Evaluating the absolute quality of a single protein model using structural features and support vector machines

2009 ◽  
Vol 75 (3) ◽  
pp. 638-647 ◽  
Author(s):  
Zheng Wang ◽  
Allison N. Tegge ◽  
Jianlin Cheng
Keyword(s):  
Support Vector Machines ◽  
Structural Features ◽  
Support Vector ◽  
Protein Model ◽  
Vector Machines ◽  
Single Protein ◽  
The Absolute

scholarly journals Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis

Foods ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2723
Author(s):  
Evgenia D. Spyrelli ◽  
Christina Papachristou ◽  
George-John E. Nychas ◽  
Efstathios Z. Panagou
Keyword(s):  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Microbiological Quality ◽  
Poultry Meat ◽  
Support Vector ◽  
Classification Models ◽  
Pseudomonas Spp ◽  
Vector Machines ◽  
Ft Ir

Fourier transform infrared spectroscopy (FT-IR) and multispectral imaging (MSI) were evaluated for the prediction of the microbiological quality of poultry meat via regression and classification models. Chicken thigh fillets (n = 402) were subjected to spoilage experiments at eight isothermal and two dynamic temperature profiles. Samples were analyzed microbiologically (total viable counts (TVCs) and Pseudomonas spp.), while simultaneously MSI and FT-IR spectra were acquired. The organoleptic quality of the samples was also evaluated by a sensory panel, establishing a TVC spoilage threshold at 6.99 log CFU/cm2. Partial least squares regression (PLS-R) models were employed in the assessment of TVCs and Pseudomonas spp. counts on chicken’s surface. Furthermore, classification models (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs), and quadratic support vector machines (QSVMs)) were developed to discriminate the samples in two quality classes (fresh vs. spoiled). PLS-R models developed on MSI data predicted TVCs and Pseudomonas spp. counts satisfactorily, with root mean squared error (RMSE) values of 0.987 and 1.215 log CFU/cm2, respectively. SVM model coupled to MSI data exhibited the highest performance with an overall accuracy of 94.4%, while in the case of FT-IR, improved classification was obtained with the QDA model (overall accuracy 71.4%). These results confirm the efficacy of MSI and FT-IR as rapid methods to assess the quality in poultry products.

Adaptive Two-Pass Median Filter Based on Support Vector Machines for Image Restoration

2004 ◽  
Vol 16 (2) ◽  
pp. 333-354 ◽  
Author(s):  
Tzu-Chao Lin ◽  
Pao-Ta Yu
Keyword(s):  
Support Vector Machines ◽  
Image Restoration ◽  
Impulse Noise ◽  
Median Filter ◽  
Support Vector ◽  
New Approach ◽  
Median Filters ◽  
Vector Machines ◽  
Impulse Detector

In this letter, a novel adaptive filter, the adaptive two-pass median (ATM) filter based on support vector machines (SVMs), is proposed to preserve more image details while effectively suppressing impulse noise for image restoration. The proposed filter is composed of a noise decision maker and two-pass median filters. Our new approach basically uses an SVM impulse detector to judge whether the input pixel is noise. If a pixel is detected as a corrupted pixel, the noise-free reduction median filter will be triggered to replace it. Otherwise, it remains unchanged. Then, to improve the quality of the restored image, a decision impulse filter is put to work in the second-pass filtering procedure. As for the noise suppressing both fixed-valued and random-valued impulses without degrading the quality of the fine details, the results of our extensive experiments demonstrate that the proposed filter outperforms earlier median-based filters in the literature. Our new filter also provides excellent robustness at various percentages of impulse noise.

scholarly journals Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Mustafa Serter Uzer ◽  
Nihat Yilmaz ◽  
Onur Inan
Keyword(s):  
Feature Selection ◽  
Support Vector Machines ◽  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
Feature Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Bee Colony ◽  
Vector Machines

This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.

scholarly journals Deep convolutional neural networks for predicting the quality of single protein structural models

2019 ◽  
Author(s):  
Jie Hou ◽  
Renzhi Cao ◽  
Jianlin Cheng
Keyword(s):  
Structure Prediction ◽  
Structural Model ◽  
Structural Models ◽  
Structural Features ◽  
Fixed Number ◽  
Deep Convolutional Neural Networks ◽  
Global Quality ◽  
Protein Model ◽  
Single Protein

AbstractPredicting the global quality and local (residual-specific) quality of a single protein structural model is important for protein structure prediction and application. In this work, we developed a deep one-dimensional convolutional neural network (1DCNN) that predicts the absolute local quality of a single protein model as well as two 1DCNNs to predict both local and global quality simultaneously through a novel multi-task learning framework. The networks accept sequential and structural features (i.e. amino acid sequence, agreement of secondary structure and solvent accessibilities, residual disorder properties and Rosetta energies) of a protein model of any size as input to predict its quality, which is different from existing methods using a fixed number of hand-crafted features as input. Our three methods (InteractQA-net, JointQA-net and LocalQA-net) were trained on the structural models of the single-domain protein targets of CASP8, 9, 10 and evaluated on the models of CASP11 and CASP12 targets. The results show that the performance of our deep learning methods is comparable to the state-of-the-art quality assessment methods. Our study also demonstrates that combining local and global quality predictions together improves the global quality prediction accuracy. The source code and executable of our methods are available at:https://github.com/multicom-toolbox/DeepCovQA

A Comparison of Quadratic Programming Solvers in Support Vector Machines Training

2012 ◽  
Author(s):  
N. M. Zaki ◽  
S. Deris ◽  
K. K. Chin
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Quadratic Programming ◽  
Key Words ◽  
Execution Time ◽  
Practical Method ◽  
Support Vector ◽  
Training Time ◽  
Vector Machines

Penyelesaian atur cara kuadratik yang sangat besar diperlukan untuk melatih Support Vector Machine. Tiga cara penyelesaian atur cara kuadratik yang berbeza telah digunakan untuk melaksanakan latihan Support Vector Machine bagi mengkaji keberkesanannya ke atas Support Vector Machine. Prestasi bagi kesemua penyelesaian telah dikaji dan dianalisis dari segi masa pelaksanaan dan kualiti penyelesaian. Kaedah praktikal untuk mengurangkan masa latihan tersebut telah dikaji sepenuhnya. Kata kunci: Support vector machines, atur cara kuadratik Training a Support Vector Machine requires the solution of a very large quadratic programming problem. In order to study the influence of a particular quadratic programming solver on the Support Vector Machine, three different quadratic programming solvers are used to perform the Support Vector Machine training. The performance of these solvers in term of execution time and quality of the solutions are analyzed and compared. A practical method to reduce the training time is investigated. Key words: Support vector machines, quadratic programming

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