-time monitoring of the eutrophication status of coastal waters is of great importance for realizing potential cost savings in coastal monitoring programs and providing timely advice for marine health management. In this study, a GS optimized SVM was proposed to model relationships between 6 easily measured parameters (DO, Chl-a, C1, C2, C3 and C4) and the TRIX index for rapidly assessing marine eutrophication states of coastal waters. The good predictive performance of the developed method was indicated by the R(2) between the measured and predicted values (0...
April 20, 2017: Marine Pollution Bulletin
https://www.readbyqxmd.com/read/28433870/automated-detection-of-premature-delivery-using-empirical-mode-and-wavelet-packet-decomposition-techniques-with-uterine-electromyogram-signals
#14
U Rajendra Acharya, Vidya K Sudarshan, Soon Qing Rong, Zechariah Tan, Choo Min Lim, Joel Ew Koh, Sujatha Nayak, Sulatha V Bhandary
An accurate detection of preterm labor and the risk of preterm delivery before 37 weeks of gestational age is crucial to increase the chance of survival rate for both mother and the infant. Thus, the uterine contractions measured using uterine electromyogram (EMG) or electro hysterogram (EHG) need to have high sensitivity in the detection of true preterm labor signs. However, visual observation and manual interpretation of EHG signals at the time of emergency situation may lead to errors. Therefore, the employment of computer-based approaches can assist in fast and accurate detection during the emergency situation...
April 18, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28433431/application-of-structured-support-vector-machine-backpropagation-to-a-convolutional-neural-network-for-human-pose-estimation
#15
Peerajak Witoonchart, Prabhas Chongstitvatana
In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer...
February 16, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28431822/detecting-bursts-in-the-eeg-of-very-and-extremely-premature-infants-using-a-multi-feature-approach
#16
John M O'Toole, Geraldine B Boylan, Rhodri O Lloyd, Robert M Goulding, Sampsa Vanhatalo, Nathan J Stevenson
AIM: To develop a method that segments preterm EEG into bursts and inter-bursts by extracting and combining multiple EEG features. METHODS: Two EEG experts annotated bursts in individual EEG channels for 36 preterm infants with gestational age < 30 weeks. The feature set included spectral, amplitude, and frequency-weighted energy features. Using a consensus annotation, feature selection removed redundant features and a support vector machine combined features...
April 18, 2017: Medical Engineering & Physics
https://www.readbyqxmd.com/read/28431389/prediction-of-size-fractionated-airborne-particle-bound-metals-using-mlr-bp-ann-and-svm-analyses
#17
Xiang'zi Leng, Jinhua Wang, Haibo Ji, Qin'geng Wang, Huiming Li, Xin Qian, Fengying Li, Meng Yang
Size-fractionated heavy metal concentrations were observed in airborne particulate matter (PM) samples collected from 2014 to 2015 (spanning all four seasons) from suburban (Xianlin) and industrial (Pukou) areas in Nanjing, a megacity of southeast China. Rapid prediction models of size-fractionated metals were established based on multiple linear regression (MLR), back propagation artificial neural network (BP-ANN) and support vector machine (SVM) by using meteorological factors and PM concentrations as input parameters...
April 6, 2017: Chemosphere
https://www.readbyqxmd.com/read/28428140/automated-annotation-and-classification-of-bi-rads-assessment-from-radiology-reports
#18
Sergio M Castro, Eugene Tseytlin, Olga Medvedeva, Kevin Mitchell, Shyam Visweswaran, Tanja Bekhuis, Rebecca S Jacobson
The Breast Imaging Reporting and Data System (BI-RADS) was developed to reduce variation in the descriptions of findings. Manual analysis of breast radiology report data is challenging but is necessary for clinical and healthcare quality assurance activities. The objective of this study is to develop a natural language processing (NLP) system for automated BI-RADS categories extraction from breast radiology reports. We evaluated an existing rule-based NLP algorithm, and then we developed and evaluated our own method using a supervised machine learning approach...
April 17, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28428048/multi-center-machine-learning-in-imaging-psychiatry-a-meta-model-approach
#19
Petr Dluhoš, Daniel Schwarz, Wiepke Cahn, Neeltje van Haren, René Kahn, Filip Španiel, Jiří Horáček, Tomáš Kašpárek, Hugo Schnack
One of the biggest problems in automated diagnosis of psychiatric disorders from medical images is the lack of sufficiently large samples for training. Sample size is especially important in the case of highly heterogeneous disorders such as schizophrenia, where machine learning models built on relatively low numbers of subjects may suffer from poor generalizability. Via multicenter studies and consortium initiatives researchers have tried to solve this problem by combining data sets from multiple sites. The necessary sharing of (raw) data is, however, often hindered by legal and ethical issues...
April 17, 2017: NeuroImage
https://www.readbyqxmd.com/read/28426817/evaluation-of-machine-learning-algorithms-and-structural-features-for-optimal-mri-based-diagnostic-prediction-in-psychosis
#20
Raymond Salvador, Joaquim Radua, Erick J Canales-Rodríguez, Aleix Solanes, Salvador Sarró, José M Goikolea, Alicia Valiente, Gemma C Monté, María Del Carmen Natividad, Amalia Guerrero-Pedraza, Noemí Moro, Paloma Fernández-Corcuera, Benedikt L Amann, Teresa Maristany, Eduard Vieta, Peter J McKenna, Edith Pomarol-Clotet
A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with schizophrenia from healthy controls. However, very few of them have also included patients with bipolar disorder, allowing the clinically relevant discrimination between both psychotic diagnostics. To assess the efficacy of sMRI data for diagnostic prediction in psychosis we objectively evaluated the discriminative power of a wide range of commonly used machine learning algorithms (ridge, lasso, elastic net and L0 norm regularized logistic regressions, a support vector classifier, regularized discriminant analysis, random forests and a Gaussian process classifier) on main sMRI features including grey and white matter voxel-based morphometry (VBM), vertex-based cortical thickness and volume, region of interest volumetric measures and wavelet-based morphometry (WBM) maps...
2017: PloS One
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