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Data Mining for Systems Biology

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Cover of 'Data Mining for Systems Biology'

Table of Contents

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    Book Overview
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    Chapter 1 Identifying Bacterial Strains from Sequencing Data
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    Chapter 2 MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification
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    Chapter 3 Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas
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    Chapter 4 Generative Models for Quantification of DNA Modifications
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    Chapter 5 DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data
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    Chapter 6 Implementing a Transcription Factor Interaction Prediction System Using the GenoMetric Query Language
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    Chapter 7 Multiple Testing Tool to Detect Combinatorial Effects in Biology
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    Chapter 8 SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining
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    Chapter 9 Computing and Visualizing Gene Function Similarity and Coherence with NaviGO
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    Chapter 10 Analyzing Glycan-Binding Profiles Using Weighted Multiple Alignment of Trees
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    Chapter 11 Analysis of Fluxomic Experiments with Principal Metabolic Flux Mode Analysis
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    Chapter 12 Analyzing Tandem Mass Spectra Using the DRIP Toolkit: Training, Searching, and Post-Processing
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    Chapter 13 Sparse Modeling to Analyze Drug–Target Interaction Networks
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    Chapter 14 DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank
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    Chapter 15 MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing
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    Chapter 16 Disease Gene Classification with Metagraph Representations
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    Chapter 17 Inferring Antimicrobial Resistance from Pathogen Genomes in KEGG
Attention for Chapter 15: MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing
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Chapter title
MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing
Chapter number 15
Book title
Data Mining for Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-8561-6_15
Pubmed ID
Book ISBNs
978-1-4939-8560-9, 978-1-4939-8561-6
Authors

Shengwen Peng, Hiroshi Mamitsuka, Shanfeng Zhu, Peng, Shengwen, Mamitsuka, Hiroshi, Zhu, Shanfeng

Abstract

The US National Library of Medicine (NLM) uses the Medical Subject Headings (MeSH) (see Note 1 ) to index almost all 24 million citations in MEDLINE, which greatly facilitates the application of biomedical information retrieval and text mining. Large-scale automatic MeSH indexing has two challenging aspects: the MeSH side and citation side. For the MeSH side, each citation is annotated by only 12 (on average) out of all 28,000 MeSH terms. For the citation side, all existing methods, including Medical Text Indexer (MTI) by NLM, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well. To solve these two challenges, we developed the MeSHLabeler and DeepMeSH. By utilizing "learning to rank" (LTR) framework, MeSHLabeler integrates multiple types of information to solve the challenge in the MeSH side, while DeepMeSH integrates deep semantic representation to solve the challenge in the citation side. MeSHLabeler achieved the first place in both BioASQ2 and BioASQ3, and DeepMeSH achieved the first place in both BioASQ4 and BioASQ5 challenges. DeepMeSH is available at http://datamining-iip.fudan.edu.cn/deepmesh .

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 22%
Other 2 22%
Researcher 1 11%
Student > Bachelor 1 11%
Unknown 3 33%
Readers by discipline Count As %
Computer Science 2 22%
Agricultural and Biological Sciences 1 11%
Medicine and Dentistry 1 11%
Engineering 1 11%
Unknown 4 44%