Machine Learning for Protein Subcellular Localization Prediction / / Shibiao Wan, Man-Wai Mak.
Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species. Three machine learning tools are introduced to improve classification refinement, feature extraction, and dimens...
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Place / Publishing House: | Berlin ;, Boston : : De Gruyter, , [2015] ©2015 |
Year of Publication: | 2015 |
Language: | English |
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Physical Description: | 1 online resource (192 p.) |
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Wan, Shibiao, author. aut http://id.loc.gov/vocabulary/relators/aut Machine Learning for Protein Subcellular Localization Prediction / Shibiao Wan, Man-Wai Mak. Berlin ; Boston : De Gruyter, [2015] ©2015 1 online resource (192 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Frontmatter -- Preface -- Contents -- List of Abbreviations -- 1. Introduction -- 2. Overview of subcellular localization prediction -- 3. Legitimacy of using gene ontology information -- 4. Single-location protein subcellular localization -- 5. From single- to multi-location -- 6. Mining deeper on GO for protein subcellular localization -- 7. Ensemble random projection for large-scale predictions -- 8. Experimental setup -- 9. Results and analysis -- 10. Properties of the proposed predictors -- 11. Conclusions and future directions -- A. Webservers for protein subcellular localization -- B. Support vector machines -- C. Proof of no bias in LOOCV -- D. Derivatives for penalized logistic regression -- Bibliography -- Index restricted access http://purl.org/coar/access_right/c_16ec online access with authorization star Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species. Three machine learning tools are introduced to improve classification refinement, feature extraction, and dimensionality reduction. Mode of access: Internet via World Wide Web. In English. Description based on online resource; title from PDF title page (publisher's Web site, viewed 30. Aug 2021) Artificial intelligence Congresses. Computer vision Congresses. Pattern Recognition, Automated Congresses. Pattern perception Congresses. Bioinformatik. Informatik. Proteomik. Technology & Engineering / Signals & Signal Processing. bisacsh Bioinformatics. Computer Science. Proteomics. Mak, Man-Wai, author. aut http://id.loc.gov/vocabulary/relators/aut Title is part of eBook package: De Gruyter DG Plus eBook-Package 2015 9783110700985 Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2015 9783110439687 ZDB-23-DGG Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering, Computer Sciences 2015 9783110438772 ZDB-23-DEI EPUB 9781501501524 print 9781501510489 https://doi.org/10.1515/9781501501500 https://www.degruyter.com/isbn/9781501501500 Cover https://www.degruyter.com/cover/covers/9781501501500.jpg |
language |
English |
format |
eBook |
author |
Wan, Shibiao, Wan, Shibiao, Mak, Man-Wai, |
spellingShingle |
Wan, Shibiao, Wan, Shibiao, Mak, Man-Wai, Machine Learning for Protein Subcellular Localization Prediction / Frontmatter -- Preface -- Contents -- List of Abbreviations -- 1. Introduction -- 2. Overview of subcellular localization prediction -- 3. Legitimacy of using gene ontology information -- 4. Single-location protein subcellular localization -- 5. From single- to multi-location -- 6. Mining deeper on GO for protein subcellular localization -- 7. Ensemble random projection for large-scale predictions -- 8. Experimental setup -- 9. Results and analysis -- 10. Properties of the proposed predictors -- 11. Conclusions and future directions -- A. Webservers for protein subcellular localization -- B. Support vector machines -- C. Proof of no bias in LOOCV -- D. Derivatives for penalized logistic regression -- Bibliography -- Index |
author_facet |
Wan, Shibiao, Wan, Shibiao, Mak, Man-Wai, Mak, Man-Wai, Mak, Man-Wai, |
author_variant |
s w sw s w sw m w m mwm |
author_role |
VerfasserIn VerfasserIn VerfasserIn |
author2 |
Mak, Man-Wai, Mak, Man-Wai, |
author2_variant |
m w m mwm |
author2_role |
VerfasserIn VerfasserIn |
author_sort |
Wan, Shibiao, |
title |
Machine Learning for Protein Subcellular Localization Prediction / |
title_full |
Machine Learning for Protein Subcellular Localization Prediction / Shibiao Wan, Man-Wai Mak. |
title_fullStr |
Machine Learning for Protein Subcellular Localization Prediction / Shibiao Wan, Man-Wai Mak. |
title_full_unstemmed |
Machine Learning for Protein Subcellular Localization Prediction / Shibiao Wan, Man-Wai Mak. |
title_auth |
Machine Learning for Protein Subcellular Localization Prediction / |
title_alt |
Frontmatter -- Preface -- Contents -- List of Abbreviations -- 1. Introduction -- 2. Overview of subcellular localization prediction -- 3. Legitimacy of using gene ontology information -- 4. Single-location protein subcellular localization -- 5. From single- to multi-location -- 6. Mining deeper on GO for protein subcellular localization -- 7. Ensemble random projection for large-scale predictions -- 8. Experimental setup -- 9. Results and analysis -- 10. Properties of the proposed predictors -- 11. Conclusions and future directions -- A. Webservers for protein subcellular localization -- B. Support vector machines -- C. Proof of no bias in LOOCV -- D. Derivatives for penalized logistic regression -- Bibliography -- Index |
title_new |
Machine Learning for Protein Subcellular Localization Prediction / |
title_sort |
machine learning for protein subcellular localization prediction / |
publisher |
De Gruyter, |
publishDate |
2015 |
physical |
1 online resource (192 p.) |
contents |
Frontmatter -- Preface -- Contents -- List of Abbreviations -- 1. Introduction -- 2. Overview of subcellular localization prediction -- 3. Legitimacy of using gene ontology information -- 4. Single-location protein subcellular localization -- 5. From single- to multi-location -- 6. Mining deeper on GO for protein subcellular localization -- 7. Ensemble random projection for large-scale predictions -- 8. Experimental setup -- 9. Results and analysis -- 10. Properties of the proposed predictors -- 11. Conclusions and future directions -- A. Webservers for protein subcellular localization -- B. Support vector machines -- C. Proof of no bias in LOOCV -- D. Derivatives for penalized logistic regression -- Bibliography -- Index |
isbn |
9781501501500 9783110700985 9783110439687 9783110438772 9781501501524 9781501510489 |
callnumber-first |
Q - Science |
callnumber-subject |
QP - Physiology |
callnumber-label |
QP552 |
callnumber-sort |
QP 3552 M44 W36 42015 |
genre_facet |
Congresses. |
url |
https://doi.org/10.1515/9781501501500 https://www.degruyter.com/isbn/9781501501500 https://www.degruyter.com/cover/covers/9781501501500.jpg |
illustrated |
Not Illustrated |
dewey-hundreds |
500 - Science |
dewey-tens |
570 - Life sciences; biology |
dewey-ones |
570 - Life sciences; biology |
dewey-full |
570 |
dewey-sort |
3570 |
dewey-raw |
570 |
dewey-search |
570 |
doi_str_mv |
10.1515/9781501501500 |
oclc_num |
912323205 |
work_keys_str_mv |
AT wanshibiao machinelearningforproteinsubcellularlocalizationprediction AT makmanwai machinelearningforproteinsubcellularlocalizationprediction |
status_str |
n |
ids_txt_mv |
(DE-B1597)444958 (OCoLC)912323205 |
carrierType_str_mv |
cr |
hierarchy_parent_title |
Title is part of eBook package: De Gruyter DG Plus eBook-Package 2015 Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2015 Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering, Computer Sciences 2015 |
is_hierarchy_title |
Machine Learning for Protein Subcellular Localization Prediction / |
container_title |
Title is part of eBook package: De Gruyter DG Plus eBook-Package 2015 |
author2_original_writing_str_mv |
noLinkedField noLinkedField |
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1770177060255301632 |
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