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Support vector machine

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https://www.readbyqxmd.com/read/28650828/convex-formulation-for-kernel-pca-and-its-use-in-semisupervised-learning
#1
Carlos M Alaiz, Michael Fanuel, Johan A K Suykens
In this brief, kernel principal component analysis (KPCA) is reinterpreted as the solution to a convex optimization problem. Actually, there is a constrained convex problem for each principal component, so that the constraints guarantee that the principal component is indeed a solution, and not a mere saddle point. Although these insights do not imply any algorithmic improvement, they can be used to further understand the method, formulate possible extensions, and properly address them. As an example, a new convex optimization problem for semisupervised classification is proposed, which seems particularly well suited whenever the number of known labels is small...
June 23, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28650800/depth-aware-salient-object-detection-and-segmentation-via-multiscale-discriminative-saliency-fusion-and-bootstrap-learning
#2
Hangke Song, Zhi Liu, Huan Du, Guangling Sun, Olivier Le Meur, Tongwei Ren
This paper proposes a novel depth-aware salient object detection and segmentation framework via multiscale discriminative saliency fusion (MDSF) and bootstrap learning for RGBD images (RGB color images with corresponding Depth maps) and stereoscopic images. By exploiting low-level feature contrasts, mid-level feature weighted factors and high-level location priors, various saliency measures on four classes of features are calculated based on multiscale region segmentation. A random forest regressor is learned to perform the discriminative saliency fusion (DSF) and generate the DSF saliency map at each scale, and DSF saliency maps across multiple scales are combined to produce the MDSF saliency map...
September 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28649752/investigating-dna-rna-and-protein-based-features-as-a-means-to-discriminate-pathogenic-synonymous-variants
#3
Mark Livingstone, Lukas Folkman, Yuedong Yang, Ping Zhang, Matthew Mort, David N Cooper, Yunlong Liu, Bela Stantic, Yaoqi Zhou
Synonymous single nucleotide variants (SNVs), although they do not alter the encoded protein sequences, have been implicated in many genetic diseases. Experimental studies indicate that synonymous SNVs can lead to changes in the secondary and tertiary structures of DNA and RNA, thereby impacting translational efficiency, co-translational protein folding as well as the binding of DNA/RNA-binding proteins. However, the importance of these various features in disease phenotypes is not clearly understood. Here we have built a support vector machine model (termed DDIG-SN) as a means to discriminate disease-causing synonymous variants...
June 25, 2017: Human Mutation
https://www.readbyqxmd.com/read/28649677/outcome-prediction-for-patient-with-high-grade-gliomas-from-brain-functional-and-structural-networks
#4
Luyan Liu, Han Zhang, Islem Rekik, Xiaobo Chen, Qian Wang, Dinggang Shen
High-grade glioma (HGG) is a lethal cancer, which is characterized by very poor prognosis. To help optimize treatment strategy, accurate preoperative prediction of HGG patient's outcome (i.e., survival time) is of great clinical value. However, there are huge individual variability of HGG, which produces a large variation in survival time, thus making prognostic prediction more challenging. Previous brain imaging-based outcome prediction studies relied only on the imaging intensity inside or slightly around the tumor, while ignoring any information that is located far away from the lesion (i...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28649444/reverse-engineering-highlights-potential-principles-of-large-gene-regulatory-network-design-and-learning
#5
Clément Carré, André Mas, Gabriel Krouk
Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions...
2017: NPJ Systems Biology and Applications
https://www.readbyqxmd.com/read/28649205/support-vector-machine-analysis-of-functional-magnetic-resonance-imaging-of-interoception-does-not-reliably-predict-individual-outcomes-of-cognitive-behavioral-therapy-in-panic-disorder-with-agoraphobia
#6
Benedikt Sundermann, Jens Bode, Ulrike Lueken, Dorte Westphal, Alexander L Gerlach, Benjamin Straube, Hans-Ulrich Wittchen, Andreas Ströhle, André Wittmann, Carsten Konrad, Tilo Kircher, Volker Arolt, Bettina Pfleiderer
BACKGROUND: The approach to apply multivariate pattern analyses based on neuro imaging data for outcome prediction holds out the prospect to improve therapeutic decisions in mental disorders. Patients suffering from panic disorder with agoraphobia (PD/AG) often exhibit an increased perception of bodily sensations. The purpose of this investigation was to assess whether multivariate classification applied to a functional magnetic resonance imaging (fMRI) interoception paradigm can predict individual responses to cognitive behavioral therapy (CBT) in PD/AG...
2017: Frontiers in Psychiatry
https://www.readbyqxmd.com/read/28646852/the-facial-expression-of-schizophrenic-patients-applied-with-infrared-thermal-facial-image-sequence
#7
Bo-Lin Jian, Chieh-Li Chen, Wen-Lin Chu, Min-Wei Huang
BACKGROUND: Schizophrenia is a neurological disease characterized by alterations to patients' cognitive functions and emotional expressions. Relevant studies often use magnetic resonance imaging (MRI) of the brain to explore structural differences and responsiveness within brain regions. However, as this technique is expensive and commonly induces claustrophobia, it is frequently refused by patients. Thus, this study used non-contact infrared thermal facial images (ITFIs) to analyze facial temperature changes evoked by different emotions in moderately and markedly ill schizophrenia patients...
June 24, 2017: BMC Psychiatry
https://www.readbyqxmd.com/read/28646763/deep-neural-mapping-support-vector-machines
#8
Yujian Li, Ting Zhang
The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately...
June 21, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28646177/hyperspectral-imaging-for-presymptomatic-detection-of-tobacco-disease-with-successive-projections-algorithm-and-machine-learning-classifiers
#9
Hongyan Zhu, Bingquan Chu, Chu Zhang, Fei Liu, Linjun Jiang, Yong He
We investigated the feasibility and potentiality of presymptomatic detection of tobacco disease using hyperspectral imaging, combined with the variable selection method and machine-learning classifiers. Images from healthy and TMV-infected leaves with 2, 4, and 6 days post infection were acquired by a pushbroom hyperspectral reflectance imaging system covering the spectral range of 380-1023 nm. Successive projections algorithm was evaluated for effective wavelengths (EWs) selection. Four texture features, including contrast, correlation, entropy, and homogeneity were extracted according to grey-level co-occurrence matrix (GLCM)...
June 23, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28644811/applying-pattern-recognition-to-high-resolution-images-to-determine-cellular-signaling-status
#10
Michael F Lohrer, Darrin M Hanna, Yang Liu, Kang-Hsin Wang, Fu-Tong Liu, Ted A Laurence, Gang-Yu Liu
Two frequently used tools to acquire high-resolution images of cells are scanning electron microscopy (SEM) and atomic force microscopy (AFM). The former provides a nanometer resolution view of cellular features rapidly and with high throughput, while the latter enables visualizing hydrated and living cells. In current practice, these images are viewed by eye to determine cellular status, e.g. activated versus resting. Automatic and quantitative data analysis is lacking. This work develops an algorithm of pattern recognition that works very effectively for AFM and SEM images...
June 21, 2017: IEEE Transactions on Nanobioscience
https://www.readbyqxmd.com/read/28644140/a-pathways-based-prediction-model-for-classifying-breast-cancer-subtypes
#11
Tong Wu, Yunfeng Wang, Ronghui Jiang, Xinliang Lu, Jiawei Tian
Breast cancer is highly heterogeneous and is classified into four subtypes characterized by specific biological traits, treatment responses, and clinical prognoses. We performed a systemic analysis of 698 breast cancer patient samples from The Cancer Genome Atlas project database. We identified 136 breast cancer genes differentially expressed among the four subtypes. Based on unsupervised clustering analysis, these 136 core genes efficiently categorized breast cancer patients into the appropriate subtypes. Functional enrichment based on Kyoto Encyclopedia of Genes and Genomes analysis identified six functional pathways regulated by these genes: JAK-STAT signaling, basal cell carcinoma, inflammatory mediator regulation of TRP channels, non-small cell lung cancer, glutamatergic synapse, and amyotrophic lateral sclerosis...
June 17, 2017: Oncotarget
https://www.readbyqxmd.com/read/28643328/drug-repurposing-by-simulating-flow-through-protein-protein-interaction-networks
#12
M Manczinger, V Bodnár, B T Papp, B Sz Bolla, K Szabó, B Balázs, E Csányi, E Szél, G Erős, L Kemény
As drug development is extremely expensive, the identification of novel indications for in-market drugs is financially attractive. Multiple algorithms are used to support such drug repurposing, but highly reliable methods combining simulation of intracellular networks and machine learning are currently not available. We developed an algorithm that simulates drug effects on the flow of information through protein-protein interaction networks, and uses Support Vector Machine to identify potentially effective drugs in our model disease, psoriasis...
June 23, 2017: Clinical Pharmacology and Therapeutics
https://www.readbyqxmd.com/read/28642286/brain-regions-important-for-recovery-after-severe-post-stroke-upper-limb-paresis
#13
Jane M Rondina, Chang-Hyun Park, Nick S Ward
Background The ability to predict outcome after stroke is clinically important for planning treatment and for stratification in restorative clinical trials. In relation to the upper limbs, the main predictor of outcome is initial severity, with patients who present with mild to moderate impairment regaining about 70% of their initial impairment by 3 months post-stroke. However, in those with severe presentations, this proportional recovery applies in only about half, with the other half experiencing poor recovery...
June 22, 2017: Journal of Neurology, Neurosurgery, and Psychiatry
https://www.readbyqxmd.com/read/28641264/a-single-channel-eog-based-speller
#14
Shenghong He, Yuanqing Li
Electrooculography (EOG) signals, which can be used to infer the intentions of a user based on eye movements, are widely used in human-computer interface (HCI) systems. Most existing EOG-based HCI systems incorporate a limited number of commands because they generally associate different commands with a few different types of eye movements, such as looking up, down, left, or right. This paper presents a novel single-channel EOG-based HCI that allows users to spell asynchronously by only blinking. Forty buttons corresponding to 40 characters displayed to the user via a graphical user interface (GUI) are intensified in a random order...
June 15, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28641244/computerized-lung-sound-screening-for-pediatric-auscultation-in-noisy-field-environments
#15
Dimitra Emmanouilidou, Eric D McCollum, Daniel E Park, Mounya Elhilali
GOAL: Chest auscultations offer a non-invasive and low-cost tool for monitoring lung disease. However, they present many shortcomings including inter-listener variability, subjectivity, and vulnerability to noise and distortions. The current work proposes a computer-aided approach to process lung signals acquired in the field under adverse noisy conditions, by improving the signal quality and offering automated iden- tification of abnormal auscultations indicative of respiratory pathologies...
June 19, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28640189/automatic-detection-and-classification-of-pole-like-objects-for-urban-cartography-using-mobile-laser-scanning-data
#16
Celestino Ordóñez, Carlos Cabo, Enoc Sanz-Ablanedo
Mobile laser scanning (MLS) is a modern and powerful technology capable of obtaining massive point clouds of objects in a short period of time. Although this technology is nowadays being widely applied in urban cartography and 3D city modelling, it has some drawbacks that need to be avoided in order to strengthen it. One of the most important shortcomings of MLS data is concerned with the fact that it provides an unstructured dataset whose processing is very time-consuming. Consequently, there is a growing interest in developing algorithms for the automatic extraction of useful information from MLS point clouds...
June 22, 2017: Sensors
https://www.readbyqxmd.com/read/28640181/a-study-for-texture-feature-extraction-of-high-resolution-satellite-images-based-on-a-direction-measure-and-gray-level-co-occurrence-matrix-fusion-algorithm
#17
Xin Zhang, Jintian Cui, Weisheng Wang, Chao Lin
To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image...
June 22, 2017: Sensors
https://www.readbyqxmd.com/read/28639886/a-competing-endogenous-rna-network-identifies-novel-mrna-mirna-and-lncrna-markers-for-the-prognosis-of-diabetic-pancreatic-cancer
#18
Kanyu Yao, Qi Wang, Jianhua Jia, Haiping Zhao
Pancreatic cancer (PaC) is highly associated with diabetes mellitus (DM). However, the mechanisms are insufficient. The study aimed to uncover the underlying regulatory mechanism on diabetic PaC and find novel biomarkers for the disease prognosis. Two RNA-sequencing (RNA-seq) datasets, GSE74629 and GSE15932, as well as relevant data in TCGA were utilized. After pretreatment, differentially expressed genes (DEGs) or miRNAs (DEMs) or lncRNAs (DELs) between diabetic PaC and non-diabetic PaC patients were identified, and further examined for their correlations with clinical information...
June 2017: Tumour Biology: the Journal of the International Society for Oncodevelopmental Biology and Medicine
https://www.readbyqxmd.com/read/28635678/evaluation-of-classifier-performance-for-multiclass-phenotype-discrimination-in-untargeted-metabolomics
#19
Patrick J Trainor, Andrew P DeFilippis, Shesh N Rai
Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k-Nearest Neighbors (k-NN), and Naïve Bayes classification techniques for discrimination...
June 21, 2017: Metabolites
https://www.readbyqxmd.com/read/28635541/association-between-abnormal-brain-functional-connectivity-in-children-and-psychopathology-a-study-based-on-graph-theory-and-machine-learning
#20
João Ricardo Sato, Claudinei Eduardo Biazoli, Giovanni Abrahão Salum, Ary Gadelha, Nicolas Crossley, Gilson Vieira, André Zugman, Felipe Almeida Picon, Pedro Mario Pan, Marcelo Queiroz Hoexter, Edson Amaro, Mauricio Anés, Luciana Monteiro Moura, Marco Antonio Gomes Del'Aquilla, Philip Mcguire, Luis Augusto Rohde, Euripedes Constantino Miguel, Andrea Parolin Jackowski, Rodrigo Affonseca Bressan
OBJECTIVES: One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. Many of these disorders affect brain development and its connectivity. In this study, we propose a novel method for assessing brain networks based on the combination of a graph theory measure (eigenvector centrality) and a one-class support vector machine (OC-SVM). METHODS: We applied this approach to resting-state fMRI data from 622 children and adolescents...
February 8, 2017: World Journal of Biological Psychiatry
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