arXiv subjects

Chenrui Duan

Publications and source records attributed to Chenrui Duan.

3 recordsLinked to original sources

arXiv2025

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

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.

q-bio.PE↗

arXiv2024

Must: Maximizing Latent Capacity of Spatial Transcriptomics Data

Spatial transcriptomics (ST) technologies have revolutionized the study of gene expression patterns in tissues by providing multimodality data in transcriptomic, spatial, and morphological, offering opportunities for understanding tissue biology beyond transcriptomics. However, we identify the modality bias phenomenon in ST data species, i.e., the inconsistent contribution of different modalities to the labels leads to a tendency for the analysis methods to retain the information of the dominant modality. How to mitigate the adverse effects of modality bias to satisfy various downstream tasks remains a fundamental challenge. This paper introduces Multiple-modality Structure Transformation, named MuST, a novel methodology to tackle the challenge. MuST integrates the multi-modality information contained in the ST data effectively into a uniform latent space to provide a foundation for all the downstream tasks. It learns intrinsic local structures by topology discovery strategy and topology fusion loss function to solve the inconsistencies among different modalities. Thus, these topology-based and deep learning techniques provide a solid foundation for a variety of analytical tasks while coordinating different modalities. The effectiveness of MuST is assessed by performance metrics and biological significance. The results show that it outperforms existing state-of-the-art methods with clear advantages in the precision of identifying and preserving structures of tissues and biomarkers. MuST offers a versatile toolkit for the intricate analysis of complex biological systems.

cs.CE↗

arXiv2021

Video Captioning in Compressed Video

Existing approaches in video captioning concentrate on exploring global frame features in the uncompressed videos, while the free of charge and critical saliency information already encoded in the compressed videos is generally neglected. We propose a video captioning method which operates directly on the stored compressed videos. To learn a discriminative visual representation for video captioning, we design a residuals-assisted encoder (RAE), which spots regions of interest in I-frames under the assistance of the residuals frames. First, we obtain the spatial attention weights by extracting features of residuals as the saliency value of each location in I-frame and design a spatial attention module to refine the attention weights. We further propose a temporal gate module to determine how much the attended features contribute to the caption generation, which enables the model to resist the disturbance of some noisy signals in the compressed videos. Finally, Long Short-Term Memory is utilized to decode the visual representations into descriptions. We evaluate our method on two benchmark datasets and demonstrate the effectiveness of our approach.

cs.CV↗