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https://www.readbyqxmd.com/read/27910886/sequential-information-in-a-great-ape-utterance
#1
Pawel Fedurek, Klaus Zuberbühler, Christoph D Dahl
Birdsong is a prime example of acoustically sophisticated vocal behaviour, but its complexity has evolved mainly through sexual selection to attract mates and repel sexual rivals. In contrast, non-human primate calls often mediate complex social interactions, but are generally regarded as acoustically simple. Here, we examine arguably the most complex call in great ape vocal communication, the chimpanzee (Pan troglodytes schweinfurthii) 'pant hoot'. This signal consists of four acoustically distinct phases: introduction, build-up, climax and let-down...
December 2, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27909886/automatic-snoring-sounds-detection-from-sleep-sounds-via-multi-features-analysis
#2
Can Wang, Jianxin Peng, Lijuan Song, Xiaowen Zhang
Obstructive sleep apnea hypopnea syndrome (OSAHS) is a serious respiratory disorder. Snoring is the most intuitively characteristic symptom of OSAHS. Recently, many studies have attempted to develop snore analysis technology for diagnosing OSAHS. The preliminary and essential step in such diagnosis is to automatically segment snoring sounds from original sleep sounds. This study presents an automatic snoring detection algorithm that detects potential snoring episodes using an adaptive effective-value threshold method, linear and nonlinear feature extraction using maximum power ratio, sum of positive/negative amplitudes, 500 Hz power ratio, spectral entropy (SE) and sample entropy (SampEn), and automatic snore/nonsnore classification using a support vector machine...
December 1, 2016: Australasian Physical & Engineering Sciences in Medicine
https://www.readbyqxmd.com/read/27908705/a-novel-method-for-in-silico-identification-of-regulatory-snps-in-human-genome
#3
Rong Li, Dexing Zhong, Ruiling Liu, Hongqiang Lv, Xinman Zhang, Jun Liu, Jiuqiang Han
Regulatory single nucleotide polymorphisms (rSNPs), kind of functional noncoding genetic variants, can affect gene expression in a regulatory way, and they are thought to be associated with increased susceptibilities to complex diseases. Here a novel computational approach to identify potential rSNPs is presented. Different from most other rSNPs finding methods which based on hypothesis that SNPs causing large allele-specific changes in transcription factor binding affinities are more likely to play regulatory functions, we use a set of documented experimentally verified rSNPs and nonfunctional background SNPs to train classifiers, so the discriminating features are found...
November 28, 2016: Journal of Theoretical Biology
https://www.readbyqxmd.com/read/27908249/automated-system-for-referral-of-cotton-wool-spots
#4
Syed Ali Gohar Naqvi, Hafiz Muhammad Faisal Zafar, Ihsan Ul Haq
BACKGROUND: Cotton-wool spots also referred as soft exudates are the early sign of complications in the eye fundus of the patients suffering from diabetic retinopathy. Early detection of exudates helps in diagnosis of the disease and provides better medical attention. METHODS: In the paper an automated system for detection of soft exudates has been suggested. The system has been developed by the combination of different techniques like scale invariant feature transform (SIFT), Visual Dictionaries, K-means clustering and support vector machine (SVM)...
December 1, 2016: Current Diabetes Reviews
https://www.readbyqxmd.com/read/27907014/classification-and-verification-of-handwritten-signatures-with-time-causal-information-theory-quantifiers
#5
Osvaldo A Rosso, Raydonal Ospina, Alejandro C Frery
We present a new approach for handwritten signature classification and verification based on descriptors stemming from time causal information theory. The proposal uses the Shannon entropy, the statistical complexity, and the Fisher information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results are better than state-of-the-art online techniques that employ higher-dimensional feature spaces which often require specialized software and hardware...
2016: PloS One
https://www.readbyqxmd.com/read/27904990/computer-modeling-in-predicting-the-bioactivity-of-human-5-lipoxygenase-inhibitors
#6
Mengdi Zhang, Zhonghua Xia, Aixia Yan
5-Lipoxygenase (5-LOX) is a key enzyme in the inflammatory path. Inhibitors of 5-LOX are useful for the treatment of diseases like arthritis, cancer, and asthma. We have collected a dataset including 220 human 5-LOX inhibitors for classification. A self-organizing map (SOM), a support vector machine (SVM), and a multilayer perceptron (MLP) algorithm were used to build models with selected descriptors for classifying 5-LOX inhibitors into active and weakly active ones. MACCS fingerprints were used in this model building process...
November 30, 2016: Molecular Diversity
https://www.readbyqxmd.com/read/27903489/finding-important-terms-for-patients-in-their-electronic-health-records-a-learning-to-rank-approach-using-expert-annotations
#7
Jinying Chen, Jiaping Zheng, Hong Yu
BACKGROUND: Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients' notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them...
November 30, 2016: JMIR Medical Informatics
https://www.readbyqxmd.com/read/27900948/prediction-of-antiepileptic-drug-treatment-outcomes-using-machine-learning
#8
Sinisa Colic, Robert G Wither, Min Lang, Liang Zhang, James H Eubanks, Berj L Bardakjian
OBJECTIVE: Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs. APPROACH: Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome...
November 30, 2016: Journal of Neural Engineering
https://www.readbyqxmd.com/read/27899584/cath-an-expanded-resource-to-predict-protein-function-through-structure-and-sequence
#9
Natalie L Dawson, Tony E Lewis, Sayoni Das, Jonathan G Lees, David Lee, Paul Ashford, Christine A Orengo, Ian Sillitoe
The latest version of the CATH-Gene3D protein structure classification database has recently been released (version 4.1, http://www.cathdb.info). The resource comprises over 300 000 domain structures and over 53 million protein domains classified into 2737 homologous superfamilies, doubling the number of predicted protein domains in the previous version. The daily-updated CATH-B, which contains our very latest domain assignment data, provides putative classifications for over 100 000 additional protein domains...
November 28, 2016: Nucleic Acids Research
https://www.readbyqxmd.com/read/27898691/a-personalized-electronic-movie-recommendation-system-based-on-support-vector-machine-and-improved-particle-swarm-optimization
#10
Xibin Wang, Fengji Luo, Ying Qian, Gianluca Ranzi
With the rapid development of ICT and Web technologies, a large an amount of information is becoming available and this is producing, in some instances, a condition of information overload. Under these conditions, it is difficult for a person to locate and access useful information for making decisions. To address this problem, there are information filtering systems, such as the personalized recommendation system (PRS) considered in this paper, that assist a person in identifying possible products or services of interest based on his/her preferences...
2016: PloS One
https://www.readbyqxmd.com/read/27897236/recognition-of-mould-colony-on-unhulled-paddy-based-on-computer-vision-using-conventional-machine-learning-and-deep-learning-techniques
#11
Ke Sun, Zhengjie Wang, Kang Tu, Shaojin Wang, Leiqing Pan
To investigate the potential of conventional and deep learning techniques to recognize the species and distribution of mould in unhulled paddy, samples were inoculated and cultivated with five species of mould, and sample images were captured. The mould recognition methods were built using support vector machine (SVM), back-propagation neural network (BPNN), convolutional neural network (CNN), and deep belief network (DBN) models. An accuracy rate of 100% was achieved by using the DBN model to identify the mould species in the sample images based on selected colour-histogram parameters, followed by the SVM and BPNN models...
November 29, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27895962/texture-analysis-of-supraspinatus-ultrasound-image-for-computer-aided-diagnostic-system
#12
Byung Eun Park, Won Seuk Jang, Sun Kook Yoo
OBJECTIVES: In this paper, we proposed an algorithm for recognizing a rotator cuff supraspinatus tendon tear using a texture analysis based on a histogram, gray level co-occurrence matrix (GLCM), and gray level run length matrix (GLRLM). METHODS: First, we applied a total of 57 features (5 first order descriptors, 40 GLCM features, and 12 GLRLM features) to each rotator cuff region of interest. Our results show that first order statistics (mean, skewness, entropy, energy, smoothness), GLCM (correlation, contrast, energy, entropy, difference entropy, homogeneity, maximum probability, sum average, sum entropy), and GLRLM features are helpful to distinguish a normal supraspinatus tendon and an abnormal supraspinatus tendon...
October 2016: Healthcare Informatics Research
https://www.readbyqxmd.com/read/27893380/area-determination-of-diabetic-foot-ulcer-images-using-a-cascaded-two-stage-svm-based-classification
#13
Lei Wang, Peder Pedersen, Emmanuel Agu, Diane Strong, Bengisu Tulu
It is standard practice for clinicians and nurses to primarily assess patients' wounds via visual examination. This subjective method can be inaccurate in wound assessment and also represents a significant clinical workload. Hence, computer-based systems, especially implemented on mobile devices, can provide automatic, quantitative wound assessment and can thus be valuable for accurately monitoring wound healing status. Out of all wound assessment parameters, the measurement of the wound area is the most suitable for automated analysis...
November 23, 2016: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/27892693/a-graph-approach-to-mining-biological-patterns-in-the-binding-interfaces
#14
Wen Cheng, Changhui Yan
Protein-RNA interactions play important roles in the biological systems. Searching for regular patterns in the Protein-RNA binding interfaces is important for understanding how protein and RNA recognize each other and bind to form a complex. Herein, we present a graph-mining method for discovering biological patterns in the protein-RNA interfaces. We represented known protein-RNA interfaces using graphs and then discovered graph patterns enriched in the interfaces. Comparison of the discovered graph patterns with UniProt annotations showed that the graph patterns had a significant overlap with residue sites that had been proven crucial for the RNA binding by experimental methods...
November 28, 2016: Journal of Computational Biology: a Journal of Computational Molecular Cell Biology
https://www.readbyqxmd.com/read/27892471/combining-multiple-hypothesis-testing-with-machine-learning-increases-the-statistical-power-of-genome-wide-association-studies
#15
Bettina Mieth, Marius Kloft, Juan Antonio Rodríguez, Sören Sonnenburg, Robin Vobruba, Carlos Morcillo-Suárez, Xavier Farré, Urko M Marigorta, Ernst Fehr, Thorsten Dickhaus, Gilles Blanchard, Daniel Schunk, Arcadi Navarro, Klaus-Robert Müller
The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction...
November 28, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27891256/scale-dependent-signal-identification-in-low-dimensional-subspace-motor-imagery-task-classification
#16
Qingshan She, Haitao Gan, Yuliang Ma, Zhizeng Luo, Tom Potter, Yingchun Zhang
Motor imagery electroencephalography (EEG) has been successfully used in locomotor rehabilitation programs. While the noise-assisted multivariate empirical mode decomposition (NA-MEMD) algorithm has been utilized to extract task-specific frequency bands from all channels in the same scale as the intrinsic mode functions (IMFs), identifying and extracting the specific IMFs that contain significant information remain difficult. In this paper, a novel method has been developed to identify the information-bearing components in a low-dimensional subspace without prior knowledge...
2016: Neural Plasticity
https://www.readbyqxmd.com/read/27891045/reinforced-angle-based-multicategory-support-vector-machines
#17
Chong Zhang, Yufeng Liu, Junhui Wang, Hongtu Zhu
The Support Vector Machine (SVM) is a very popular classification tool with many successful applications. It was originally designed for binary problems with desirable theoretical properties. Although there exist various Multicategory SVM (MSVM) extensions in the literature, some challenges remain. In particular, most existing MSVMs make use of k classification functions for a k-class problem, and the corresponding optimization problems are typically handled by existing quadratic programming solvers. In this paper, we propose a new group of MSVMs, namely the Reinforced Angle-based MSVMs (RAMSVMs), using an angle-based prediction rule with k - 1 functions directly...
2016: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/27889654/development-of-a-sugar-binding-residue-prediction-system-from-protein-sequences-using-support-vector-machine
#18
Masaki Banno, Yusuke Komiyama, Wei Cao, Yuya Oku, Kokoro Ueki, Kazuya Sumikoshi, Shugo Nakamura, Tohru Terada, Kentaro Shimizu
Several methods have been proposed for protein-sugar binding site prediction using machine learning algorithms. However, they are not effective to learn various properties of binding site residues caused by various interactions between proteins and sugars. In this study, we classified sugars into acidic and nonacidic sugars and showed that their binding sites have different amino acid occurrence frequencies. By using this result, we developed sugar-binding residue predictors dedicated to the two classes of sugars: an acid sugar binding predictor and a nonacidic sugar binding predictor...
November 9, 2016: Computational Biology and Chemistry
https://www.readbyqxmd.com/read/27889399/a-natural-language-processing-based-model-to-automate-mri-brain-protocol-selection-and-prioritization
#19
Andrew D Brown, Thomas R Marotta
RATIONALE AND OBJECTIVES: Incorrect imaging protocol selection can contribute to increased healthcare cost and waste. To help healthcare providers improve the quality and safety of medical imaging services, we developed and evaluated three natural language processing (NLP) models to determine whether NLP techniques could be employed to aid in clinical decision support for protocoling and prioritization of magnetic resonance imaging (MRI) brain examinations. MATERIALS AND METHODS: To test the feasibility of using an NLP model to support clinical decision making for MRI brain examinations, we designed three different medical imaging prediction tasks, each with a unique outcome: selecting an examination protocol, evaluating the need for contrast administration, and determining priority...
November 23, 2016: Academic Radiology
https://www.readbyqxmd.com/read/27887571/lbsizecleav-improved-support-vector-machine-svm-based-prediction-of-dicer-cleavage-sites-using-loop-bulge-length
#20
Yu Bao, Morihiro Hayashida, Tatsuya Akutsu
BACKGROUND: Dicer is necessary for the process of mature microRNA (miRNA) formation because the Dicer enzyme cleaves pre-miRNA correctly to generate miRNA with correct seed regions. Nonetheless, the mechanism underlying the selection of a Dicer cleavage site is still not fully understood. To date, several studies have been conducted to solve this problem, for example, a recent discovery indicates that the loop/bulge structure plays a central role in the selection of Dicer cleavage sites...
November 25, 2016: BMC Bioinformatics
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