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Arena3D 3.0: a web server for exploring, analyzing, annotating and sharing multilayered networks in 3D

Arena3D 3.0 is a web server for exploring, analyzing, and sharing multilayered biological networks in 3D with various layouts and visualization tools.

Micro-fragmented adipose tissue reprogrammes the systemic molecular landscape of inflammatory knee osteoarthritis

This study uses AI to analyze blood molecular changes after MFAT or HA treatment in knee OA, showing MFAT alters systemic pathways more than HA.

iTissue

The paper introduces iTissue, a method integrating spatial and single-cell transcriptomics with molecular networks to identify metastasis-related genes in ccRCC.

Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data

INMTD integrates omics and 3D imaging data to produce unconfounded individual subgroups with biologically meaningful embeddings.

MONFIT: Multi-omics factorization-based integration of time-series data sheds light on Parkinson's disease

MONFIT integrates time-series single-cell RNA-sequencing with bulk proteomics and metabolomics to identify PD-associated genes and pathways.

Multi-omics integration of scRNA-seq time series data predicts new intervention points for Parkinson's disease

The paper integrates scRNA-seq time series data with molecular networks to identify PD-related genes and potential drug targets.

The axes of biology: a novel axes-based network embedding paradigm to decipher the functional mechanisms of the cell.

This paper introduces an axes-based network embedding method to uncover functional mechanisms of cells by analyzing the structure of embedding spaces rather than gene clusters.

Graphlet-based hyperbolic embeddings capture evolutionary dynamics in genetic networks

The paper introduces GraCoal embedding to improve functional organization analysis of scale-free biological networks by incorporating graphlet patterns, outperforming traditional Spring embedding methods.

Graphlet Laplacians: graphlet-based neighbourhoods highlight topology-function and topology-disease relationships

The paper introduces Graphlet Laplacians to capture topology-function and topology-disease relationships in biological networks by generalizing Laplacian matrices to consider graphlet-based neighbourhoods.