A Review of BioTree Construction in the Context of Information Fusion: Priors, Methods, Applications and Trends

TL;DR

This review discusses how deep learning enhances biological tree construction by integrating biological priors and multimodal data, addressing traditional methods' limitations and advancing applications across biological disciplines.

Contribution

It systematically reviews recent DL-based methods for BioTree construction, emphasizing prior knowledge integration and multimodal data fusion, highlighting future research directions.

Findings

Deep learning improves BioTree accuracy and interpretability.

Integration of biological priors enhances model relevance.

Multimodal data fusion expands application scope.

Abstract

Biological tree (BioTree) analysis is a foundational tool in biology, enabling the exploration of evolutionary and differentiation relationships among organisms, genes, and cells. Traditional tree construction methods, while instrumental in early research, face significant challenges in handling the growing complexity and scale of modern biological data, particularly in integrating multimodal datasets. Advances in deep learning (DL) offer transformative opportunities by enabling the fusion of biological prior knowledge with data-driven models. These approaches address key limitations of traditional methods, facilitating the construction of more accurate and interpretable BioTrees. This review highlights critical biological priors essential for phylogenetic and differentiation tree analyses and explores strategies for integrating these priors into DL models to enhance accuracy and interpretability. Additionally, the review systematically examines commonly used data modalities and databases, offering a valuable resource for developing and evaluating multimodal fusion models. Traditional tree construction methods are critically assessed, focusing on their biological assumptions, technical limitations, and scalability issues. Recent advancements in DL-based tree generation methods are reviewed, emphasizing their innovative approaches to multimodal integration and prior knowledge incorporation. Finally, the review discusses diverse applications of BioTrees in various biological disciplines, from phylogenetics to developmental biology, and outlines future trends in leveraging DL to advance BioTree research. By addressing the challenges of data complexity and prior knowledge integration, this review aims to inspire interdisciplinary innovation at the intersection of biology and DL.