XAI meets Biology: A Comprehensive Review of Explainable AI in Bioinformatics Applications

• URL: http://arxiv.org/abs/2312.06082v1
• Date: Mon, 11 Dec 2023 03:08:18 GMT
• Title: XAI meets Biology: A Comprehensive Review of Explainable AI in Bioinformatics Applications
• Authors: Zhongliang Zhou, Mengxuan Hu, Mariah Salcedo, Nathan Gravel, Wayland Yeung, Aarya Venkat, Dongliang Guo, Jielu Zhang, Natarajan Kannan, Sheng Li
• Abstract summary: Explainable AI (XAI) has emerged as a promising solution to enhance the transparency and interpretability of AI models in bioinformatics. This review provides a comprehensive analysis of various XAI techniques and their applications across various bioinformatics domains.
• Score: 5.91274133032321
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Artificial intelligence (AI), particularly machine learning and deep learning models, has significantly impacted bioinformatics research by offering powerful tools for analyzing complex biological data. However, the lack of interpretability and transparency of these models presents challenges in leveraging these models for deeper biological insights and for generating testable hypotheses. Explainable AI (XAI) has emerged as a promising solution to enhance the transparency and interpretability of AI models in bioinformatics. This review provides a comprehensive analysis of various XAI techniques and their applications across various bioinformatics domains including DNA, RNA, and protein sequence analysis, structural analysis, gene expression and genome analysis, and bioimaging analysis. We introduce the most pertinent machine learning and XAI methods, then discuss their diverse applications and address the current limitations of available XAI tools. By offering insights into XAI's potential and challenges, this review aims to facilitate its practical implementation in bioinformatics research and help researchers navigate the landscape of XAI tools.

Related papers

• Large Language Models in Bioinformatics: A Survey [13.722344139230827]
  Large Language Models (LLMs) are revolutionizing bioinformatics, enabling advanced analysis of DNA, RNA, proteins, and single-cell data. This survey provides a systematic review of recent advancements, focusing on genomic sequence modeling, RNA structure prediction, protein function inference, and single-cell transcriptomics.
  arXiv  Detail & Related papers  (2025-03-06T14:38:20Z)
• Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence [54.14779179869007]
  We highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for data generation, sharing, and analysis.
  arXiv  Detail & Related papers  (2025-02-21T13:20:33Z)
• Large Language Models for Bioinformatics [58.892165394487414]
  This survey focuses on the evolution, classification, and distinguishing features of bioinformatics-specific language models (BioLMs) We explore the wide-ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities.
  arXiv  Detail & Related papers  (2025-01-10T01:43:05Z)
• Explainable AI Methods for Multi-Omics Analysis: A Survey [3.885941688264509]
  Multi-omics refers to the integrative analysis of data derived from multiple 'omes' Deep learning methods are increasingly utilized to integrate multi-omics data, offering insights into molecular interactions and enhancing research into complex diseases. These models, with their numerous interconnected layers and nonlinear relationships, often function as black boxes, lacking transparency in decision-making processes. This review explores how xAI can improve the interpretability of deep learning models in multi-omics research, highlighting its potential to provide clinicians with clear insights.
  arXiv  Detail & Related papers  (2024-10-15T05:01:17Z)
• BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments [112.25067497985447]
  We introduce BioDiscoveryAgent, an agent that designs new experiments, reasons about their outcomes, and efficiently navigates the hypothesis space to reach desired solutions. BioDiscoveryAgent can uniquely design new experiments without the need to train a machine learning model. It achieves an average of 21% improvement in predicting relevant genetic perturbations across six datasets.
  arXiv  Detail & Related papers  (2024-05-27T19:57:17Z)
• An Evaluation of Large Language Models in Bioinformatics Research [52.100233156012756]
  We study the performance of large language models (LLMs) on a wide spectrum of crucial bioinformatics tasks. These tasks include the identification of potential coding regions, extraction of named entities for genes and proteins, detection of antimicrobial and anti-cancer peptides, molecular optimization, and resolution of educational bioinformatics problems. Our findings indicate that, given appropriate prompts, LLMs like GPT variants can successfully handle most of these tasks.
  arXiv  Detail & Related papers  (2024-02-21T11:27:31Z)
• Extracting human interpretable structure-property relationships in chemistry using XAI and large language models [0.4769602527256662]
  We propose the XpertAI framework that integrates XAI methods with large language models (LLMs) accessing scientific literature to generate natural language explanations of raw chemical data automatically. Our results show that XpertAI combines the strengths of LLMs and XAI tools in generating specific, scientific, and interpretable explanations.
  arXiv  Detail & Related papers  (2023-11-07T15:02:32Z)
• ProBio: A Protocol-guided Multimodal Dataset for Molecular Biology Lab [67.24684071577211]
  The challenge of replicating research results has posed a significant impediment to the field of molecular biology. We first curate a comprehensive multimodal dataset, named ProBio, as an initial step towards this objective. Next, we devise two challenging benchmarks, transparent solution tracking and multimodal action recognition, to emphasize the unique characteristics and difficulties associated with activity understanding in BioLab settings.
  arXiv  Detail & Related papers  (2023-11-01T14:44:01Z)
• eXplainable Artificial Intelligence (XAI) in aging clock models [0.0]
  We discuss the application of XAI for developing the "aging clocks" We present a comprehensive analysis of the literature categorized by the focus on particular physiological systems.
  arXiv  Detail & Related papers  (2023-07-21T18:06:43Z)
• BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks [68.39821375903591]
  Generalist AI holds the potential to address limitations due to its versatility in interpreting different data types. Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model.
  arXiv  Detail & Related papers  (2023-05-26T17:14:43Z)
• Explainable AI for Bioinformatics: Methods, Tools, and Applications [1.6855835471222005]
  Explainable artificial intelligence (XAI) is an emerging field that aims to mitigate the opaqueness of black-box models. In this paper, we discuss the importance of explainability with a focus on bioinformatics.
  arXiv  Detail & Related papers  (2022-12-25T21:00:36Z)
• Machine learning in bioprocess development: From promise to practice [58.720142291102135]
  Data-driven methods like machine learning (ML) approaches have a high potential to rationally explore large design spaces. The aim of this review is to demonstrate how ML methods have been applied so far in bioprocess development.
  arXiv  Detail & Related papers  (2022-10-04T13:48:59Z)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.