Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Genomics02:02

Genomics

39.5K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
39.5K
Genetic Lingo01:11

Genetic Lingo

113.5K
Overview
113.5K
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

7.3K
Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
7.3K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.2K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
15.2K
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.8K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.8K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

17.8K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
17.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

GWAS analysis of a depression cohort defined by an EHR-phenotyping algorithm reveals the role of immune regulations in depression risk.

Frontiers in genetics·2026
Same author

Molecular mechanisms underlying amyloid lowering by aducanumab: differential and comparative effects of sex and IgG reveal the post-treatment disease brain.

bioRxiv : the preprint server for biology·2026
Same author

Unsupervised characterization of 100,272 EHR patients identifies high-risk groups and comorbidities linked to premature aging.

NPJ digital medicine·2026
Same author

Evaluating the feasibility of the CIPHER metadata framework towards building a conceptual phenotype standard.

JAMIA open·2026
Same author

Punctate Intramural Gastric Calcifications Reveal Diffuse-Type Adenocarcinoma in Asymptomatic Iron Deficiency Anemia.

Gastro hep advances·2026
Same author

Author Correction: Transformer patient embedding using electronic health records enables patient stratification and progression analysis.

NPJ digital medicine·2026

Related Experiment Video

Updated: Jan 6, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

980

Biomedical literature-based clinical phenotype definition discovery using large language models.

Samar Binkheder1,2, Xiaofu Liu2, Michael Wu3

  • 1Medical Informatics Unit, Department of Medical Education, College of Medicine, King Saud University, Riyadh 12372, Saudi Arabia.

Database : the Journal of Biological Databases and Curation
|September 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an automated text-mining approach to extract clinical phenotype definitions from biomedical literature. The developed Clinical Phenotype Knowledgebase (CliPheKB) aids researchers in discovering phenotype-related sentences efficiently.

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.2K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

9.1K

Related Experiment Videos

Last Updated: Jan 6, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

980
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.2K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

9.1K

Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Computational Biology

Background:

  • Electronic Health Record (EHR) phenotyping is crucial but challenging due to undefined phenotypes.
  • Automated methods are needed to efficiently extract clinical phenotype definitions from vast biomedical literature.

Purpose of the Study:

  • To develop and evaluate a text-mining pipeline for automatically extracting clinical phenotype definition-related sentences from biomedical literature.
  • To create a searchable knowledgebase of clinical phenotype definitions.

Main Methods:

  • Developed abstract-level and full-text sentence-level classifiers using machine learning algorithms including Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes, Decision Trees, Convolutional Neural Networks (CNNs), Bidirectional Encoder Representations from Transformers (BERT), and BioBERT.
  • Compared classifier performance using F-measure, selecting SVM for abstract-level and BioBERT for sentence-level classification.
  • Performed large-scale screening of the PubMed database to identify millions of relevant sentences and constructed the Clinical Phenotype Knowledgebase (CliPheKB).

Main Results:

  • The SVM abstract-level classifier achieved 98% F-measure, identifying over 4.5 million relevant abstracts.
  • The BioBERT full-text sentence-level classifier achieved 91% F-measure.
  • The system successfully extracted over two million clinical phenotype-related sentences, forming the basis for the CliPheKB.

Conclusions:

  • The developed text-mining pipeline provides a high-throughput, generalizable, and scalable method for clinical phenotype discovery.
  • The Clinical Phenotype Knowledgebase (CliPheKB) offers a valuable resource for researchers to query and retrieve phenotype-specific sentences from biomedical literature.
  • This approach complements existing EHR phenotyping methods, advancing the field of automated clinical research.