Selby David Antony, Jakhmola Rashika, Sprang Maximilian, Großmann Gerrit, Raki Hind, Maani Niloofar, Pavliuk Daria, Ewald Jan, Vollmer Sebastian
Data Science and its Applications, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
Front Artif Intell. 2025 Jul 17;8:1595291. doi: 10.3389/frai.2025.1595291. eCollection 2025.
Biomarker discovery and drug response prediction are central to personalized medicine, driving demand for predictive models that also offer biological insights. Biologically informed neural networks (BINNs), also referred to as visible neural networks (VNNs), have recently emerged as a solution to this goal. BINNs or VNNs are neural networks whose inter-layer connections are constrained based on prior knowledge from gene ontologies and pathway databases. These sparse models enhance interpretability by embedding prior knowledge into their architecture, ideally reducing the space of learnable functions to those that are biologically meaningful.
This systematic review-the first of its kind-identified 86 recent papers implementing BINNs/VNNs. We analyzed these papers to highlight key trends in architectural design, data sources and evaluation methodologies.
Our analysis reveals a growing adoption of BINNs/VNNs. However, this growth is apparently juxtaposed with a lack of standardized, terminology, computational tools and benchmarks.
BINNs/VNNs represent a promising approach for integrating biological knowledge into predictive models for personalized medicine. Addressing the current deficiencies in standardization and tooling is important for widespread adoption and further progress in the field.
生物标志物的发现和药物反应预测是个性化医疗的核心，推动了对能提供生物学见解的预测模型的需求。生物信息神经网络（BINNs），也被称为可见神经网络（VNNs），最近作为实现这一目标的解决方案而出现。BINNs或VNNs是其层间连接基于来自基因本体和通路数据库的先验知识而受到约束的神经网络。这些稀疏模型通过将先验知识嵌入其架构来增强可解释性，理想情况下将可学习函数的空间缩小到具有生物学意义的函数。
这一首次此类系统性综述识别出了86篇近期实施BINNs/VNNs的论文。我们分析这些论文以突出架构设计、数据源和评估方法学方面的关键趋势。
我们的分析显示BINNs/VNNs的应用日益增多。然而，这种增长显然与缺乏标准化、术语、计算工具和基准测试并存。
BINNs/VNNs是将生物学知识整合到个性化医疗预测模型中的一种有前景的方法。解决当前在标准化和工具方面的不足对于该领域的广泛应用和进一步发展很重要。
