Read by QxMD icon Read


Farzaneh Karimi-Alavijeh, Saeed Jalili, Masoumeh Sadeghi
BACKGROUND: Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome...
May 2016: ARYA Atherosclerosis
Hadi Ratham Al Ghayab, Yan Li, Shahab Abdulla, Mohammed Diykh, Xiangkui Wan
Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. Secondly, the sequential feature selection (SFS) algorithm is applied to select the key features and to reduce the dimensionality of the data...
June 2016: Brain Informatics
Renu Vyas, Sanket Bapat, Esha Jain, Muthukumarasamy Karthikeyan, Sanjeev Tambe, Bhaskar D Kulkarni
In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein-protein interactions therefore assume significance...
September 30, 2016: Computational Biology and Chemistry
Wei Lan, Min Li, Kaijie Zhao, Jin Liu, Fang-Xiang Wu, Yi Pan, Jianxin Wang
MOTIVATION: Increasing evidences have demonstrated that long noncoding RNAs (lncRNAs) play important roles in many human diseases. Therefore, predicting novel lncRNA-disease associations would contribute to dissect the complex mechanisms of disease pathogenesis. Some computational methods have been developed to infer lncRNA-disease associations. However, most of these methods infer lncRNA-disease associations only based on single data resource. RESULTS: In this paper, we propose a new computational method to predict lncRNA-disease associations by integrating multiple biological data resources...
October 14, 2016: Bioinformatics
Jun Hu, Yang Li, Ming Zhang, Xibei Yang, Hong-Bin Shen, Dong-Jun Yu
Protein-DNA interactions are ubiquitous in a wide variety of biological processes. Correctly locating DNA-binding residues solely from protein sequences is an important but challenging task for protein function annotations and drug discovery, especially in the post-genomic era where large volumes of protein sequences have quickly accumulated. In this study, we report a new predictor, named TargetDNA, for targeting protein-DNA binding residues from primary sequences. TargetDNA uses a protein's evolutionary information and its predicted solvent accessibility as two base features and employs a centered linear kernel alignment algorithm to learn the weights for weightedly combining the two features...
October 11, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Pol Cirujeda, Yashin Dicente Cid, Henning Muller, Daniel Rubin, Todd A Aguilera, Billy W Loo, Maximilian Diehn, Xavier Binefa, Adrien Depeursinge
This paper proposes a novel imaging biomarker of lung cancer relapse from 3-D texture analysis of CT images. Three-dimensional morphological nodular tissue properties are described in terms of 3-D Riesz-wavelets. The responses of the latter are aggregated within nodular regions by means of feature covariances, which leverage rich intra- and inter- variations of the feature space dimensions. When compared to the classical use of the average for feature aggregation, feature covariances preserve spatial co-variations between features...
July 18, 2016: IEEE Transactions on Medical Imaging
Shuai-Bing He, Man-Man Li, Bai-Xia Zhang, Xiao-Tong Ye, Ran-Feng Du, Yun Wang, Yan-Jiang Qiao
During the past decades, there have been continuous attempts in the prediction of metabolism mediated by cytochrome P450s (CYP450s) 3A4, 2D6, and 2C9. However, it has indeed remained a huge challenge to accurately predict the metabolism of xenobiotics mediated by these enzymes. To address this issue, microsomal metabolic reaction system (MMRS)-a novel concept, which integrates information about site of metabolism (SOM) and enzyme-was introduced. By incorporating the use of multiple feature selection (FS) techniques (ChiSquared (CHI), InfoGain (IG), GainRatio (GR), Relief) and hybrid classification procedures (Kstar, Bayes (BN), K-nearest neighbours (IBK), C4...
October 9, 2016: International Journal of Molecular Sciences
Pedro GuimarĂ£es, Jeffrey Wigdahl, Alfredo Ruggeri
PURPOSE: We describe a novel fully automatic method capable of tracing the subbasal plexus nerves from human corneal confocal images. METHODS: Following an increasing interest in the automatic analysis of corneal nerves, a few approaches have been proposed. These, however, cannot cope with large images, such as mosaics, in due time. The rationale of the proposed method is to minimize required computing time while still providing accurate results. Our method consists of two sequential steps - a thresholding step followed by a supervised classification...
September 2016: Translational Vision Science & Technology
Christian Keinki, Richard Zowalla, Martin Wiesner, Marie Jolin Koester, Jutta Huebner
The improvement of health literacy in general and the information of individual patient is a major concern of the German national cancer plan and similar initiatives in other western countries. The aim of our study was to assess the readability and understandability of information booklets for cancer patients available at German Web sites. A support vector machine (SVM) was used to discriminate between laymen- and expert-centric patient information booklets about nine most common tumor types. All booklets had to be available for free at the Internet...
October 10, 2016: Journal of Cancer Education: the Official Journal of the American Association for Cancer Education
Noman Naseer, Nauman Khalid Qureshi, Farzan Majeed Noori, Keum-Shik Hong
We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks...
2016: Computational Intelligence and Neuroscience
Shunian Xiang, Ke Liu, Zhangming Yan, Yaou Zhang, Zhirong Sun
N6-Methyladenosine (m6A) is the most common mRNA modification; it occurs in a wide range of taxon and is associated with many key biological processes. High-throughput experiments have identified m6A-peaks and sites across the transcriptome, but studies of m6A sites at the transcriptome-wide scale are limited to a few species and tissue types. Therefore, the computational prediction of mRNA m6A sites has become an important strategy. In this study, we integrated multiple features of mRNA (flanking sequences, local secondary structure information, and relative position information) and trained a SVM classifier to predict m6A sites in mammalian mRNA sequences...
2016: PloS One
Vanya Van Belle, Ben Van Calster, Sabine Van Huffel, Johan A K Suykens, Paulo Lisboa
PROBLEM SETTING: Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models...
2016: PloS One
Tao Zhou, Huiling Lu, Junjie Zhang, Hongbin Shi
In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detection algorithm is proposed based on SVM and CT image feature-level fusion with rough sets. Firstly, CT images of pulmonary nodule are analyzed, and 42-dimensional feature components are extracted, including six new 3-dimensional features proposed by this paper and others 2-dimensional and 3-dimensional features...
2016: BioMed Research International
Qiuling Hou, Meng Lv, Ling Zhen, Ling Jing
Although support vector machine (SVM) has become a powerful tool for pattern classification and regression, a major disadvantage is it fails to exploit the underlying correlation between any pair of data points as much as possible. Inspired by the modified pairwise constraints trick, in this paper, we propose a novel classifier termed as support vector machine with hypergraph-based pairwise constraints to improve the performance of the classical SVM by introducing a new regularization term with hypergraph-based pairwise constraints (HPC)...
2016: SpringerPlus
Qiannan Jiang, Mingzhou Liu, Xiaoqiao Wang, Maogen Ge, Ling Lin
The observation, decomposition and record of motion are usually accomplished through artificial means during the process of motion analysis. This method not only has a heavy workload, its efficiency is also very low. To solve this problem, this paper proposes a novel method to segment and recognize continuous human motion automatically based on machine vision for mechanical assembly operation. First, the content-based dynamic key frame extraction technology was utilized to extract key frames from video stream, and then automatic segmentation of action was implemented...
2016: SpringerPlus
Jiang-Lin Li, Da-Wen Sun, Hongbin Pu, Digvir S Jayas
Surface-enhanced Raman scattering (SERS) imaging coupling with multivariate analysis in spectral region of 200 to 1800cm(-1) was developed to quantify and visualize thiophanate-methyl (TM) and its metabolite carbendazim residues in red bell pepper (Capsicum annuum L.). Least squares support vector machines (LS-SVM) and support vector machines (SVM) models based on seven optimized characteristic peaks that showed SERS effects of TM and its metabolite carbendazim residues were employed to establish prediction models...
March 1, 2017: Food Chemistry
Meijun Sun, Dong Zhang, Li Liu, Zheng Wang
Hyperspectral imaging (HSI) in the near-infrared (NIR) region (900-1700nm) was used for non-intrusive quality measurements (of sweetness and texture) in melons. First, HSI data from melon samples were acquired to extract the spectral signatures. The corresponding sample sweetness and hardness values were recorded using traditional intrusive methods. Partial least squares regression (PLSR), principal component analysis (PCA), support vector machine (SVM), and artificial neural network (ANN) models were created to predict melon sweetness and hardness values from the hyperspectral data...
March 1, 2017: Food Chemistry
Wei Wang, Xiaolei Feng, Xiaoran Duan, Shanjuan Tan, Sihua Wang, Tuanwei Wang, Feifei Feng, Yiming Wu, Yongjun Wu
OBJECTIVE: To identify the significance of a support vector machine (SVM) model and a decision tree (DT) model for the diagnosis of lung cancer combined with the detection of fragile histidine triad (FHIT), RAS association domain family 1 (RASSF1A) and cyclin-dependent kinase inhibitor 2A (p16) promoter methylation levels and relative telomere length (RTL) of white blood cells from peripheral blood DNA. METHODS: The levels of p16, RASSF1A and FHIT promoter methylation and the RTL of white blood cells in peripheral blood DNA of 200 healthy individuals and 200 lung cancer patients were analyzed by SYBR Green-based quantitative methylation-specific PCR and quantitative PCR...
September 24, 2016: International Journal of Biological Markers
Alborz Feizi, Yibo Zhang, Alon Greenbaum, Alex Guziak, Michelle Luong, Raymond Yan Lok Chan, Brandon Berg, Haydar Ozkan, Wei Luo, Michael Wu, Yichen Wu, Aydogan Ozcan
Monitoring yeast cell viability and concentration is important in brewing, baking and biofuel production. However, existing methods of measuring viability and concentration are relatively bulky, tedious and expensive. Here we demonstrate a compact and cost-effective automatic yeast analysis platform (AYAP), which can rapidly measure cell concentration and viability. AYAP is based on digital in-line holography and on-chip microscopy and rapidly images a large field-of-view of 22.5 mm(2). This lens-free microscope weighs 70 g and utilizes a partially-coherent illumination source and an opto-electronic image sensor chip...
September 30, 2016: Lab on a Chip
Wanmao Ni, Beili Hu, Cuiping Zheng, Yin Tong, Lei Wang, Qing-Qing Li, Xiangmin Tong, Yong Han
We investigated the ability of support vector machines (SVM) to analyze minimal residual disease (MRD) in flow cytometry data from patients with acute myeloid leukemia (AML) automatically, objectively and standardly. The initial disease data and MRD review data in the form of 159 flow cytometry standard 3.0 files from 36 CD7-positive AML patients in whom MRD was detected more than once were exported. SVM was used for training with setting the initial disease data to 1 as the flag and setting 15 healthy persons to set 0 as the flag...
October 4, 2016: Oncotarget
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"