10 papers · ranked by Valyu relevance
Maddalena M Bolognesi, Lorenzo Dall’Olio, Giulio Eugenio Mandelli, Luisa Lorenzi + 7 more
Lymph nodes (LN) are key secondary lymphoid organs (SLO) for a coordinated immune response. They have been extensively characterized by numerous investigative techniques chiefly as single cell suspensions because they are composed of vagile yet crowded hematolymphoid elements, unfriendly to spatial tissue…
Guangyu Wang, Limei Zhang, Lishan Qiao
Brain functional network (BFN) analysis has become a popular technique for identifying neurological/mental diseases. Due to the fact that BFN is a graph, graph convolutional network (GCN) can be naturally used in the classification of BFN. Different from traditional methods that directly use the adjacency matrices of…
Siyu Tao, Yang Yang, Xin Liu, Yimiao Feng + 1 more
Predicting biomedical interactions is crucial for understanding various biological processes and drug discovery. Graph neural networks (GNNs) are promising in identifying novel interactions when extensive labeled data are available. However, labeling biomedical interactions is often time-consuming and labor-intensive…
Lida Kanari, Stanislav Schmidt, Francesco Casalegno, Emilie Delattre + 7 more
The shape of neuronal morphologies plays a critical role in determining their dynamical properties and the functionality of the brain. With an abundance of neuronal morphology reconstructions, a robust definition of cell types is important to understand their role in brain functionality. However, an objective…
Paul Chon, William B. Andreopoulos
Protein language models (PLMs) are shown to be powerful predictors of protein structure and function but their internal mechanisms remain poorly understood. Recent mechanistic interpretability methods have decomposed PLM representations into interpretable features, but they have not combined methods on a single…
Renming Liu, Matthew Hirn, Arjun Krishnan
Accurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased random walks. However, while many networks, including functional gene interaction…
Shawn Zheng Kai Tan, Huseyin Kir, Brian Aevermann, Tom Gillespie + 12 more
Large scale single cell omics profiling is revolutionising our understanding of cell types, especially in complex organs like the brain. This presents both an opportunity and a challenge for cell ontologies. Annotation of cell types in single cell ‘omics data typically uses unstructured free text, making comparison and…
Paul Kruse, Caroline Ring
This paper presents the treecompareR package for R, which provides tools for reproducible visualizations of data through the use of taxonomies. The package builds on developments from ggplot2 and ggtree to provide visualizations tailored for use with taxonomic classification data. Additionally, it provides tools that…
Diane Duroux, Kristel Van Steen
Many problems in life sciences can be brought back to a comparison of graphs. Even though a multitude of such techniques exist, often, these assume prior knowledge about the partitioning or the number of clusters and fail to provide statistical significance of observed between-network heterogeneity. Addressing these…
Jianmei Zhong, Junyao Yang, Yinghui Song, Zhihua Zhang + 8 more
In this study, we have devised a computational framework SuperFeat that allows for the training of a machine learning model and evaluate the canonical cellular states/features in pathological tissues that underlie the progression of disease. This framework also enables the identification of potential drugs that target…