Prediction of Subcellular Location for Apoptosis Proteins by Dual-layer Support Vector Machine Based on Multiple Compositions

2007 ◽  
Author(s):  
Xi-Bin Zhou ◽  
Chao Chen ◽  
Zhan-Chao Li ◽  
Xiao-Yong Zou
Keyword(s):  
Support Vector Machine ◽  
Subcellular Location ◽  
Support Vector ◽  
Apoptosis Proteins

scholarly journals Predicting Apoptosis Protein Subcellular Locations based on the Protein Overlapping Property Matrix and Tri-Gram Encoding

2019 ◽  
Vol 20 (9) ◽  
pp. 2344
Author(s):  
Yang Yang ◽  
Huiwen Zheng ◽  
Chunhua Wang ◽  
Wanyue Xiao ◽  
Taigang Liu
Keyword(s):  
Support Vector Machine ◽  
Subcellular Location ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Svm Classifier ◽  
Protein Subcellular Location ◽  
Promising Tool ◽  
Apoptosis Protein ◽  
Benchmark Datasets ◽  
Apoptosis Proteins

To reveal the working pattern of programmed cell death, knowledge of the subcellular location of apoptosis proteins is essential. Besides the costly and time-consuming method of experimental determination, research into computational locating schemes, focusing mainly on the innovation of representation techniques on protein sequences and the selection of classification algorithms, has become popular in recent decades. In this study, a novel tri-gram encoding model is proposed, which is based on using the protein overlapping property matrix (POPM) for predicting apoptosis protein subcellular location. Next, a 1000-dimensional feature vector is built to represent a protein. Finally, with the help of support vector machine-recursive feature elimination (SVM-RFE), we select the optimal features and put them into a support vector machine (SVM) classifier for predictions. The results of jackknife tests on two benchmark datasets demonstrate that our proposed method can achieve satisfactory prediction performance level with less computing capacity required and could work as a promising tool to predict the subcellular locations of apoptosis proteins.

Predicting subcellular location of apoptosis proteins based on wavelet transform and support vector machine

Amino Acids ◽  
2009 ◽  
Vol 38 (4) ◽  
pp. 1201-1208 ◽  
Author(s):  
Jian-Ding Qiu ◽  
San-Hua Luo ◽  
Jian-Hua Huang ◽  
Xing-Yu Sun ◽  
Ru-Ping Liang
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Subcellular Location ◽  
Support Vector ◽  
Apoptosis Proteins

Prediction of Apoptosis Proteins Subcellular Location Using Evolutionary Profiles and Motifs Information

2013 ◽  
Vol 647 ◽  
pp. 600-606 ◽  
Author(s):  
Tao Li ◽  
Qian Zhong Li
Keyword(s):  
Cell Proliferation ◽  
Support Vector Machine ◽  
Protein Sequences ◽  
Subcellular Location ◽  
Support Vector ◽  
Data Sets ◽  
Success Rates ◽  
Jackknife Test ◽  
Apoptosis Protein ◽  
Apoptosis Proteins

Apoptosis proteins are very important for regulating the balance between cell proliferation and death. Because the function of apoptosis protein is closely related to its subcellular location, it is desirable to explore their function by predicting the subcellular location of apoptosis protein. In this paper, based on evolutionary profiles and motifs information of protein sequences, an approach for predicting apoptosis proteins subcellular location is presented by using support vector machine (SVM). When the method is applied to three data sets (98 apoptosis proteins dataset, 225 apoptosis proteins dataset and 317 apoptosis proteins dataset), the overall accuracies of our method on the three data sets reach 95.9%, 86.7% and 91.8% in the jackknife test, respectively. The higher predictive success rates indicate that the proposed method is very useful for apoptosis proteins subcellular localization.

MSLVP: prediction of multiple subcellular localization of viral proteins using a support vector machine

2016 ◽  
Vol 12 (8) ◽  
pp. 2572-2586 ◽  
Author(s):  
Anamika Thakur ◽  
Akanksha Rajput ◽  
Manoj Kumar
Keyword(s):  
Support Vector Machine ◽  
Subcellular Localization ◽  
Host Cell ◽  
Subcellular Location ◽  
Viral Proteins ◽  
Support Vector

Knowledge of the subcellular location (SCL) of viral proteins in the host cell is important for understanding their function in depth.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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