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

36.5K
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...
36.5K
Biostatistics: Overview01:20

Biostatistics: Overview

285
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
285
Next-generation Sequencing03:00

Next-generation Sequencing

91.6K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
91.6K

You might also read

Related Articles

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

Sort by
Same author

Deep learning models for cell cycle phase prediction from single-cell RNA sequencing data.

Briefings in bioinformatics·2026
Same author

The mitochondrial protease, LonP1, is a potential cardioprotective target for attenuating doxorubicin-induced cardiomyocyte death.

Journal of translational medicine·2026
Same author

Text-dominant decision-making by large multimodal models in dermatology clinical challenges: Comment on "AI-assisted dermatologic diagnosis using a large language model".

Journal of the American Academy of Dermatology·2026
Same author

Regulating Electrostatic Discharge via Quasi-gate Electrode for High-Performance Direct-current Triboelectric Nanogenerators.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Variable performance of 5 detection methods for identifying intravenous fluid contamination in basic metabolic panels at an academic medical center.

Laboratory medicine·2026
Same author

Comparative Analysis of General-Purpose vs. Domain-Specific Multimodal Models for Diabetic Retinopathy Classification.

Diagnostics (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jul 25, 2025

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
09:10

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

9.2K

Empowering beginners in bioinformatics with ChatGPT.

Evelyn Shue1, Li Liu2,3, Bingxin Li4

  • 1Department of Microbiology, Immunology & Cell Biology, West Virginia University, Morgantown, WV 26506, USA.

Quantitative Biology (Beijing, China)
|June 28, 2023
PubMed
Summary

This study introduces a method to train chatbots for generating bioinformatics code, making data analysis education more accessible for beginners. The iterative model refines chatbot instructions for effective code generation in bioinformatics.

Keywords:
ChatGPTbioinformaticseducationscientific data analysis

More Related Videos

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

35.4K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.0K

Related Experiment Videos

Last Updated: Jul 25, 2025

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
09:10

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

9.2K
A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

35.4K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.0K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Educational Technology

Background:

  • Bioinformatics data analysis is crucial but challenging for beginners.
  • Large language models like ChatGPT offer potential for educational support.
  • Current chatbot capabilities require fine-tuning for specific scientific domains.

Purpose of the Study:

  • To develop and evaluate an iterative model for fine-tuning chatbot instructions.
  • To guide chatbots in generating code for bioinformatics data analysis tasks.
  • To assess the feasibility of chatbot-aided bioinformatics education.

Main Methods:

  • Proposed an iterative model to fine-tune chatbot instructions.
  • Applied the model to various bioinformatics data analysis tasks.
  • Demonstrated code generation capabilities of the fine-tuned chatbot.

Main Results:

  • The iterative model successfully guided the chatbot in generating relevant code.
  • Feasibility was demonstrated across diverse bioinformatics topics.
  • Identified practical considerations and limitations for implementation.

Conclusions:

  • Chatbot-aided instruction, guided by an iterative model, is a feasible approach for bioinformatics education.
  • This method can enhance learning for beginners in bioinformatics data analysis.
  • Further research is needed to address practical challenges and limitations.