The Resource Machine Learning for Protein Subcellular Localization Prediction, (electronic resource)

Machine Learning for Protein Subcellular Localization Prediction, (electronic resource)

Label
Machine Learning for Protein Subcellular Localization Prediction
Title
Machine Learning for Protein Subcellular Localization Prediction
Creator
Contributor
Subject
Genre
Language
  • eng
  • eng
Summary
<!doctype html public ""-//w3c//dtd html 4.0 transitional//en""> <html><head> <meta http-equiv=content-type content=""text/html; charset=iso-8859-1""> <meta content=""mshtml 6.00.6000.21371"" name=generator></head> <body> <P>For bioinformaticians, computational biologists, and wet-lab biologists, the authors provide the latest machine learning approaches for protein subcellular localization prediction with a systemic scheme for improving predictors performance.</P></body></html>
Cataloging source
AU-PeEL
http://library.link/vocab/creatorName
Wan, Shibiao
Dewey number
572.696
Language note
English
LC call number
QP552 .M44
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
Mak, Man-Wai
http://library.link/vocab/subjectName
  • Artificial intelligence
  • Probability
  • Protein transport
  • Statistics as Topic
  • Computing Methodologies
  • Biological Transport
  • Mathematical Concepts
  • Phenomena and Processes
  • Health Care Evaluation Mechanisms
  • Information Science
  • Epidemiologic Methods
  • Metabolism
  • Public Health
  • Investigative Techniques
  • Metabolic Phenomena
  • Quality of Health Care
  • Health Care Quality, Access, and Evaluation
  • Analytical, Diagnostic and Therapeutic Techniques and Equipment
  • Environment and Public Health
  • Health Care
  • Probability
  • Protein Transport
  • Artificial Intelligence
  • Human Anatomy & Physiology
  • Health & Biological Sciences
  • Animal Biochemistry
Label
Machine Learning for Protein Subcellular Localization Prediction, (electronic resource)
Instantiates
Publication
Note
Description based upon print version of record
Carrier category
online resource
Carrier category code
cr
Content category
text
Content type code
txt
Contents
  • Preface; Contents; List of Abbreviations; 1 Introduction; 1.1 Proteins and their subcellular locations; 1.2 Why computationally predict protein subcellular localization?; 1.2.1 Significance of the subcellular localization of proteins; 1.2.2 Conventional wet-lab techniques; 1.2.3 Computational prediction of protein subcellular localization; 1.3 Organization of this book; 2 Overview of subcellular localization prediction; 2.1 Sequence-based methods; 2.1.1 Composition-based methods; 2.1.2 Sorting signal-based methods; 2.1.3 Homology-based methods; 2.2 Knowledge-based methods
  • 2.2.1 GO-term extraction2.2.2 GO-vector construction; 2.3 Limitations of existing methods; 2.3.1 Limitations of sequence-based methods; 2.3.2 Limitations of knowledge-based methods; 3 Legitimacy of using gene ontology information; 3.1 Direct table lookup?; 3.1.1 Table lookup procedure for single-label prediction; 3.1.2 Table-lookup procedure for multi-label prediction; 3.1.3 Problems of table lookup; 3.2 Using only cellular component GO terms?; 3.3 Equivalent to homologous transfer?; 3.4 More reasons for using GO information; 4 Single-location protein subcellular localization
  • 4.1 Extracting GO from the Gene Ontology Annotation Database4.1.1 Gene Ontology Annotation Database; 4.1.2 Retrieval of GO terms; 4.1.3 Construction of GO vectors; 4.1.4 Multiclass SVM classification; 4.2 FusionSVM: Fusion of gene ontology and homology-based features; 4.2.1 InterProGOSVM: Extracting GO from InterProScan; 4.2.2 PairProSVM: A homology-based method; 4.2.3 Fusion of InterProGOSVM and PairProSVM; 4.3 Summary; 5 From single- to multi-location; 5.1 Significance of multi-location proteins; 5.2 Multi-label classification; 5.2.1 Algorithm-adaptation methods
  • 5.2.2 Problem transformation methods5.2.3 Multi-label classification in bioinformatics; 5.3 mGOASVM: A predictor for both single- and multi-location proteins; 5.3.1 Feature extraction; 5.3.2 Multi-label multiclass SVM classification; 5.4 AD-SVM: An adaptive decision multi-label predictor; 5.4.1 Multi-label SVM scoring; 5.4.2 Adaptive decision for SVM (AD-SVM); 5.4.3 Analysis of AD-SVM; 5.5 mPLR-Loc: A multi-label predictor based on penalized logistic regression; 5.5.1 Single-label penalized logistic regression; 5.5.2 Multi-label penalized logistic regression
  • 5.5.3 Adaptive decision for LR (mPLR-Loc)5.6 Summary; 6 Mining deeper on GO for protein subcellular localization; 6.1 Related work; 6.2 SS-Loc: Using semantic similarity over GO; 6.2.1 Semantic similarity measures; 6.2.2 SS vector construction; 6.3 HybridGO-Loc: Hybridizing GO frequency and semantic similarity features; 6.3.1 Hybridization of two GO features; 6.3.2 Multi-label multiclass SVM classification; 6.4 Summary; 7 Ensemble random projection for large-scale predictions; 7.1 Random projection; 7.2 RP-SVM: A multi-label classifier with ensemble random projection
  • 7.2.1 Ensemble multi-label classifier
Dimensions
unknown
Extent
1 online resource (210 p.)
Form of item
electronic
Isbn
9781501501524
Media category
computer
Media type code
c
Specific material designation
remote
System control number
  • (CKB)3710000000420347
  • (EBL)1820373
  • (SSID)ssj0001482330
  • (PQKBManifestationID)12496229
  • (PQKBTitleCode)TC0001482330
  • (PQKBWorkID)11508592
  • (PQKB)10943492
  • (EXLCZ)993710000000420347
Label
Machine Learning for Protein Subcellular Localization Prediction, (electronic resource)
Publication
Note
Description based upon print version of record
Carrier category
online resource
Carrier category code
cr
Content category
text
Content type code
txt
Contents
  • Preface; Contents; List of Abbreviations; 1 Introduction; 1.1 Proteins and their subcellular locations; 1.2 Why computationally predict protein subcellular localization?; 1.2.1 Significance of the subcellular localization of proteins; 1.2.2 Conventional wet-lab techniques; 1.2.3 Computational prediction of protein subcellular localization; 1.3 Organization of this book; 2 Overview of subcellular localization prediction; 2.1 Sequence-based methods; 2.1.1 Composition-based methods; 2.1.2 Sorting signal-based methods; 2.1.3 Homology-based methods; 2.2 Knowledge-based methods
  • 2.2.1 GO-term extraction2.2.2 GO-vector construction; 2.3 Limitations of existing methods; 2.3.1 Limitations of sequence-based methods; 2.3.2 Limitations of knowledge-based methods; 3 Legitimacy of using gene ontology information; 3.1 Direct table lookup?; 3.1.1 Table lookup procedure for single-label prediction; 3.1.2 Table-lookup procedure for multi-label prediction; 3.1.3 Problems of table lookup; 3.2 Using only cellular component GO terms?; 3.3 Equivalent to homologous transfer?; 3.4 More reasons for using GO information; 4 Single-location protein subcellular localization
  • 4.1 Extracting GO from the Gene Ontology Annotation Database4.1.1 Gene Ontology Annotation Database; 4.1.2 Retrieval of GO terms; 4.1.3 Construction of GO vectors; 4.1.4 Multiclass SVM classification; 4.2 FusionSVM: Fusion of gene ontology and homology-based features; 4.2.1 InterProGOSVM: Extracting GO from InterProScan; 4.2.2 PairProSVM: A homology-based method; 4.2.3 Fusion of InterProGOSVM and PairProSVM; 4.3 Summary; 5 From single- to multi-location; 5.1 Significance of multi-location proteins; 5.2 Multi-label classification; 5.2.1 Algorithm-adaptation methods
  • 5.2.2 Problem transformation methods5.2.3 Multi-label classification in bioinformatics; 5.3 mGOASVM: A predictor for both single- and multi-location proteins; 5.3.1 Feature extraction; 5.3.2 Multi-label multiclass SVM classification; 5.4 AD-SVM: An adaptive decision multi-label predictor; 5.4.1 Multi-label SVM scoring; 5.4.2 Adaptive decision for SVM (AD-SVM); 5.4.3 Analysis of AD-SVM; 5.5 mPLR-Loc: A multi-label predictor based on penalized logistic regression; 5.5.1 Single-label penalized logistic regression; 5.5.2 Multi-label penalized logistic regression
  • 5.5.3 Adaptive decision for LR (mPLR-Loc)5.6 Summary; 6 Mining deeper on GO for protein subcellular localization; 6.1 Related work; 6.2 SS-Loc: Using semantic similarity over GO; 6.2.1 Semantic similarity measures; 6.2.2 SS vector construction; 6.3 HybridGO-Loc: Hybridizing GO frequency and semantic similarity features; 6.3.1 Hybridization of two GO features; 6.3.2 Multi-label multiclass SVM classification; 6.4 Summary; 7 Ensemble random projection for large-scale predictions; 7.1 Random projection; 7.2 RP-SVM: A multi-label classifier with ensemble random projection
  • 7.2.1 Ensemble multi-label classifier
Dimensions
unknown
Extent
1 online resource (210 p.)
Form of item
electronic
Isbn
9781501501524
Media category
computer
Media type code
c
Specific material designation
remote
System control number
  • (CKB)3710000000420347
  • (EBL)1820373
  • (SSID)ssj0001482330
  • (PQKBManifestationID)12496229
  • (PQKBTitleCode)TC0001482330
  • (PQKBWorkID)11508592
  • (PQKB)10943492
  • (EXLCZ)993710000000420347

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