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Protein Cellular Localization with Multiclass Support Vector Machines and Decision Trees [chapter]

Ana Carolina Lorena, André C. P. L. F. de Carvalho
2005 Lecture Notes in Computer Science  
This paper investigates the use of two Machine Learning techniques, Support Vector Machines (SVMs) and Decision Trees (DTs), in the protein cellular localization prediction problem.  ...  Many cellular functions are carried out in compartments of the cell. The cellular localization of a protein is thus related to its function identification.  ...  Keywords. protein cellular localization, Machine Learning, multiclass Support Vector Machines, Decision Trees.  ... 
doi:10.1007/11532323_6 fatcat:tfcqmd4hj5gvbe2owbb4scoisi

Predicting Protein-Protein Interactions From Protein Sequences Using Phylogenetic Profiles

Omer Nebil Yaveroglu, Tolga Can
2009 Zenodo  
Support Vector Machines, Feature Extraction using ReliefF, Naive Bayes Classification, K-Nearest Neighborhood Classification, Decision Trees, and Random Forest Classification are the methods we applied  ...  Random Forest Classification outperformed all other methods with a prediction accuracy of 76.93%  ...  For this purpose, we have separately applied Support Vector Machines, Feature Extraction using ReliefF, Naive Bayes Classification, K-Nearest Neighborhood Classification, Decision Trees, and Random Forest  ... 
doi:10.5281/zenodo.1076740 fatcat:gptavl52bfhvbbrdjkwmqkvvzy

Prediction of G-protein-coupled receptor classes based on the concept of Chou's pseudo amino acid composition: An approach from discrete wavelet transform

Jian-Ding Qiu, Jian-Hua Huang, Ru-Ping Liang, Xiao-Quan Lu
2009 Analytical Biochemistry  
In this study, the decomposition level 3 was 186 chosen to classify the GPCRs, and the obtained 16 dimension fea-187 ture vectors were then inputted to SVM for classification. 188 Support vector machine  ...  algorithm (CDA) [12,13], support vector 65 machine (69 due to its attractive fea-71 tures, including effective avoidance of overfitting, ability to handle 72 large feature space, and absence  ... 
doi:10.1016/j.ab.2009.04.009 pmid:19364489 fatcat:drjrnic52baz7o5d63gtfdmg4q

Prediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks

Adele Sadat Haghighat Hoseini, Mitra Mirzarezaee
2018 Iranian Journal of Biotechnology  
The accuracy of combining all features with PPI data, using the Decision Tree and Random Forest classifiers, was 82.49% and 83.35%, respectively.  ...  Materials and Methods: In this study, we have examined the protein interaction network as one of the features for prediction of the protein localization and its effects on the prediction results.  ...  The first supplementary file was attached to mention a list of all of the proteins involved in the present study.  ... 
doi:10.21859/ijb.1933 fatcat:senetise2zfv5mtxkpfe6rdcrm

Prediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks

Adele Sadat Haghighat Hoseini, Mitra Mirzarezaee
2018 Iranian Journal of Biotechnology  
The accuracy of combining all features with PPI data, using the Decision Tree and Random Forest classifiers, was 82.49% and 83.35%, respectively.  ...  Prediction of the protein localization is among the most important issues in the bioinformatics that is used for the prediction of the proteins in the cells and organelles such as mitochondria.  ...  The first supplementary file was attached to mention a list of all of the proteins involved in the present study.  ... 
doi:10.15171/ijb.1933 pmid:31457027 pmcid:PMC6697825 fatcat:t6qos6nngfbd7ffhxwqk7apar4

Classification of Enzymes Using Machine Learning Based Approaches: A Review

Sanjeev Kumar Yadav, Arvind Kumar Tiwari
2015 Machine Learning and Applications An International Journal  
vector machine recursive Feature elimination (SVRRFE) is used to select the optimal number of features.  ...  The Random Forest has been used to construct a three level model with optimal number of features selected by SVMRFE, where top level distinguish a query protein as an enzyme or nonenzyme, second level  ...  wrapper method, classification techniques such as artificial neural network, K-nearest-neighbor, Decision Tree, Random Forests, Naive Bayes, support vector machine.  ... 
doi:10.5121/mlaij.2015.2404 fatcat:hgw4bponzffxthn2tqptz4nufq

Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach

Josh L. Espinoza, Chris L. Dupont, Aubrie O'Rourke, Sinem Beyhan, Pavel Morales, Amy Spoering, Kirsten J. Meyer, Agnes P. Chan, Yongwook Choi, William C. Nierman, Kim Lewis, Karen E. Nelson (+1 others)
2021 PLoS Computational Biology  
However, rediscovery of known antibiotics demonstrates a need for approaches that accurately identify potential novelty with higher throughput and reduced labor.  ...  We evaluated our methods using whole transcriptome responses from Escherichia coli challenged with 41 FDA-approved antibiotics and 9 crude extracts while depositing 306 transcriptomes.  ...  Acknowledgments We would like to acknowledge Suren Singh of Durban University of Technology, South Africa for his mentorship and support during this work.  ... 
doi:10.1371/journal.pcbi.1008857 pmid:33780444 pmcid:PMC8031737 fatcat:747cakuwj5hbtbyyuasecm6vnq

A Novel Protein Subcellular Localization Method With CNN-XGBoost Model for Alzheimer's Disease

Long Pang, Junjie Wang, Lingling Zhao, Chunyu Wang, Hui Zhan
2019 Frontiers in Genetics  
The prediction of protein subcellular localization play important roles in the understanding of the mechanism of protein function, pathogenes and disease therapy.  ...  obtain features from the original sequence information and a XGBoost classifier as a recognizer to identify the protein subcellular localization based on the output of the CNN.  ...  ACKNOWLEDGMENTS This work is supported by the National Natural Science Foundation of China (NSFC, Grant no. 61305013 and 61872114).  ... 
doi:10.3389/fgene.2018.00751 pmid:30713552 pmcid:PMC6345701 fatcat:5mejeof2cbgfhpfppuiftqp5su

Machine learning classification of trajectories from molecular dynamics simulations of chromosome segregation

David Geisel, Peter Lenz, Hans A. Kestler
2022 PLoS ONE  
We used these trajectories to train four different machine learning algorithms, two linear models and two tree-based classifiers, to discriminate segregation mechanisms and possible combinations of them  ...  Apparently, some bacteria even use combinations of different segregation mechanisms such as protein machines or rely on physical forces.  ...  P.L and D.G. wrote the paper.  ... 
doi:10.1371/journal.pone.0262177 pmid:35061790 pmcid:PMC8782305 fatcat:oauftpqfl5ekbpwcc3d5rr72o4

In-Pero: Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins

Marco Anteghini, Vitor Martins dos Santos, Edoardo Saccenti
2021 International Journal of Molecular Sciences  
In-Pero combines standard machine learning approaches with recently proposed multi-dimensional deep-learning representations of the protein amino-acid sequence.  ...  We present here In-Pero, a new method for predicting protein sub-peroxisomal cellular localisation.  ...  A list of the most common tools for subcellular localisation includes BaCello [7] a predictor based on different Support Vector Machines (SVM) organised in a decision tree; Phobius [8] , a combined  ... 
doi:10.3390/ijms22126409 fatcat:mlbirnvpwzczff2p5zsl25cxka

Hierarchical Ensemble Methods for Protein Function Prediction

Giorgio Valentini
2014 ISRN Bioinformatics  
According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a "consensus" ensemble decision, taking  ...  In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines.  ...  Acknowledgements The author thanks the reviewers for their comments and suggestions and acknowledges partial support from the PRIN project "Automi e linguaggi formali: aspetti matematici e applicativi"  ... 
doi:10.1155/2014/901419 pmid:25937954 pmcid:PMC4393075 fatcat:i6w56fpbqnekjozm2kdpik635e

A Survey of Computational Intelligence Techniques in Protein Function Prediction

Arvind Kumar Tiwari, Rajeev Srivastava
2014 International Journal of Proteomics  
, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein  ...  coupled receptors, membrane proteins, and pathway analysis from gene expression datasets.  ...  The authors compared their result with decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function.  ... 
doi:10.1155/2014/845479 pmid:25574395 pmcid:PMC4276698 fatcat:p3vwanr2nran7arwzotirhgyte

Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis

Yunyi Wu, Guanyu Wang
2018 International Journal of Molecular Sciences  
In this article, we review machine learning methods that have been applied to toxicity prediction, including deep learning, random forests, k-nearest neighbors, and support vector machines.  ...  Among its many applications, toxicity prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical  ...  Main algorithms of machine learning, evolved from the study of cluster analysis and pattern recognition, include artificial neural networks (ANN), decision trees, support vector machines (SVM), and Bayesian  ... 
doi:10.3390/ijms19082358 pmid:30103448 fatcat:mjgeejthrzex7kbyxgncnncgla

In-Pero: Exploiting deep learning embeddings of protein sequences to predict the localisation of peroxisomal proteins [article]

Marco Anteghini, Vitor A.P. Martins dos Santos, Edoardo Saccenti
2021 bioRxiv   pre-print
In-Pero combines standard machine learning approaches with recently proposed multi-dimensional deep-learning representations of the protein amino-acid sequence.  ...  We present here In-Pero, a new method for predicting protein sub-peroxisomal cellular localisation.  ...  A list of the most common tools for subcellular localisation includes BaCello [7] a predictor based on different Support Vector Machines (SVM) organised in a decision tree; Phobius [8] , a combined  ... 
doi:10.1101/2021.01.18.427146 fatcat:bzsz3e3edja65llvbr6x55htzm

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

2018 Cancer Genomics & Proteomics  
Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification  ...  KNN), decision trees (DT) and Naive Bayes.  ...  Acknowledgements This study has been supported by University of Manitoba Faculty of Science Interdisciplinary/New Directions Research Collaboration Initiation Grant, and partially by the Canadian Breast  ... 
doi:10.21873/cgp.20063 pmid:29275361 pmcid:PMC5822181 fatcat:o6l764hssnh2nbu4fm6b52f3mi
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