Biological Random Walks: integrating heterogeneous data in disease gene prioritization

Disease gene prioritization using network topological analysis from a sequence based human functional linkage network

Sequencing large number of candidate disease genes which cause diseases in order to identify the relationship between them is an expensive and time-consuming task. To handle these challenges, different computational approaches have been developed. Based on the observation that genes associated with similar diseases have a higher likelihood of interaction, a large class of these approaches relay on analyzing the topological properties of biological networks. However, the incomplete and noisy nature of biological networks is known as an important challenge in these approaches. In this paper, we …

基于量子游走的疾病基因优先级排序

Disease Gene Prioritization With Quantum Walks

本研究提出一种基于连续时间量子游走的疾病基因优先级排序算法，通过PPI网络邻接矩阵构建，支持种子节点自环编码，在多数据集和网络上性能优于传统扩散核等方法，并经冠状动脉疾病富集分析验证有效。

图神经网络解释揭示了生物网络中与疾病相关的枢纽的拓扑特征

Graph neural network explanations reveal a topological signature of disease-associated hubs in biological networks

本文研究了图神经网络在生物网络中识别疾病相关结构的方法，发现不同解释方法在稀疏单节点驱动和分布式路径信号中有不同的表现，并提出了一种结合壳层枢纽评分和解释器共识排名的框架，提升了对癌症基因的优先级排序和生物学相关分子的恢复能力。

VTCdb: A transcriptomics & co-expression database for the crop species Vitis vinifera (grapevine)

Gene co-expression databases in model plants such as Arabidopsis and rice have been extensively developed and utilised for predicting gene function and identifying functional modules for underlying biological processes. However, such tools are less available for non-model plants such as grapevine. We have constructed a gene co-expression database, VTCdb (http://vtcdb.adelaide.edu.au/Home.aspx) that offers an online platform for transcriptional regulatory inference in the cultivated grapevine. Using a condition-independent approach, the grapevine co-expression network was constructed using 352 …

Module networks revisited: computational assessment and prioritization of model predictions

The solution of high-dimensional inference and prediction problems in computational biology is almost always a compromise between mathematical theory and practical constraints such as limited computational resources. As time progresses, computational power increases but well-established inference methods often remain locked in their initial suboptimal solution. We revisit the approach of Segal et al. (2003) to infer regulatory modules and their condition-specific regulators from gene expression data. In contrast to their direct optimization-based solution we use a more representative centroid-…

网络社区检测与新颖性评分揭示类风湿关节炎中未被充分探索的枢纽基因

Network Community Detection and Novelty Scoring Reveal Underexplored Hub Genes in Rheumatoid Arthritis

该研究构建RA滑膜组织加权基因共表达网络，结合Louvain/Leiden社区检测、枢纽排序与GWAS及文献新颖性评分，识别出5个未被充分探索的免疫相关枢纽基因。