Machine learning for protein subcellular localization prediction / Shibiao Wan, Man-Wai Mak.

Format:

Book

Author/Creator:

Wan, Shibiao, author.

Mak, M. W., author.

Language:

English

Subjects (All):

Proteins--Physiological transport--Data processing.

Proteins.

Machine learning.

Probabilities--Data processing.

Probabilities.

Physical Description:

1 online resource (210 p.)

Edition:

1st ed.

Place of Publication:

Berlin, Germany ; Boston, Massachusetts : De Gruyter, 2015.

Language Note:

English

Summary:

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.

Contents:

Front matter

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

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781501501500

150150150X

9781501501524

1501501526

OCLC:

912323205