IntelliGenes:用于生物标记物发现和预测医学的交互式、用户友好型多模态人工智能/人工智能应用。

IF 2.5 Q3 BIOCHEMICAL RESEARCH METHODS Biology Methods and Protocols Pub Date : 2024-05-29 eCollection Date: 2024-01-01 DOI:10.1093/biomethods/bpae040
Rishabh Narayanan, William DeGroat, Dinesh Mendhe, Habiba Abdelhalim, Zeeshan Ahmed
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引用次数: 0

摘要

人工智能(AI)和机器学习(ML)已在多个生活领域取得进展,但在多组学领域的进展却无法与其他领域相提并论。所面临的挑战包括但不限于处理和分析大量复杂的多组学数据,以及实施和执行人工智能/ML 方法所需的专业知识。在这篇文章中,我们介绍了 IntelliGenes,这是一种交互式、可定制、跨平台、用户友好的人工智能/ML 应用程序,用于多组学数据探索,以发现新型生物标记物,预测罕见、常见和复杂疾病。该方法基于传统统计技术和前沿 ML 算法的结合,优于单一算法并提高了准确性。IntelliGenes 的交互式跨平台图形用户界面分为三个主要部分:(i) 数据管理器,(ii) 人工智能/ML 分析,以及 (iii) 可视化。数据管理器支持用户加载和定制输入数据和现有生物标记物列表。AI/ML 分析允许用户应用默认的统计和 ML 算法组合,以及自定义和创建新的 AI/ML 管道。可视化功能提供了多种选项,用于解释各种生成结果,包括性能指标、疾病预测和各种图表。IntelliGenes 的性能已在不同的内部研究和同行评议研究中进行了成功测试,能够正确地将个体分类为患者,并高精度地预测疾病。它的与众不同之处主要在于其简单易用,适合非技术用户使用,并强调生成可解释的可视化结果。我们设计和实施 IntelliGenes 的方式是,无论用户是否具有计算背景,都可以应用人工智能/ML 方法来发现新的生物标记物和预测疾病。
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IntelliGenes: Interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine.

Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of multi-omics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex multi-omics data, and the expertise needed to implement and execute AI/ML approaches. In this article, we present IntelliGenes, an interactive, customizable, cross-platform, and user-friendly AI/ML application for multi-omics data exploration to discover novel biomarkers and predict rare, common, and complex diseases. The implemented methodology is based on a nexus of conventional statistical techniques and cutting-edge ML algorithms, which outperforms single algorithms and result in enhanced accuracy. The interactive and cross-platform graphical user interface of IntelliGenes is divided into three main sections: (i) Data Manager, (ii) AI/ML Analysis, and (iii) Visualization. Data Manager supports the user in loading and customizing the input data and list of existing biomarkers. AI/ML Analysis allows the user to apply default combinations of statistical and ML algorithms, as well as customize and create new AI/ML pipelines. Visualization provides options to interpret a diverse set of produced results, including performance metrics, disease predictions, and various charts. The performance of IntelliGenes has been successfully tested at variable in-house and peer-reviewed studies, and was able to correctly classify individuals as patients and predict disease with high accuracy. It stands apart primarily in its simplicity in use for nontechnical users and its emphasis on generating interpretable visualizations. We have designed and implemented IntelliGenes in a way that a user with or without computational background can apply AI/ML approaches to discover novel biomarkers and predict diseases.

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来源期刊
Biology Methods and Protocols
Biology Methods and Protocols Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
3.80
自引率
2.80%
发文量
28
审稿时长
19 weeks
期刊最新文献
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