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Biomedical Text Mining

Overview of attention for book
Cover of 'Biomedical Text Mining'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Biomedical Literature Mining and Its Components
  3. Altmetric Badge
    Chapter 2 Text Mining Protocol to Retrieve Significant Drug–Gene Interactions from PubMed Abstracts
  4. Altmetric Badge
    Chapter 3 A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining
  5. Altmetric Badge
    Chapter 4 Finding Gene Associations by Text Mining and Annotating it with Gene Ontology
  6. Altmetric Badge
    Chapter 5 Biomedical Literature Mining for Repurposing Laboratory Tests
  7. Altmetric Badge
    Chapter 6 A Simple Computational Approach to Identify Potential Drugs for Multiple Sclerosis and Cognitive Disorders from Expert Curated Resources
  8. Altmetric Badge
    Chapter 7 Combining Literature Mining and Machine Learning for Predicting Biomedical Discoveries
  9. Altmetric Badge
    Chapter 8 A Text Mining Protocol for Mining Biological Pathways and Regulatory Networks from Biomedical Literature
  10. Altmetric Badge
    Chapter 9 Text Mining and Machine Learning Protocol for Extracting Human-Related Protein Phosphorylation Information from PubMed
  11. Altmetric Badge
    Chapter 10 A Text Mining and Machine Learning Protocol for Extracting Posttranslational Modifications of Proteins from PubMed: A Special Focus on Glycosylation, Acetylation, Methylation, Hydroxylation, and Ubiquitination
  12. Altmetric Badge
    Chapter 11 A Hybrid Protocol for Identifying Comorbidity-Based Potential Drugs for COVID-19 Using Biomedical Literature Mining, Network Analysis, and Deep Learning
  13. Altmetric Badge
    Chapter 12 BioBERT and Similar Approaches for Relation Extraction
  14. Altmetric Badge
    Chapter 13 A Text Mining Protocol for Predicting Drug–Drug Interaction and Adverse Drug Reactions from PubMed Articles
  15. Altmetric Badge
    Chapter 14 A Text Mining Protocol for Extracting Drug–Drug Interaction and Adverse Drug Reactions Specific to Patient Population, Pharmacokinetics, Pharmacodynamics, and Disease
  16. Altmetric Badge
    Chapter 15 Extracting Significant Comorbid Diseases from MeSH Index of PubMed
  17. Altmetric Badge
    Chapter 16 Integration of Transcriptomics Data and Metabolomic Data Using Biomedical Literature Mining and Pathway Analysis
Attention for Chapter 15: Extracting Significant Comorbid Diseases from MeSH Index of PubMed
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Mentioned by

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2 X users

Citations

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Chapter title
Extracting Significant Comorbid Diseases from MeSH Index of PubMed
Chapter number 15
Book title
Biomedical Text Mining
Published in
Methods in molecular biology, June 2022
DOI 10.1007/978-1-0716-2305-3_15
Pubmed ID
35713870
Book ISBNs
978-1-07-162304-6, 978-1-07-162305-3
Authors

Dheepa Anand, Sharanya Manoharan, Oviya Ramalakshmi Iyyappan, Sadhanha Anand, Kalpana Raja, Anand, Dheepa, Manoharan, Sharanya, Iyyappan, Oviya Ramalakshmi, Anand, Sadhanha, Raja, Kalpana

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 33%
Unknown 2 67%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 33%
Unknown 2 67%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 July 2022.
All research outputs
#17,748,987
of 22,792,160 outputs
Outputs from Methods in molecular biology
#7,218
of 13,110 outputs
Outputs of similar age
#291,317
of 440,170 outputs
Outputs of similar age from Methods in molecular biology
#288
of 545 outputs
Altmetric has tracked 22,792,160 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,110 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 440,170 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 545 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.

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