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Raja K. Biomedical Text Mining 2022
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This volume details step-by-step instructions on biomedical literature mining protocols. Chapters guide readers through various topics such as, disease comorbidity, literature-based discovery, protocols to combine literature mining, machine learning for predicting biomedical discoveries, and uncovering unknown public knowledge by combining two pieces of information from different sets of PubMed articles. Additional chapters discuss the importance of data science to understand outbreaks such as COVID-19. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Biomedical Text Mining aims to be a useful practical guide to researches to help further their studies.
Preface
Contributors
Biomedical Literature Mining and Its Components
Introduction
Documents Retrieval
Identification of Biomedical Entities
Information Extraction
Methods
Overview
Extracting Patient Population Information
Evaluation Dataset
Classifying PubMed Sentences
Classification Using Maximum Entropy (MaxEnt) Classifier
Classification Using Naïve-Bayes Classifier
Extracting the Phrase with Patient Population Information
Identifying Disease and Drug Mentions
Retrieving Relevant PubMed Abstracts
Notes
References
Text Mining Protocol to Retrieve Significant Drug-Gene Interactions from PubMed Abstracts
Introduction
Drug-Gene Interaction
Adverse Drug Reactions (ADRs)
Text Mining for Disease-Gene Association
Pharmacogenomics Knowledge Base (PharmGKB)
DrugBank
OpenTargets Platform
Gene Druggability
Precision Medicine
BioCreative Community
Methods
Creating Desktop Version of PubMed for Text Mining Purpose
PubMed Download
Simple Protocol for Building the Desktop Version
Gene Annotation from genepubmed
Gene Information from Entrez Gene
Mapping Gene ID to Gene Name, Gene Symbol, and Aliases
PubMed Abstracts to Sentences
Gene Recognition and Extraction
Pattern Matching Rules
Ambiguity
Disambiguation Approach
UMLS Metathesaurus for Identifying Drug Mentions
Processing UMLS Metathesaurus
Drugs from UMLS Metathesaurus
Drug Recognition and Extraction
Filtering Significant Drug-Gene Interaction Information
Machine Learning
Algorithm Selection
Feature Extraction
Drug Gene Pairs from PharmGKB
Exclusion of Sentences with Genes and Drugs from PharmGKB
Feature Selection
Classification Using Machine Learning Algorithms
Notes
References
A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and
Introduction
Gene Drug Targets
Text Mining in Drug Target Discovery
Validation of Drug targets and Drug-Target Ontology
Expression Analysis
Limitations and Challenges of Application of Text Mining in Target Discovery
Methods
Microarray expression Data annotation Approach to Determine Disease Gene Targets
Gene Expression Analysis Using GEOR
Gene Enrichment and Functional Annotation Analysis Using DAVID Tool
Text Mining Approach to Determine Genes for any Disease Target
Retrieval of Literature Information from pubmedensembl
Retrieval of PMIDs Using e-Utils
Retrieval of Genes Related to Muscular Dystrophy from PubMed Using genepubmed
Annotating Genes with Corresponding PMIDs for Muscular Dystrophy
Retrieval of Genes from Comparative Toxicogenomics Database
Network Analysis Using STRING Database
Notes
References
Finding Gene Associations by Text Mining and Annotating it with Gene Ontology
Introduction
Genome Sequencing, Human Genome Project, and ENCODE Project
Genes and Its Evolution
Gene Prediction
Text Mining in Functional Genomics
Sequence Ontology (SO)
Gene Ontology (GO)
Medical Subject Headings (MeSH)
Functional Annotation
Methods
Overview of the Protocol
Functional Annotation by Text Mining Approach
Text Mining Using a Tool PubTator
Data Extraction
Expasy (Expert Protein Analysis System) Translate Tool
Gene Ontology (GO) Annotation Using BlastGO
Functional Annotation by Semantic Similarity Approach
Retrieval of MeSH Terms from Coremine Medical
Semantic Similarity Analysis
MeSH Semantic Similarity
GO Semantic Similarity
Jaccard Similarity Analysis
Notes
References
Biomedical Literature Mining for Repurposing Laboratory Tests
Introduction
Methods
Machine Learning Models and Lab Feature Importance
Mine Literature for Lab-Diagnosis Associations
Text Mining Algorithm
Curate Diagnosis and Lab Names for Search
Building a Text Index
Rank Predictive Lab-Diagnosis Candidates
Further Evaluation of Top Candidates
Odds Ratio Assessment
Manual Literature Inspection
Notes
References
A Simple Computational Approach to Identify Potential Drugs for Multiple Sclerosis and Cognitive Disorders from Exp
Introduction
Methods
Comprehensive Association Databases from Experts´ Curated Resources
Identification of Drug-Gene Interactions from Experts Curated Resources
Selection of Risk Genes
Experts´ Opinion and Literature Evidence
Mechanistic Insights on the Drugs Interacting with GWAS Signals for MS
Mechanistic Insights on the Drugs Interacting with GWAS Signals for Cognitive Disorder
Future Work
Notes
References
Combining Literature Mining and Machine Learning for Predicting Biomedical Discoveries
Introduction
Methods
Overview
Named Entity Recognition
Medical Entity Recognition
Literature Based Discovery
Literature Based Discovery Using DisGeReExT
Deep Learning for Literature Based Discovery
Evaluation Measures
Precision, Recall and F-Score
ROC (Receiver Operating Characteristic) Curve
Cross-Validation
Notes
References
A Text Mining Protocol for Mining Biological Pathways and Regulatory Networks from Biomedical Literature
Introduction
Key Pathways and Regulatory Networks
Biomedical Text Resources/Corpora
State-of-the-Art Existing Studies to Extract Biological Pathways
Methods
Preprocessing
Named Entity Recognition
Bioevent Extraction
Regulatory Network Construction
Postprocessing and Network Visualization
Evaluation Metrics
Notes
References
Text Mining and Machine Learning Protocol for Extracting Human-Related Protein Phosphorylation Information from Pub
Introduction
Proteins as the `molecules of life´
Post translational Modifications of Proteins (PTMs)
Databases on Protein Phosphorylation
Methods
Preprocessing of Textual Content
Tagging of Entities
Recognition of Phosphorylation Keyword
Base-Format and Subformat Templates
Extraction of Entities
Singles/Pair/Triplet Classification by SVM
Characteristics of Machine Learning Model
Classification Using Support Vector Machine
Notes
References
A Text Mining and Machine Learning Protocol for Extracting Posttranslational Modifications of Proteins from PubMed
Introduction
Glycosylation of Proteins
N-Linked Glycosylation
Acetylation
N-Terminal Acetylation
Lysine Acetylation
Methylation
Arginine Methylation
Lysine Methylation
Hydroxylation
Ubiquitination
Existing Text Mining Approaches
Reusing an Existing Approach for Selected PTM Extraction
Methods
Extracting Protein Glycosylation Information
Named Entity Recognition
Recognition of Keywords
Basic Pattern Forms and Subbasic Pattern Forms
Machine Learning for Classification
Extraction of Protein Acetylation Information
Extraction of Protein Methylation Information
Extraction of Protein Hydroxylation Information
Extraction of Protein Ubiquitination Information
Notes
References
A Hybrid Protocol for Identifying Comorbidity-Based Potential Drugs for COVID- Using Biomedical Literature Minin
Introduction
Methods
Comorbidity Analysis
Biomedical Literature-Based Comorbidity Analysis
Gene Concept Finding Using PubTator
Collection of Multiomics Data
Transcriptomic Data Analysis
Identification of Differentially Expressed Genes
Proteomics Data Analysis
Comorbidity Analysis Using GWAS
Construction of Interactome Network
Network-Based Comorbidity Analysis
Mapping of Gene Signatures with Drug Profile Using CMAP
Gene-Expression Hypothesis
Deep Learning Using Knowledge Graph Mining
Performance Evaluation
Notes
References
BioBERT and Similar Approaches for Relation Extraction
Introduction
Methods
Pretraining Task-Matching the Blanks (MTB)
Fine Tuning Task-Relation Extraction
BioBERT
Prerequisite
Pretrained Weights
Pretraining Corpus
Installation
Datasets
Fine-Tuning BioBERT
Relation Extraction
Protocol for Generating Biomedical Language Models
Notes
References
A Text Mining Protocol for Predicting Drug-Drug Interaction and Adverse Drug Reactions from PubMed Articles
Introduction
Drug-Drug Interaction
Brief Note on Existing Text Mining Systems on DDI Extraction
Adverse Drug Reaction
Brief Note on Existing Text Mining Systems on ADR Extraction
Significance of DDI and ADR in Current Healthcare
Methods
Text Mining
Local Version of PubMed
Gene Annotation from GenePubmed
Chemicals and Drugs Lexicon
Processing UMLS Metathesaurus
Chemicals and Drugs from UMLS Metathesaurus
Postprocessing of Chemicals and Drugs Lexicon
Drugs from DrugBank
Drug from PharmGKB
Combining UMLS Metathesaurus, DrugBank, and PharmGKB
Drugs Lexicon
Mapping Drug Names
Extracting Expert Curated Drug-Gene Interaction
Finding Common Interacting Genes for Drug Pairs
Evaluation Metrics for Drug Name Recognition
Machine Learning
Feature Selection
Classifying DDI and ADR
Evaluation of DDI and ADR Prediction
Notes
References
A Text Mining Protocol for Extracting Drug-Drug Interaction and Adverse Drug Reactions Specific to Patient Populat
Introduction
Drug-Drug Interaction
Administration/Absorption
Distribution
Metabolism
Excretion/Elimination
Food-Drug Interaction
Adverse Drug Reaction
ADRs Related to Age
ADRs Related to Gender
ADRs During Pregnancy
Drug Independent Adverse Reactions
Methods
Text Processing
Building Local PubMed
Drugs and Biologics Lexicon
Concept Extraction
Retrieval of Relevant PubMed Articles
PubMed Articles with DDIs and ADRs
PubMed Articles with DDIs and ADRs Related to Age
Gender-Based DDIs and ADRs in PubMed
Pharmacokinetics and Pharmacodynamics Related DDI and ADR in PubMed
Pharmacokinetics Related DDI and ADR in PubMed
Pharmacodynamics Related DDI and ADR in PubMed
DDI and ADR Prediction from PubMed Abstracts
Notes
References
Extracting Significant Comorbid Diseases from MeSH Index of PubMed
Introduction
Disease Comorbidity
Multimorbidity
Text Mining Approach in Disease Comorbidity
Methods
UMLS Metathesaurus-Installation and Processing
SNOMED CT-Installation and Processing
Diseases from UMLS Metathesaurus
Diseases from SNOMED CT
Diseases from UMLS Metathesaurus and SNOMED CT
Postprocessing
Diseases that Are Stop Words
Disease Concepts Matching English Vocabulary
Semantic Types as Disease Concepts
Range of Values or Symbols as Disease Concepts
Multiple CUI for a Disease
Reduced List of Diseases
Offline Version of PubMed Database
Retrieval of Disease-Related MeSH Index of PubMed Articles
Recognizing Diseases Using MedTagger
Retrieval of Significant Comorbid Diseases
Notes
References
Integration of Transcriptomics Data and Metabolomic Data Using Biomedical Literature Mining and Pathway Analysis
Introduction
Transcriptomics
Metabolomics
Knowledge-Based Approaches
Methods
Extraction of Transcriptomic and Metabolomic Data
Transcriptome Analysis (RNA-Seq Data Analysis)
LncRNA Identification
miRNA Data Analysis
Metabolome Data Analysis
Integration of Transcriptomic Data and Metabolomic Data Using Literature Mining
Data Integration of Transcriptome and Metabolome Using Pathway Analysis
Text Mining and Omics Data
Notes
References
Index