Review of ChatGPT Applications in Bioinformatics and Biomedical Informatics

Keskeiset käsitteet

The application of ChatGPT in bioinformatics and biomedical informatics shows promise but also highlights limitations that can be addressed through strategic prompt engineering.

Tiivistelmä

This review explores the applications of ChatGPT in various sectors of bioinformatics and biomedical informatics. It covers topics such as omics, genetics, biomedical text mining, drug discovery, biomedical image understanding, bioinformatics programming, and bioinformatics education. The review delves into the strengths and limitations of using ChatGPT in these areas and provides insights into potential avenues for future development.
Directory:

Introduction to AI in Scientific Research

AI's role across disciplines.

Omics Techniques

Application of GPT models in transcriptomics.
Identifying protein-coding regions within DNA sequences.

Genetics Counseling

Incorporation of ChatGPT by genetic counselors.

Biomedical Text Mining with ChatGPT

Performance assessments across typical tasks.
Biological pathway mining.

Drug Discovery with ChatGPT

Human-in-the-loop approach.
In-context learning for response accuracy.

Biomedical Image Understanding with GPT-4V(ision)
Bioinformatics Programming with ChatGPT

Application in applied bioinformatics.

Biomedical Database Access using SQL queries with LLMs like GPT-4.
Chatbots in Bioinformatics Education

Tilastot

"In 2023 alone, at least 2,074 manuscripts were indexed in PubMed when searching with the keyword 'ChatGPT'."
"ChatGPT demonstrates strong concordance with manual annotations for identifying cell types based on marker genes."
"ChatGPT excels at writing short scripts that call existing functions with specific instructions."
"AutoBA attained a 65% success rate in end-to-end automation for multi-omics data analysis."

Lainaukset

"Empowering Beginners in Bioinformatics with ChatGPT."
"An Extensive Benchmark Study on Biomedical Text Generation and Mining with ChatGPT."

Tärkeimmät oivallukset

Bioinformatics and Biomedical Informatics with ChatGPT

by Jinge Wang,Z... klo arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.15274.pdf

Syvällisempiä Kysymyksiä

How can the limitations of chatbots like ChatGPT be effectively mitigated to enhance their performance?

To mitigate the limitations of chatbots like ChatGPT and enhance their performance, several strategies can be employed:

Prompt Engineering: Crafting precise and strategic prompts is crucial for improving the accuracy of responses from chatbots. Including examples in prompts, utilizing CoT reasoning, and incorporating domain-specific knowledge into prompts can significantly enhance performance.

Human-in-the-loop Approach: Incorporating human feedback into the training process helps ground chatbot responses in real-world context and improves accuracy. This iterative exchange between AI and human operators ensures that responses align closely with intended task requirements.

Error Message Feedback: Automating error message feedback mechanisms within chatbot tools allows for quick identification and correction of errors in generated code or responses, leading to more reliable outcomes.

Retrieval-augmented Generation (RAG): Leveraging external knowledge bases or graphs to provide contextual hints for response generation enhances reliability by sourcing facts from domain-specific information.

Task-tuning and Instruction Finetuning: Task-tuning foundation models with specific data relevant to bioinformatics tasks or instruction tuning across a spectrum of tasks using instruction-output pairs can improve model adaptability to new challenges.

By implementing these strategies, it is possible to address the limitations of chatbots like ChatGPT effectively and optimize their performance in bioinformatics applications.

How ethical considerations should be taken into account when integrating large language models like GPTs into bioinformatics?

When integrating large language models like GPTs into bioinformatics, several ethical considerations must be taken into account:

Data Privacy: Ensuring that sensitive patient data used in bioinformatics analysis is protected from unauthorized access or misuse by implementing robust security measures.

Bias Mitigation: Addressing biases present in training data that could lead to skewed results or discriminatory outcomes in analyses conducted using GPTs.

Transparency: Providing clear explanations on how GPTs are utilized in bioinformatics processes, including disclosing any limitations or uncertainties associated with AI-generated outputs.

Accountability: Establishing accountability frameworks to attribute responsibility for decisions made based on AI recommendations generated by GPTs during bioinformatics analyses.

Informed Consent: Obtaining informed consent from individuals whose data is used in bioinformatics studies involving GPTs, ensuring they understand how their information will be processed.

How can the use of chatbots impact traditional educational methodologies in bioinformatics?

The use of chatbots has the potential to significantly impact traditional educational methodologies in bioinformatics:

Enhanced Accessibility: Chatbots make learning more accessible by providing immediate assistance with coding exercises, clarifying concepts through natural language interactions, and offering personalized feedback tailored to individual student needs.

2 .Interactive Learning: Students engage actively with course material through interactive sessions where they receive instant feedback on coding exercises or queries related to complex biological concepts.
3 .Promoting Critical Thinking: By prompting students with challenging questions that require problem-solving skills rather than rote memorization, chatbots encourage critical thinking abilities essential for success in research-oriented fields such as bioinformatics.
4 .Supplementing Traditional Teaching Methods: While not replacing traditional teaching methods entirely,
chatbots complement lectures and practical sessions by offering additional support outside regular class hours,
enabling students to practice coding skills independently at their own pace.
5 .Feedback Mechanism Improvement: The integration of error messages as a form
of constructive criticism aids students' understanding while also guiding them towards correcting mistakes
and refining their analytical capabilities over time.