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https://www.readbyqxmd.com/read/28822161/-synergistic-mechanism-of-traditional-chinese-medicine-based-on-target-combination-of-pept1-and-ppar%C3%AE
#1
Lian-Sheng Qiao, Yan-Kun Chen, Gang-Gang Luo, Fang Lu, Si-Jia Liu, Gong-Yu Li, Yan-Ling Zhang
Synergistic effect is main pharmacological mechanism of traditional Chinese medicine(TCM). The research method based on the key targets combination is an important method to explore the synergistic effect of TCM. Peptide transporter 1 (PepT1) is an essential target for drug uptake into the bloodstream, accounting for about 50% of the total transporter protein content from the small intestine. Peroxisome proliferator-activated receptor α(PPARα) is the lipid-lowering target of fibrates, which have a good hypolipidemic effect by activating PPARα...
June 2017: Zhongguo Zhong Yao za Zhi, Zhongguo Zhongyao Zazhi, China Journal of Chinese Materia Medica
https://www.readbyqxmd.com/read/28820478/detection-of-interactions-between-proteins-by-using-legendre-moments-descriptor-to-extract-discriminatory-information-embedded-in-pssm
#2
Yan-Bin Wang, Zhu-Hong You, Li-Ping Li, Yu-An Huang, Hai-Cheng Yi
Protein-protein interactions (PPIs) play a very large part in most cellular processes. Although a great deal of research has been devoted to detecting PPIs through high-throughput technologies, these methods are clearly expensive and cumbersome. Compared with the traditional experimental methods, computational methods have attracted much attention because of their good performance in detecting PPIs. In our work, a novel computational method named as PCVM-LM is proposed which combines the probabilistic classification vector machine (PCVM) model and Legendre moments (LMs) to predict PPIs from amino acid sequences...
August 18, 2017: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
https://www.readbyqxmd.com/read/28817075/discrimination-of-transgenic-maize-kernel-using-nir-hyperspectral-imaging-and-multivariate-data-analysis
#3
Xuping Feng, Yiying Zhao, Chu Zhang, Peng Cheng, Yong He
There are possible environmental risks related to gene flow from genetically engineered organisms. It is important to find accurate, fast, and inexpensive methods to detect and monitor the presence of genetically modified (GM) organisms in crops and derived crop products. In the present study, GM maize kernels containing both cry1Ab/cry2Aj-G10evo proteins and their non-GM parents were examined by using hyperspectral imaging in the near-infrared (NIR) range (874.41-1733.91 nm) combined with chemometric data analysis...
August 17, 2017: Sensors
https://www.readbyqxmd.com/read/28815921/structure-modification-toward-applicability-domain-of-a-qsar-qspr-model-considering-activity-property
#4
Shoki Ochi, Tomoyuki Miyao, Kimito Funatsu
In drug and material design, the activity and property values of the designed chemical structures can be predicted by quantitative structure-activity and structure-property relationship (QSAR/QSPR) models. When a QSAR/QSPR model is applied to chemical structures, its applicability domain (AD) must be considered. The predicted activity/property values are only reliable for chemical structures inside the AD. Chemical structures outside the AD are usually neglected, as the predicted values are unreliable. The purpose of this study is to develop a methodology for obtaining novel chemical structures with the desired activity or property based on a QSAR/QSPR model by making use of the neglected structures...
August 16, 2017: Molecular Informatics
https://www.readbyqxmd.com/read/28813873/automation-of-motor-dexterity-assessment
#5
Patrick Heyer, Luis R Castrejon, Felipe Orihuela-Espina, Luis Enrique Sucar
Motor dexterity assessment is regularly performed in rehabilitation wards to establish patient status and automatization for such routinary task is sought. A system for automatizing the assessment of motor dexterity based on the Fugl-Meyer scale and with loose restrictions on sensing technologies is presented. The system consists of two main elements: 1) A data representation that abstracts the low level information obtained from a variety of sensors, into a highly separable low dimensionality encoding employing t-distributed Stochastic Neighbourhood Embedding, and, 2) central to this communication, a multi-label classifier that boosts classification rates by exploiting the fact that the classes corresponding to the individual exercises are naturally organized as a network...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813864/gait-assessment-system-based-on-novel-gait-variability-measures
#6
Xingchen Wang, Danijela Ristic-Durrant, Matthias Spranger, Axel Graser
In this paper, a novel gait assessment system based on measures of gait variability reflected through the variability of shapes of gait cycles trajectories is proposed. The presented gait assessment system is based on SVM (support vector machine) classifier and on gait variability-based features calculated from the hip and knee joint angle trajectories recorded using wearable IMUs during walking trials. A system classifier was trained to distinguish healthy gait patterns from the pathological ones. The features were extracted by calculating the distances between the joint trajectories of the individual gait cycles using 4 different distance functions...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813811/a-multichannel-near-infrared-spectroscopy-triggered-robotic-hand-rehabilitation-system-for-stroke-patients
#7
Jongseung Lee, Nobutaka Mukae, Jumpei Arata, Hiroyuki Iwata, Keiji Iramina, Koji Iihara, Makoto Hashizume
There is a demand for a new neurorehabilitation modality with a brain-computer interface for stroke patients with insufficient or no remaining hand motor function. We previously developed a robotic hand rehabilitation system triggered by multichannel near-infrared spectroscopy (NIRS) to address this demand. In a preliminary prototype system, a robotic hand orthosis, providing one degree-of-freedom motion for a hand's closing and opening, is triggered by a wireless command from a NIRS system, capturing a subject's motor cortex activation...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813803/application-of-support-vector-machines-in-detecting-hand-grasp-gestures-using-a-commercially-off-the-shelf-wireless-myoelectric-armband
#8
Farshid Amirabdollahian, Michael L Walters
The propose of this study was to assess the feasibility of using support vector machines in analysing myoelectric signals acquired using an off the shelf device, the Myo armband from Thalmic Lab, when performing hand grasp gestures. Participants (n = 26) took part in the study wearing the armband and producing a series of required gestures. Support vector machines were used to train a model using participant training values, and to classify gestures produced by the same participants. Different Kernel functions and electrode combinations were studied...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813000/an-ameliorated-prediction-of-drug-target-interactions-based-on-multi-scale-discrete-wavelet-transform-and-network-features
#9
Cong Shen, Yijie Ding, Jijun Tang, Xinying Xu, Fei Guo
The prediction of drug-target interactions (DTIs) via computational technology plays a crucial role in reducing the experimental cost. A variety of state-of-the-art methods have been proposed to improve the accuracy of DTI predictions. In this paper, we propose a kind of drug-target interactions predictor adopting multi-scale discrete wavelet transform and network features (named as DAWN) in order to solve the DTIs prediction problem. We encode the drug molecule by a substructure fingerprint with a dictionary of substructure patterns...
August 16, 2017: International Journal of Molecular Sciences
https://www.readbyqxmd.com/read/28812373/evaluation-of-factors-in-development-of-vis-nir-spectroscopy-models-for-discriminating-pse-dfd-and-normal-broiler-breast-meat
#10
Hongzhe Jiang, Seung-Chul Yoon, Hong Zhuang, Wei Wang, Yi Yang
1. To evaluate the performance of visible and near-infrared (Vis/NIR) spectroscopic models for discriminating true pale, soft and exudative (PSE), normal and dark, firm and dry (DFD) broiler breast meat in different conditions of preprocessing methods, spectral ranges, characteristic wavelengths selection and water-holding capacity (WHC) indexes. 2. Quality attributes of 214 intact chicken fillets (pectoralis major), such as lightness (L*), pH, and WHC indicators including drip loss (DL), water gain (WG), and expressible fluid (EF) were measured...
August 16, 2017: British Poultry Science
https://www.readbyqxmd.com/read/28811820/modified-mahalanobis-taguchi-system-for-imbalance-data-classification
#11
Mahmoud El-Banna
The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary classification algorithms to handle imbalance data. Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification. In this paper, a nonlinear optimization model is formulated based on minimizing the distance between MTS Receiver Operating Characteristics (ROC) curve and the theoretical optimal point named Modified Mahalanobis Taguchi System (MMTS). To validate the MMTS classification efficacy, it has been benchmarked with Support Vector Machines (SVMs), Naive Bayes (NB), Probabilistic Mahalanobis Taguchi Systems (PTM), Synthetic Minority Oversampling Technique (SMOTE), Adaptive Conformal Transformation (ACT), Kernel Boundary Alignment (KBA), Hidden Naive Bayes (HNB), and other improved Naive Bayes algorithms...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28811508/discrimination-of-plant-root-zone-water-status-in-greenhouse-production-based-on-phenotyping-and-machine-learning-techniques
#12
Doudou Guo, Jiaxiang Juan, Liying Chang, Jingjin Zhang, Danfeng Huang
Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all...
August 15, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28809717/multiview-privileged-support-vector-machines
#13
Jingjing Tang, Yingjie Tian, Peng Zhang, Xiaohui Liu
Multiview learning (MVL), by exploiting the complementary information among multiple feature sets, can improve the performance of many existing learning tasks. Support vector machine (SVM)-based models have been frequently used for MVL. A typical SVM-based MVL model is SVM-2K, which extends SVM for MVL by using the distance minimization version of kernel canonical correlation analysis. However, SVM-2K cannot fully unleash the power of the complementary information among different feature views. Recently, a framework of learning using privileged information (LUPI) has been proposed to model data with complementary information...
August 11, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28808813/maximized-inter-class-weighted-mean-for-fast-and-accurate-mitosis-cells-detection-in-breast-cancer-histopathology-images
#14
Ramin Nateghi, Habibollah Danyali, Mohammad Sadegh Helfroush
Based on the Nottingham criteria, the number of mitosis cells in histopathological slides is an important factor in diagnosis and grading of breast cancer. For manual grading of mitosis cells, histopathology slides of the tissue are examined by pathologists at 40× magnification for each patient. This task is very difficult and time-consuming even for experts. In this paper, a fully automated method is presented for accurate detection of mitosis cells in histopathology slide images. First a method based on maximum-likelihood is employed for segmentation and extraction of mitosis cell...
August 14, 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28807134/classification-of-suicide-attempters-in-schizophrenia-using-sociocultural-and-clinical-features-a-machine-learning-approach
#15
Nuwan C Hettige, Thai Binh Nguyen, Chen Yuan, Thanara Rajakulendran, Jermeen Baddour, Nikhil Bhagwat, Ali Bani-Fatemi, Aristotle N Voineskos, M Mallar Chakravarty, Vincenzo De Luca
OBJECTIVE: Suicide is a major concern for those afflicted by schizophrenia. Identifying patients at the highest risk for future suicide attempts remains a complex problem for psychiatric interventions. Machine learning models allow for the integration of many risk factors in order to build an algorithm that predicts which patients are likely to attempt suicide. Currently it is unclear how to integrate previously identified risk factors into a clinically relevant predictive tool to estimate the probability of a patient with schizophrenia for attempting suicide...
July 2017: General Hospital Psychiatry
https://www.readbyqxmd.com/read/28807038/nlr-mlp-svm-and-lda-a-comparative-analysis-on-emg-data-from-people-with-trans-radial-amputation
#16
Alberto Dellacasa Bellingegni, Emanuele Gruppioni, Giorgio Colazzo, Angelo Davalli, Rinaldo Sacchetti, Eugenio Guglielmelli, Loredana Zollo
BACKGROUND: Currently, the typically adopted hand prosthesis surface electromyography (sEMG) control strategies do not provide the users with a natural control feeling and do not exploit all the potential of commercially available multi-fingered hand prostheses. Pattern recognition and machine learning techniques applied to sEMG can be effective for a natural control based on the residual muscles contraction of amputated people corresponding to phantom limb movements. As the researches has reached an advanced grade accuracy, these algorithms have been proved and the embedding is necessary for the realization of prosthetic devices...
August 14, 2017: Journal of Neuroengineering and Rehabilitation
https://www.readbyqxmd.com/read/28806936/enhancement-of-hepatitis-virus-immunoassay-outcome-predictions-in-imbalanced-routine-pathology-data-by-data-balancing-and-feature-selection-before-the-application-of-support-vector-machines
#17
Alice M Richardson, Brett A Lidbury
BACKGROUND: Data mining techniques such as support vector machines (SVMs) have been successfully used to predict outcomes for complex problems, including for human health. Much health data is imbalanced, with many more controls than positive cases. METHODS: The impact of three balancing methods and one feature selection method is explored, to assess the ability of SVMs to classify imbalanced diagnostic pathology data associated with the laboratory diagnosis of hepatitis B (HBV) and hepatitis C (HCV) infections...
August 14, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28806759/peculiar-genes-selection-a-new-features-selection-method-to-improve-classification-performances-in-imbalanced-data-sets
#18
Federica Martina, Marco Beccuti, Gianfranco Balbo, Francesca Cordero
High-Throughput technologies provide genomic and trascriptomic data that are suitable for biomarker detection for classification purposes. However, the high dimension of the output of such technologies and the characteristics of the data sets analysed represent an issue for the classification task. Here we present a new feature selection method based on three steps to detect class-specific biomarkers in case of high-dimensional data sets. The first step detects the differentially expressed genes according to the experimental conditions tested in the experimental design, the second step filters out the features with low discriminative power and the third step detects the class-specific features and defines the final biomarker as the union of the class-specific features...
2017: PloS One
https://www.readbyqxmd.com/read/28802824/highly-accurate-prediction-of-protein-self-interactions-by-incorporating-the-average-block-and-pssm-information-into-the-general-pseaac
#19
Jing-Xuan Zhai, Tian-Jie Cao, Ji-Yong An, Yong-Tao Bian
It is a challenging task for fundamental research whether proteins can interact with their partners. Protein self-interaction (SIP) is a special case of PPIs, which plays a key role in the regulation of cellular functions. Due to the limitations of experimental self-interaction identification, it is very important to develop an effective biological tool for predicting SIPs based on protein sequences. In the study, we developed a novel computational method called RVM-AB that combines the Relevance Vector Machine (RVM) model and Average Blocks (AB) for detecting SIPs from protein sequences...
August 9, 2017: Journal of Theoretical Biology
https://www.readbyqxmd.com/read/28800580/establishing-a-classification-system-for-high-fall-risk-among-inpatients-using-support-vector-machines
#20
Shinichiroh Yokota, Miyoko Endo, Kazuhiko Ohe
We constructed a model using a support vector machine to determine whether an inpatient will suffer a fall on a given day, depending on patient status on the previous day. Using fall report data from our own facility and intensity-of-nursing-care-needs data accumulated through hospital information systems, a dataset comprising approximately 1.2 million patient-days was created. Approximately 50% of the dataset was used as training and testing data. A multistep grid search was conducted using the semicomprehensive combination of three parameters...
August 2017: Computers, Informatics, Nursing: CIN
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