scholarly journals QuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Composition

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Chi-Hua Tung ◽  
Chi-Wei Chen ◽  
Ren-Chao Guo ◽  
Hui-Fuang Ng ◽  
Yen-Wei Chu
Keyword(s):  
Quaternary Structure ◽  
Predictive Ability ◽  
Support Vector ◽  
Functional Domain ◽  
Prediction System ◽  
Functional Protein ◽  
Domain Composition ◽  
Vector Machines ◽  
Quaternary Structures ◽  
Quaternary Assembly

Background. Quaternary structures of proteins are closely relevant to gene regulation, signal transduction, and many other biological functions of proteins. In the current study, a new method based on protein-conserved motif composition in block format for feature extraction is proposed, which is termed block composition.Results. The protein quaternary assembly states prediction system which combines blocks with functional domain composition, called QuaBingo, is constructed by three layers of classifiers that can categorize quaternary structural attributes of monomer, homooligomer, and heterooligomer. The building of the first layer classifier uses support vector machines (SVM) based on blocks and functional domains of proteins, and the second layer SVM was utilized to process the outputs of the first layer. Finally, the result is determined by the Random Forest of the third layer. We compared the effectiveness of the combination of block composition, functional domain composition, and pseudoamino acid composition of the model. In the 11 kinds of functional protein families, QuaBingo is 23% of Matthews Correlation Coefficient (MCC) higher than the existing prediction system. The results also revealed the biological characterization of the top five block compositions.Conclusions. QuaBingo provides better predictive ability for predicting the quaternary structural attributes of proteins.

scholarly journals Prediction of Cancer and Suggestion of Therapies

2020 ◽  
Author(s):  
Bhavani M ◽  
Pavithra V ◽  
Monesh R
Keyword(s):  
Attribute Selection ◽  
Support Vector ◽  
Data Mining Algorithm ◽  
Prediction System ◽  
Cancer Prediction ◽  
Mining Algorithm ◽  
Vector Machines ◽  
The Common ◽  
Genetic And Environmental Factors ◽  
Selection Algorithms

Cancer is becoming one among the common diseases in day to today life, determining cancer in an earlier stage is still problematic. Identification of genetic and environmental factors is necessary to predict the type of cancer. The idea is to develop a cancer prediction system that predict lung and oral cancer depending on the symptoms. The gathered data is pre-processed and the data mining algorithm such as decision tree, logistic regression, Random Forest and Support Vector machines are used to measure the performance. The attribute selection algorithms are used to obtain the mandatory attributes. The main aim of this study is to do a comparative analysis using different algorithms for cancer prediction and suggestion of therapy.

Parametric Identification of Ship Maneuvering Models by Using Support Vector Machines

2009 ◽  
Vol 53 (01) ◽  
pp. 19-30 ◽  
Author(s):  
W. L. Luo ◽  
Z. J. Zou
Keyword(s):  
Support Vector Machines ◽  
Predictive Ability ◽  
Parametric Identification ◽  
Inner Product ◽  
Support Vector ◽  
Identification Algorithm ◽  
Hydrodynamic Coefficients ◽  
Ship Maneuvering ◽  
Vector Machines ◽  
Maneuvering Motion

System identification combined with free-running model tests or full-scale trials is one of the effective methods to determine the hydrodynamic coefficients in the mathematical models of ship maneuvering motion. By analyzing the available data, including rudder angle, surge speed, sway speed, yaw rate, and so forth, a method based on support vector machines (SVM) to estimate the hydrodynamic coefficients is proposed for conventional surface ships. The coefficients are contained in the expansion of the inner product of a linear kernel function. Predictions of maneuvering motion are conducted by using the parameters identified. The results of identification and simulation demonstrate the validity of the identification algorithm proposed. The simultaneous drift and multicollinearity are diminished by introducing an additional ramp signal to the training samples. Comparison between the simulated and predicted motion variables from different maneuvers shows good predictive ability of the trained SVM.

scholarly journals A Novel Homogenous Hybridization Scheme for Performance Improvement of Support Vector Machines Regression in Reservoir Characterization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Kabiru O. Akande ◽  
Taoreed O. Owolabi ◽  
Sunday O. Olatunji ◽  
AbdulAzeez Abdulraheem
Keyword(s):  
Support Vector Machines ◽  
Reservoir Characterization ◽  
Petroleum Industry ◽  
Predictive Ability ◽  
Extraction Process ◽  
Superior Performance ◽  
Support Vector ◽  
Prediction Ability ◽  
Intelligent Algorithms ◽  
Vector Machines

Hybrid computational intelligence is defined as a combination of multiple intelligent algorithms such that the resulting model has superior performance to the individual algorithms. Therefore, the importance of fusing two or more intelligent algorithms to achieve better performance cannot be overemphasized. In this work, a novel homogenous hybridization scheme is proposed for the improvement of the generalization and predictive ability of support vector machines regression (SVR). The proposed and developed hybrid SVR (HSVR) works by considering the initial SVR prediction as a feature extraction process and then employs the SVR output, which is the extracted feature, as its sole descriptor. The developed hybrid model is applied to the prediction of reservoir permeability and the predicted permeability is compared to core permeability which is regarded as standard in petroleum industry. The results show that the proposed hybrid scheme (HSVR) performed better than the existing SVR in both generalization and prediction ability. The outcome of this research will assist petroleum engineers to effectively predict permeability of carbonate reservoirs with higher degree of accuracy and will invariably lead to better reservoir. Furthermore, the encouraging performance of this hybrid will serve as impetus for further exploring homogenous hybrid system.

scholarly journals Semiphysical Modelling of the Nonlinear Dynamics of a Surface Craft with LS-SVM

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
David Moreno-Salinas ◽  
Dictino Chaos ◽  
Eva Besada-Portas ◽  
José Antonio López-Orozco ◽  
Jesús M. de la Cruz ◽  
...  
Keyword(s):  
Predictive Ability ◽  
Experimental Tests ◽  
Support Vector ◽  
Nonlinear Mathematical Model ◽  
Prediction Ability ◽  
Experimental Systems ◽  
Research Fields ◽  
Vector Machines ◽  
Rudder Angle ◽  
Real Experimental Data

One of the most important problems in many research fields is the development of reliable mathematical models with good predictive ability to simulate experimental systems accurately. Moreover, in some of these fields, as marine systems, these models play a key role due to the changing environmental conditions and the complexity and high cost of the infrastructure needed to carry out experimental tests. In this paper, a semiphysical modelling technique based on least-squares support vector machines (LS-SVM) is proposed to determine a nonlinear mathematical model of a surface craft. The speed and steering equations of the nonlinear model of Blanke are determined analysing the rudder angle, surge and sway speeds, and yaw rate from real experimental data measured from a zig-zag manoeuvre made by a scale ship. The predictive ability of the model is tested with different manoeuvring experimental tests to show the good performance and prediction ability of the model computed.

Predicting the quaternary structure attribute of a protein by hybridizing functional domain composition and pseudo amino acid composition

2009 ◽  
Vol 42 (2) ◽  
pp. 169-173 ◽  
Author(s):  
Xuan Xiao ◽  
Pu Wang ◽  
Kuo-Chen Chou
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Quaternary Structure ◽  
Structural Basis ◽  
Sequence Information ◽  
Functional Domain ◽  
Protein Chain ◽  
Pseudo Amino Acid Composition ◽  
Domain Composition

In vivo, some proteins exist as monomers (single polypeptide chains) and others as oligomers. The latter are composed of two or more chains (subunits) that are associated with each other through noncovalent interactions and, occasionally, disulfide bonds. Oligomers can be further classified into homo-oligomers (formed by identical subunits) and hetero-oligomers (formed by different subunits), and they form the structural basis of various biological functions such as cooperative effects, the allosteric mechanism and ion-channel gating. Therefore, it would be of less interest or of low priority for crystallographic scientists to crystallize a single protein chain and determine its three-dimensional structure if it is already known as part of an oligomer. However, it is both time-consuming and laborious to acquire such information on the quaternary structure attribute purely by experiment. In particular, with the avalanche of protein sequences generated in the post-genomic age, it is highly desirable to develop an automated method by which crystallographic scientists can rapidly and effectively identify which quaternary attribute a particular protein chain has according to its sequence information. In view of this, a computational method has been developed by hybridizing the approaches of functional domain composition and pseudo amino acid composition. For the convenience of crystallographic scientists, a user-friendly web server,PQSA-Pred, has been established at http://218.65.61.89:8080/bioinfo/pqsa-pred, by which the desired information can be easily obtained.

MONOTONIC SUPPORT VECTOR MACHINES FOR CREDIT RISK RATING

2009 ◽  
Vol 05 (03) ◽  
pp. 557-570 ◽  
Author(s):  
MICHAEL DOUMPOS ◽  
CONSTANTIN ZOPOUNIDIS
Keyword(s):  
Support Vector Machines ◽  
Pattern Classification ◽  
Credit Rating ◽  
Monotone Function ◽  
Predictive Ability ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Probability Of Default ◽  
Modeling Framework ◽  
Vector Machines

Credit rating models are widely used by banking institutions to assess the creditworthiness of credit applicants and to estimate the probability of default. Several pattern classification algorithms are used for the development of such models. In contrast to other pattern classification tasks, however, credit rating models are not only expected to provide accurate predictions, but also to make clear economic sense. Within this context, the estimated probability of default is often required to be a monotone function of the independent variables. Most machine learning techniques do not take this requirement into account. In this paper, monotonicity hints are used to address this issue within the modeling framework of support vector machines (SVM), which have become increasingly popular in this field. Non-linear SVM credit rating models are developed with linear programming, taking into account the monotonicity requirement. The obtained results indicate that the introduction of monotonicity hints improves the predictive ability of the models.

scholarly journals QSAR Modeling of Acute Neurotoxicity of Some Organic Solvents with Respect to Rodents

2018 ◽  
Vol 1 (3) ◽  
pp. e00019
Author(s):  
V.Yu. Grigorev ◽  
O.E. Raevskaya ◽  
A.V. Yarkov ◽  
O.A. Raevsky
Keyword(s):  
Linear Regression ◽  
Organic Solvents ◽  
Predictive Ability ◽  
Proton Acceptor ◽  
Support Vector ◽  
Regression Equations ◽  
Qsar Modeling ◽  
Vector Machines ◽  
Acute Neurotoxicity ◽  
Rats And Mice

Using literature data analysis, the regression models of acute sublethal neurotoxicity of 47 organic solvents with respect to rats and mice have been developed. To construct the models, we used linear regression, random forest and support vector machines approaches. The linear regression equations were selected as the best models. They are designed on the basis of four molecular descriptors: polarizability, sum of positive atom charges, sum of proton acceptor descriptors and dipole moment. The developed models have good descriptive and predictive ability and clear physicochemical interpretation.

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