scholarly journals Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yu Wan ◽  
Zhuo Wang ◽  
Tzong-Yi Lee
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Sequential Minimal Optimization ◽  
Anticancer Peptides ◽  
Performance Accuracy ◽  
Wet Lab ◽  
Optimized Model ◽  
Scoring Matrix ◽  
New Feature ◽  
Research Show

Abstract Background Cancer is one of the major causes of death worldwide. To treat cancer, the use of anticancer peptides (ACPs) has attracted increased attention in recent years. ACPs are a unique group of small molecules that can target and kill cancer cells fast and directly. However, identifying ACPs by wet-lab experiments is time-consuming and labor-intensive. Therefore, it is significant to develop computational tools for ACPs prediction. Though some ACP prediction tools have been developed recently, their performances are not well enough and most of them do not offer a function to distinguish ACPs from antimicrobial peptides (AMPs). Considering the fact that a growing number of studies have shown that some AMPs exhibit anticancer function, this work tries to build a model for distinguishing AMPs from ACPs in addition to a model that predicts ACPs from whole peptides. Results This study chooses amino acid composition, N5C5, k-space, position-specific scoring matrix (PSSM) as features, and analyzes them by machine learning methods, including support vector machine (SVM) and sequential minimal optimization (SMO) to build a model (model 2) for distinguishing ACPs from whole peptides. Another model (model 1) that distinguishes ACPs from AMPs is also developed. Comparing to previous models, models developed in this research show better performance (accuracy: 85.5% for model 1 and 95.2% for model 2). Conclusions This work utilizes a new feature, PSSM, which contributes to better performance than other features. In addition to SVM, SMO is used in this research for optimizing SVM and the SMO-optimized models show better performance than non-optimized models. Last but not least, this work provides two different functions, including distinguishing ACPs from AMPs and distinguishing ACPs from all peptides. The second SMO-optimized model, which utilizes PSSM as a feature, performs better than all other existing tools.

scholarly journals Incorporating Support Vector Machine With Sequential Minimal Optimization to Identify Anticancer Peptides

2020 ◽  
Author(s):  
Yu Wan ◽  
Zhuo Wang ◽  
Tzong-Yi Lee
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Sequential Minimal Optimization ◽  
Anticancer Peptides ◽  
Performance Accuracy ◽  
Wet Lab ◽  
Optimized Model ◽  
Scoring Matrix ◽  
New Feature ◽  
Research Show

Abstract BackgroundCancer is a major cause of death worldwide. To treat cancer, the use of anticancer peptides (ACPs) has received increasing attention in recent years. ACPs are a unique group of small molecules that can target and kill cancer cells fast and directly. However, identifying ACPs by wet-lab experiments is time-consuming and labor-intensive. Therefore, it is significant to develop computational tools for ACPs prediction.ResultsThis study chose amino acid composition (AAC), N5C5, k-space, position-specific scoring matrix (PSSM) as features, and analyzed them by machine learning methods, including support vector machine (SVM) and sequential minimal optimization (SMO) to build a model (model 2) distinguishing ACPs from non-ACPs. Since a growing number of studies have shown that some antimicrobial peptides (AMPs) exhibit anticancer function, a model (model 1) to distinguish ACPs from AMPs is also been developed. Comparing to previous models, models developed in this research show better performance (accuracy: 82.5% for model 1 and 93.5% for model 2).ConclusionsThis work utilizes a new feature, PSSM, which contributes to better performance than other features. In addition to SVM, SMO is used in this research for optimizing SVM and the SMO-models show better performance than unoptimized models. Last but not least, this work provides two different functions, including distinguishing ACPs from AMPs and distinguishing ACPs from all peptides. The second SMO-optimized model, which utilizes PSSM as feature, performs better than all other existing tools.

Blade Damage Forecast Research Based on Support Vector Machine

2012 ◽  
Vol 588-589 ◽  
pp. 278-282
Author(s):  
Zhi Wei Ma ◽  
Yu Xiu Xu ◽  
Shi Rong Xing
Keyword(s):  
Support Vector Machine ◽  
Strain Energy ◽  
Energy Change ◽  
Mode Shapes ◽  
Support Vector ◽  
Change Rate ◽  
Wind Turbine System ◽  
Mass Eccentricity ◽  
Damage States ◽  
Research Show

By doing modal analysis of the whole wind turbine system, we can get the first twenty orders natural frequencies and corresponding mode shapes, By analyzing the dynamic characteristics of the blade, the weakness points of blade were fund. Under rotor rotating excitation in the normal state, mass eccentricity states and stiffness damage states of blade, the strain energy change rates (SECR) of nacelle are obtained. While based on the SECR of nacelle, the methods of strain energy change rate and support vector machine are introduced to indentify locate the mass eccentricity and stiffness damages of blade. The research show that mass eccentricity and stiffness damages at different location and in different degree can be efficiency identified and forecasted by means of support vector machine classification method.

scholarly journals Journal Classification Based on Abstract Using Cosine Similarity and Support Vector Machine

2020 ◽  
Vol 4 (3) ◽  
pp. 48
Author(s):  
Muhammad Habibi ◽  
Puji Winar Cahyo
Keyword(s):  
Support Vector Machine ◽  
Cosine Similarity ◽  
Classification Model ◽  
Support Vector ◽  
Weighting Method ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Performance Accuracy ◽  
Similarity Algorithm ◽  
Journal Editorial

One of the problems related to journal publishing is the process of categorizing entry into journals according to the field of science. A large number of journal documents included in a journal editorial makes it difficult to categorize so that the process of plotting to reviewers requires a long process. The review process in a journal must be done planning according to the expertise of the reviewer, to produce a quality journal. This study aims to create a classification model that can classify journals automatically using the Cosine Similarity algorithm and Support Vector Machine in the classification process and using the TF-IDF weighting method. The object of this research is abstract in scientific journals. The journals will be classified according to the reviewer's field of expertise. Based on the experimental results, the Support Vector Machine method produces better performance accuracy than the Cosine Similarity method. The results of the calculation of the value of precision, recall, and f-score are known that the Support Vector Machine method produces better amounts, in line with the accuracy value.

scholarly journals A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping

2019 ◽  
Vol 11 (22) ◽  
pp. 6323 ◽  
Author(s):  
Pham ◽  
Prakash ◽  
Chen ◽  
Ly ◽  
Ho ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hybrid Model ◽  
Landslide Susceptibility ◽  
Characteristic Curve ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Sequential Minimal Optimization ◽  
Affecting Factors ◽  
Landslide Modeling

The main objective of this study is to propose a novel hybrid model of a sequential minimal optimization and support vector machine (SMOSVM) for accurate landslide susceptibility mapping. For this task, one of the landslide prone areas of Vietnam, the Mu Cang Chai District located in Yen Bai Province was selected. In total, 248 landslide locations and 15 landslide-affecting factors were selected for landslide modeling and analysis. Predictive capability of SMOSVM was evaluated and compared with other landslide models, namely a hybrid model of the cascade generalization optimization-based support vector machine (CGSVM), individual models, such as support vector machines (SVM) and naïve Bayes trees (NBT). For validation, different quantitative criteria such as statistical based methods and area under the receiver operating characteristic curve (AUC) technique were used. Results of the study show that the SMOSVM model (AUC = 0.824) has the highest performance for landslide susceptibility mapping, followed by CGSVM (AUC = 0.815), SVM (AUC = 0.804), and NBT (AUC = 0.800) models, respectively. Thus, the proposed novel SMOSVM model is a promising method for better landslide susceptibility mapping and prediction, which can be applied also in other landslide prone areas.

An Experimental Study for the Effect of Stop Words Elimination for Arabic Text Classification Algorithms

2014 ◽  
pp. 184-190
Author(s):  
Bassam Al-Shargabi ◽  
Fekry Olayah ◽  
Waseem AL Romimah
Keyword(s):  
Experimental Study ◽  
Support Vector Machine ◽  
Text Classification ◽  
Error Rate ◽  
Support Vector ◽  
Arabic Text ◽  
Sequential Minimal Optimization ◽  
Split Method ◽  
Arabic Text Classification ◽  
Fold Cross Validation

In this paper, an experimental study was conducted on three techniques for Arabic text classification. These techniques are Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO), Naïve Bayesian (NB), and J48. The paper assesses the accuracy for each classifier and determines which classifier is more accurate for Arabic text classification based on stop words elimination. The accuracy for each classifier is measured by Percentage split method (holdout), and K-fold cross validation methods, along with the time needed to classify Arabic text. The results show that the SMO classifier achieves the highest accuracy and the lowest error rate, and shows that the time needed to build the SMO model is much lower compared to other classification techniques.

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