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raja k biomedical text mining 2022

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Textbook in PDF format 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

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raja k biomedical text mining 2022