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An Automated Combination of Kernels for Predicting Protein Subcellular Localization [chapter]

Cheng Soon Ong, Alexander Zien
Lecture Notes in Computer Science  
Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions.  ...  We utilize an extension of the multiclass support vector machine (SVM) method which directly solves protein subcellular localization without resorting to the common approach of splitting the problem into  ...  Acknowledgement This work was supported in part by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002-506778.  ... 
doi:10.1007/978-3-540-87361-7_16 fatcat:5adlyrqfgzditbox7yr4cpw2iy

Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based onn-peptide compositions

Chin-Sheng Yu, Chih-Jen Lin, Jenn-Kang Hwang
2004 Protein Science  
With the rapid increase of sequenced genomic data, the need for an automated and accurate tool to predict subcellular localization becomes increasingly important.  ...  We present an approach to predict subcellular localization for Gram-negative bacteria.  ...  Acknowledgments This work is supported by grants of National Science Council, Taiwan for J.K.H. and C.J.L. The publication costs of this article were defrayed in part by payment of page charges.  ... 
doi:10.1110/ps.03479604 pmid:15096640 pmcid:PMC2286765 fatcat:6rzazijwrnclxalfosciguyxjm

A New Kernel Based on High-Scored Pairs of Tri-peptides and Its Application in Prediction of Protein Subcellular Localization [chapter]

Zhengdeng Lei, Yang Dai
2005 Lecture Notes in Computer Science  
In conjunction with the use of support vector machines, the effectiveness of the new kernel is evaluated against the conventional coding schemes of k-peptide (k ≤ 3) for the prediction of subcellular localizations  ...  A new kernel has been developed for vectors derived from a coding scheme of the tri-peptide composition for protein sequences.  ...  the representation of protein sequences for the prediction of subcellular localizations [2, 3, 6, 21, 22] .  ... 
doi:10.1007/11428848_115 fatcat:2foucz7j5jf2tmkdgtiz6c4t6a

Automatic Identification of Subcellular Phenotypes on Human Cell Arrays

C. Conrad
2004 Genome Research  
RESULTS Workflow Concept The focus of this study was to set up a framework for highthroughput cell phenotyping.  ...  ACKNOWLEDGMENTS We thank Benedikt Brors and Daniel Gerlich for suggestions on the manuscript. We thank Carl Zeiss Inc. (Göttingen, Germany) for microscope support to the ALMF at EMBL.  ...  The publication costs of this article were defrayed in part by payment of page charges.  ... 
doi:10.1101/gr.2383804 pmid:15173118 pmcid:PMC419791 fatcat:spfleidskzdazgq7gx4jtnxlni

Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair composition

Tanwir Habib, Chaoyang Zhang, Jack Y Yang, Mary Qu Yang, Youping Deng
2008 BMC Genomics  
Most studied methods for prediction of subcellular localization of proteins are signal peptides, the location by sequence homology, and the correlation between the total amino acid compositions of proteins  ...  Occurrence of protein in the cell is an important step in understanding its function. It is highly desirable to predict a protein's subcellular locations automatically from its sequence.  ...  Acknowledgements The authors thank Mississippi Functional Genomics Network (DHHS/NIH/ NCRR grant # 2PORR016476-04) for providing the support.  ... 
doi:10.1186/1471-2164-9-s1-s16 pmid:18366605 pmcid:PMC2386058 fatcat:l2nq7fjkn5gonchidlitk2rpee

Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs

K.-J. Park, M. Kanehisa
2003 Bioinformatics  
Thus, computational prediction of subcellular locations from the amino acid sequence information would help annotation and functional prediction of protein coding genes in complete genomes.  ...  A set of SVMs was trained to predict the subcellular location of a given protein based on its amino acid, amino acid pair, and gapped amino acid pair compositions.  ...  This work was supported by grants from the Ministry of Education, Culture, Sports, Science, and Technology of Japan, the Japan Society for the Promotion of Science, and the Japan Science and Technology  ... 
doi:10.1093/bioinformatics/btg222 pmid:12967962 fatcat:e4xg6ydppjgd7ffo5zspmehfsu

Efficient framework for automated classification of subcellular patterns in budding yeast

Seungil Huh, Donghun Lee, Robert F. Murphy
2009 Cytometry Part A  
The first system for automated classification of subcellular patterns in these yeast images utilized a computationally expensive method for segmentation of images into individual cells and achieved an  ...  An extensive publicly available collection of images for most proteins expressed in the yeast S. cerevisae has provided both an important source of information on protein location but also a testbed for  ...  Automated classification of subcellular patterns in such images is a viable alternative, and a number of systems for this task have been described (1) (2) (3) .  ... 
doi:10.1002/cyto.a.20793 pmid:19753630 pmcid:PMC2847491 fatcat:dnq5vo3feva4dmka5y22oxtpt4

Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network

Rakesh David, Rhys-Joshua D. Menezes, Jan De Klerk, Ian R. Castleden, Cornelia M. Hooper, Gustavo Carneiro, Matthew Gilliham
2021 Scientific Reports  
Comparable scoring metrics were obtained using the CropPAL database as an independent testing dataset that stores protein subcellular localisation in crop species, demonstrating wide applicability of prediction  ...  Using the SUBA corpus, the neural network classifier predicted interactions between protein name, subcellular localisation and experimental methodology with an average precision, recall rate, accuracy  ...  Acknowledgements This research was supported by University of Adelaide Interdisciplinary Research Funding Scheme awarded to M.G. and Australian Research Council through CE140100008 to M.G.  ... 
doi:10.1038/s41598-020-80441-8 pmid:33462256 fatcat:debgp7hmq5al5pk46mjgynhfsa

Boosting accuracy of automated classification of fluorescence microscope images for location proteomics

Kai Huang, Robert F Murphy
2004 BMC Bioinformatics  
Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location.  ...  The accuracy and sensitivity of this approach represents an important alternative to low-resolution assignments by curation or sequence-based prediction.  ...  Acknowledgments We thank William Dirks and Adrienne Wells for programming and preliminary work on applying wavelet features to protein location patterns, and Dr.  ... 
doi:10.1186/1471-2105-5-78 pmid:15207009 pmcid:PMC449699 fatcat:jcedg5tzcfbudojmzwcxvozmru

Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network [article]

Rakesh David, Rhys-Joshua D Menezes, Jan De Klerk, Ian R Castleden, Cornelia M Hooper, Gustavo Carneiro, Matthew Gilliham
2020 biorxiv/medrxiv   pre-print
The system was able to extract relevant text and the classifier predicted interactions between protein name, subcellular localisation and experimental methodology.  ...  Here, we employed natural-language processing (CBOW) and deep Recurrent Neural Network (bi-directional LSTM) to predict relations between biological entities that describe protein subcellular localisation  ...  Acknowledgements This research was supported by University of Adelaide Interdisciplinary Research Funding Scheme awarded to M.G. and Australian Research Council through CE140100008 to M.G.  ... 
doi:10.1101/2020.09.09.290577 fatcat:nosfzvxmzvc5vhnfwjj5fpvfbi

Protein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multiple support vector machines

Jiren Wang, Wing-Kin Sung, Arun Krishnan, Kuo-Bin Li
2005 BMC Bioinformatics  
Predicting the subcellular localization of proteins is important for determining the function of proteins.  ...  We have developed a system for predicting the subcellular localization of proteins for Gram-negative bacteria based on amino acid subalphabets and a combination of multiple support vector machines.  ...  It is desirable to have an automated and reliable system for predicting subcellular localization of proteins from amino acid sequences.  ... 
doi:10.1186/1471-2105-6-174 pmid:16011808 pmcid:PMC1190155 fatcat:sfedpxz47fgndha2hhh3ldfqgy

Application of deep convolutional neural networks in classification of protein subcellular localization with microscopy images

Mengli Xiao, Xiaotong Shen, Wei Pan
2019 Genetic Epidemiology  
Single-cell microscopy image analysis has proved invaluable in protein subcellular localization for inferring gene/protein function.  ...  For such purposes, we applied several representative types of deep convolutional neural networks (CNNs) and two popular ensemble methods, random forests and gradient boosting, to predict protein subcellular  ...  Acknowledgement We thank the reviewers for their helpful comments and suggestions.  ... 
doi:10.1002/gepi.22182 pmid:30614068 pmcid:PMC6416075 fatcat:wiumzjxxqrdqtpfuktdjrtkmge

A Comparative Study on Feature Extraction from Protein Sequences for Subcellular Localization Prediction

Wen-Yun Yang, Bao-Liang Lu, Yang Yang
2006 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology  
One of the central problems in computational biology is to identify the protein function in an automated and high-throughput fashion.  ...  A wide variety of methods for protein subcellular localization has been proposed over recent years. They fall into two categories, sequence-based and database-based.  ...  ACKNOWLEDGEMENTS The authors thank Ke Wu for his valuable advices.  ... 
doi:10.1109/cibcb.2006.330991 dblp:conf/cibcb/YangLY06 fatcat:gauuyuwunzfcroaoll5ld56mry

Bird Eye View of Protein Subcellular Localization Prediction

Ravindra Kumar, Sandeep Kumar Dhanda
2020 Life  
The challenge of computational prediction of subcellular localization of proteins is addressed in various in silico methods.  ...  We hope the review will be useful for the researchers working in the field of protein localization predictions.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/life10120347 pmid:33327400 pmcid:PMC7764902 fatcat:4kfiezcrlfh4vmjh6bh5ysgwty

Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer

Zhen-Zhen Xue, Yanxia Wu, Qing-Zu Gao, Liang Zhao, Ying-Ying Xu
2020 BMC Bioinformatics  
Conclusions Our results demonstrate that using image patches and combining predefined and deep features can improve the performance of protein subcellular localization, and our model can effectively detect  ...  This study is anticipated to be useful in annotating unknown subcellular localization for proteins and discovering new potential location biomarkers.  ...  Availability of data and materials The datasets and code used in this study are available at https://github.com/Xue-zhen-zhen/Protein-subcellular-location Ethics approval and consent to participate Not  ... 
doi:10.1186/s12859-020-03731-y pmid:32907537 fatcat:3jmthokgavhgnk5ke5pvzwnzse
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