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Related Experiment Video

Updated: Jul 22, 2025

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

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

Published on: June 13, 2025

Explainable AI for Bioinformatics: Methods, Tools and Applications.

Md Rezaul Karim^1,2, Tanhim Islam³, Md Shajalal⁴

¹Computer Science 5 - Information Systems and Databases, RWTH Aachen University, Germany.

Briefings in Bioinformatics

|July 21, 2023

Summary

This summary is machine-generated.

Explainable AI (XAI) enhances transparency in bioinformatics by adapting domain-agnostic machine learning (ML) methods. This approach addresses the black-box nature of complex AI, improving decision fairness and accountability in critical applications.

Keywords:

NLP bioinformatics deep learning explainable AI interpretable machine learning machine learning

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Last Updated: Jul 22, 2025

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

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Published on: May 28, 2021

Area of Science:

Bioinformatics
Biomedical Informatics
Precision Medicine
Artificial Intelligence (AI)

Background:

Complex AI and machine learning (ML) models in bioinformatics are often opaque 'black-box' systems, hindering understanding of their decision-making processes.
Lack of transparency in AI poses challenges for end-users, decision-makers, and developers, particularly in healthcare where explainability and accountability are crucial.
Algorithmic fairness is a growing concern, requiring AI decisions to be free from bias against specific groups or individuals.

Conclusions:

Explainable AI (XAI) is vital for enhancing transparency, accountability, and fairness in bioinformatics applications.
Customization of existing interpretable ML methods is key to their successful implementation in bioinformatics.
This review serves as a starting point for researchers aiming to improve AI explainability and decision transparency in bioinformatics.